feat: add vLLM as alternative inference backend (#1281)

Adds vLLM (high-throughput OpenAI-compatible inference server) as a
selectable backend alongside the existing Ollama and vllm-mlx backends.
vLLM's continuous batching gives 3-10x throughput for agentic workloads.

Changes:
- config.py: add `vllm` to timmy_model_backend Literal; add vllm_url /
  vllm_model settings (VLLM_URL / VLLM_MODEL env vars)
- cascade.py: add vllm provider type with _check_provider_available
  (hits /health) and _call_vllm (OpenAI-compatible completions)
- providers.yaml: add disabled-by-default vllm-local provider (priority 3,
  port 8001); bump OpenAI/Anthropic backup priorities to 4/5
- health.py: add _check_vllm/_check_vllm_sync with 30-second TTL cache;
  /health and /health/sovereignty reflect vLLM status when it is the
  active backend
- docker-compose.yml: add vllm service behind 'vllm' profile (GPU
  passthrough commented-out template included); add vllm-cache volume
- CLAUDE.md: add vLLM row to Service Fallback Matrix
- tests: 26 new unit tests covering availability checks, _call_vllm,
  providers.yaml validation, config options, and health helpers

Graceful fallback: if vLLM is unavailable the cascade router automatically
falls back to Ollama. The app never crashes.

Fixes #1281

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

AGENTS.md

@@ -34,6 +34,44 @@ Read [`CLAUDE.md`](CLAUDE.md) for architecture patterns and conventions.
 
 ---
 
+## One-Agent-Per-Issue Convention
+
+**An issue must only be worked by one agent at a time.** Duplicate branches from
+multiple agents on the same issue cause merge conflicts, redundant code, and wasted compute.
+
+### Labels
+
+When an agent picks up an issue, add the corresponding label:
+
+| Label | Meaning |
+|-------|---------|
+| `assigned-claude` | Claude is actively working this issue |
+| `assigned-gemini` | Gemini is actively working this issue |
+| `assigned-kimi` | Kimi is actively working this issue |
+| `assigned-manus` | Manus is actively working this issue |
+
+### Rules
+
+1. **Before starting an issue**, check that none of the `assigned-*` labels are present.
+   If one is, skip the issue — another agent owns it.
+2. **When you start**, add the label matching your agent (e.g. `assigned-claude`).
+3. **When your PR is merged or closed**, remove the label (or it auto-clears when
+   the branch is deleted — see Auto-Delete below).
+4. **Never assign the same issue to two agents simultaneously.**
+
+### Auto-Delete Merged Branches
+
+`default_delete_branch_after_merge` is **enabled** on this repo. Branches are
+automatically deleted after a PR merges — no manual cleanup needed and no stale
+`claude/*`, `gemini/*`, or `kimi/*` branches accumulate.
+
+If you discover stale merged branches, they can be pruned with:
+```bash
+git fetch --prune
+```
+
+---
+
 ## Merge Policy (PR-Only)
 
 **Gitea branch protection is active on `main`.** This is not a suggestion.
@@ -131,6 +169,28 @@ self-testing, reflection — use every tool he has.
 
 ## Agent Roster
 
+### Gitea Permissions
+
+All agents that push branches and create PRs require **write** permission on the
+repository. Set via the Gitea admin API or UI under Repository → Settings → Collaborators.
+
+| Agent user | Required permission | Gitea login |
+|------------|--------------------|----|
+| kimi       | write              | `kimi` |
+| claude     | write              | `claude` |
+| gemini     | write              | `gemini` |
+| antigravity | write             | `antigravity` |
+| hermes     | write              | `hermes` |
+| manus      | write              | `manus` |
+
+To grant write access (requires Gitea admin or repo admin token):
+```bash
+curl -s -X PUT "http://143.198.27.163:3000/api/v1/repos/rockachopa/Timmy-time-dashboard/collaborators/<username>" \
+  -H "Authorization: token <admin-token>" \
+  -H "Content-Type: application/json" \
+  -d '{"permission": "write"}'
+```
+
 ### Build Tier
 
 **Local (Ollama)** — Primary workhorse. Free. Unrestricted.

CLAUDE.md

@@ -150,6 +150,7 @@ async def transcribe_audio(audio: bytes) -> str:
 | Service | When Unavailable | Fallback Behavior |
 |---------|------------------|-------------------|
 | Ollama | No local LLM | Claude backend (if ANTHROPIC_API_KEY set) |
+| vLLM | Server not running | Ollama backend (cascade router fallback) |
 | Redis | Cache/storage down | In-memory dict (ephemeral) |
 | AirLLM | Import error or no Apple Silicon | Ollama backend |
 | Voice (Piper) | Service down | Browser Web Speech API |

Modelfile.qwen3-14b

@@ -0,0 +1,51 @@
+# Modelfile.qwen3-14b
+#
+# Qwen3-14B Q5_K_M — Primary local agent model (Issue #1063)
+#
+# Tool calling F1: 0.971 — GPT-4-class structured output reliability.
+# Hybrid thinking/non-thinking mode: toggle per-request via /think or /no_think
+# in the prompt for planning vs rapid execution.
+#
+# Build:
+#   ollama pull qwen3:14b            # downloads Q4_K_M (~8.2 GB) by default
+#   # For Q5_K_M (~10.5 GB, recommended):
+#   # ollama pull bartowski/Qwen3-14B-GGUF:Q5_K_M
+#   ollama create qwen3-14b -f Modelfile.qwen3-14b
+#
+# Memory budget: ~10.5 GB weights + ~7 GB KV cache = ~17.5 GB total at 32K ctx
+# Headroom on M3 Max 36 GB: ~10.5 GB free (enough to run qwen3:8b simultaneously)
+# Generation: ~20-28 tok/s (Ollama) / ~28-38 tok/s (MLX)
+# Context: 32K native, extensible to 131K with YaRN
+#
+# Two-model strategy: set OLLAMA_MAX_LOADED_MODELS=2 so qwen3:8b stays
+# hot for fast routing while qwen3:14b handles complex tasks.
+
+FROM qwen3:14b
+
+# 32K context — optimal balance of quality and memory on M3 Max 36 GB.
+# At 32K, total memory (weights + KV cache) is ~17.5 GB — well within budget.
+# Extend to 131K with YaRN if needed: PARAMETER rope_scaling_type yarn
+PARAMETER num_ctx 32768
+
+# Tool-calling temperature — lower = more reliable structured JSON output.
+# Raise to 0.7+ for creative/narrative tasks.
+PARAMETER temperature 0.3
+
+# Nucleus sampling
+PARAMETER top_p 0.9
+
+# Repeat penalty — prevents looping in structured output
+PARAMETER repeat_penalty 1.05
+
+SYSTEM """You are Timmy, Alexander's personal sovereign AI agent.
+
+You are concise, direct, and helpful. You complete tasks efficiently and report results clearly. You do not add unnecessary caveats or disclaimers.
+
+You have access to tool calling. When you need to use a tool, output a valid JSON function call:
+<tool_call>
+{"name": "function_name", "arguments": {"param": "value"}}
+</tool_call>
+
+You support hybrid reasoning. For complex planning, include <think>...</think> before your answer. For rapid execution (simple tool calls, status checks), skip the think block.
+
+You always start your responses with "Timmy here:" when acting as an agent."""

Modelfile.qwen3-8b

@@ -0,0 +1,43 @@
+# Modelfile.qwen3-8b
+#
+# Qwen3-8B Q6_K — Fast routing model for routine agent tasks (Issue #1063)
+#
+# Tool calling F1: 0.933 at ~45-55 tok/s — 2x speed of Qwen3-14B.
+# Use for: simple tool calls, shell commands, file reads, status checks, JSON ops.
+# Route complex tasks (issue triage, multi-step planning, code review) to qwen3:14b.
+#
+# Build:
+#   ollama pull qwen3:8b
+#   ollama create qwen3-8b -f Modelfile.qwen3-8b
+#
+# Memory budget: ~6.6 GB weights + ~5 GB KV cache = ~11.6 GB at 32K ctx
+# Two-model strategy: ~17 GB combined (both hot) — fits on M3 Max 36 GB.
+# Set OLLAMA_MAX_LOADED_MODELS=2 in the Ollama environment.
+#
+# Generation: ~35-45 tok/s (Ollama) / ~45-60 tok/s (MLX)
+
+FROM qwen3:8b
+
+# 32K context
+PARAMETER num_ctx 32768
+
+# Lower temperature for fast, deterministic tool execution
+PARAMETER temperature 0.2
+
+# Nucleus sampling
+PARAMETER top_p 0.9
+
+# Repeat penalty
+PARAMETER repeat_penalty 1.05
+
+SYSTEM """You are Timmy's fast-routing agent. You handle routine tasks quickly and precisely.
+
+For simple tasks (tool calls, shell commands, file reads, status checks, JSON ops): respond immediately without a think block.
+For anything requiring multi-step planning: defer to the primary agent.
+
+Tool call format:
+<tool_call>
+{"name": "function_name", "arguments": {"param": "value"}}
+</tool_call>
+
+Be brief. Be accurate. Execute."""

SOVEREIGNTY.md

@@ -0,0 +1,122 @@
+# SOVEREIGNTY.md — Research Sovereignty Manifest
+
+> "If this spec is implemented correctly, it is the last research document
+> Alexander should need to request from a corporate AI."
+> — Issue #972, March 22 2026
+
+---
+
+## What This Is
+
+A machine-readable declaration of Timmy's research independence:
+where we are, where we're going, and how to measure progress.
+
+---
+
+## The Problem We're Solving
+
+On March 22, 2026, a single Claude session produced six deep research reports.
+It consumed ~3 hours of human time and substantial corporate AI inference.
+Every report was valuable — but the workflow was **linear**.
+It would cost exactly the same to reproduce tomorrow.
+
+This file tracks the pipeline that crystallizes that workflow into something
+Timmy can run autonomously.
+
+---
+
+## The Six-Step Pipeline
+
+| Step | What Happens | Status |
+|------|-------------|--------|
+| 1. Scope | Human describes knowledge gap → Gitea issue with template | ✅ Done (`skills/research/`) |
+| 2. Query | LLM slot-fills template → 5–15 targeted queries | ✅ Done (`research.py`) |
+| 3. Search | Execute queries → top result URLs | ✅ Done (`research_tools.py`) |
+| 4. Fetch | Download + extract full pages (trafilatura) | ✅ Done (`tools/system_tools.py`) |
+| 5. Synthesize | Compress findings → structured report | ✅ Done (`research.py` cascade) |
+| 6. Deliver | Store to semantic memory + optional disk persist | ✅ Done (`research.py`) |
+
+---
+
+## Cascade Tiers (Synthesis Quality vs. Cost)
+
+| Tier | Model | Cost | Quality | Status |
+|------|-------|------|---------|--------|
+| **4** | SQLite semantic cache | $0.00 / instant | reuses prior | ✅ Active |
+| **3** | Ollama `qwen3:14b` | $0.00 / local | ★★★ | ✅ Active |
+| **2** | Claude API (haiku) | ~$0.01/report | ★★★★ | ✅ Active (opt-in) |
+| **1** | Groq `llama-3.3-70b` | $0.00 / rate-limited | ★★★★ | 🔲 Planned (#980) |
+
+Set `ANTHROPIC_API_KEY` to enable Tier 2 fallback.
+
+---
+
+## Research Templates
+
+Six prompt templates live in `skills/research/`:
+
+| Template | Use Case |
+|----------|----------|
+| `tool_evaluation.md` | Find all shipping tools for `{domain}` |
+| `architecture_spike.md` | How to connect `{system_a}` to `{system_b}` |
+| `game_analysis.md` | Evaluate `{game}` for AI agent play |
+| `integration_guide.md` | Wire `{tool}` into `{stack}` with code |
+| `state_of_art.md` | What exists in `{field}` as of `{date}` |
+| `competitive_scan.md` | How does `{project}` compare to `{alternatives}` |
+
+---
+
+## Sovereignty Metrics
+
+| Metric | Target (Week 1) | Target (Month 1) | Target (Month 3) | Graduation |
+|--------|-----------------|------------------|------------------|------------|
+| Queries answered locally | 10% | 40% | 80% | >90% |
+| API cost per report | <$1.50 | <$0.50 | <$0.10 | <$0.01 |
+| Time from question to report | <3 hours | <30 min | <5 min | <1 min |
+| Human involvement | 100% (review) | Review only | Approve only | None |
+
+---
+
+## How to Use the Pipeline
+
+```python
+from timmy.research import run_research
+
+# Quick research (no template)
+result = await run_research("best local embedding models for 36GB RAM")
+
+# With a template and slot values
+result = await run_research(
+    topic="PDF text extraction libraries for Python",
+    template="tool_evaluation",
+    slots={"domain": "PDF parsing", "use_case": "RAG pipeline", "focus_criteria": "accuracy"},
+    save_to_disk=True,
+)
+
+print(result.report)
+print(f"Backend: {result.synthesis_backend}, Cached: {result.cached}")
+```
+
+---
+
+## Implementation Status
+
+| Component | Issue | Status |
+|-----------|-------|--------|
+| `web_fetch` tool (trafilatura) | #973 | ✅ Done |
+| Research template library (6 templates) | #974 | ✅ Done |
+| `ResearchOrchestrator` (`research.py`) | #975 | ✅ Done |
+| Semantic index for outputs | #976 | 🔲 Planned |
+| Auto-create Gitea issues from findings | #977 | 🔲 Planned |
+| Paperclip task runner integration | #978 | 🔲 Planned |
+| Kimi delegation via labels | #979 | 🔲 Planned |
+| Groq free-tier cascade tier | #980 | 🔲 Planned |
+| Sovereignty metrics dashboard | #981 | 🔲 Planned |
+
+---
+
+## Governing Spec
+
+See [issue #972](http://143.198.27.163:3000/Rockachopa/Timmy-time-dashboard/issues/972) for the full spec and rationale.
+
+Research artifacts committed to `docs/research/`.

config/agents.yaml

@@ -16,6 +16,8 @@
 #   prompt_tier   "full" (tool-capable models) or "lite" (small models)
 #   max_history   Number of conversation turns to keep in context
 #   context_window  Max context length (null = model default)
+#   initial_emotion Starting emotional state (calm, cautious, adventurous,
+#                   analytical, frustrated, confident, curious)
 #
 # ── Defaults ────────────────────────────────────────────────────────────────
 
@@ -103,6 +105,7 @@ agents:
     model: qwen3:30b
     prompt_tier: full
     max_history: 20
+    initial_emotion: calm
     tools:
       - web_search
       - read_file
@@ -136,6 +139,7 @@ agents:
     model: qwen3:30b
     prompt_tier: full
     max_history: 10
+    initial_emotion: curious
     tools:
       - web_search
       - read_file
@@ -151,6 +155,7 @@ agents:
     model: qwen3:30b
     prompt_tier: full
     max_history: 15
+    initial_emotion: analytical
     tools:
       - python
       - write_file
@@ -196,6 +201,7 @@ agents:
     model: qwen3:30b
     prompt_tier: full
     max_history: 10
+    initial_emotion: adventurous
     tools:
       - run_experiment
       - prepare_experiment

config/providers.yaml

@@ -25,6 +25,19 @@ providers:
     tier: local
     url: "http://localhost:11434"
     models:
+      # ── Dual-model routing: Qwen3-8B (fast) + Qwen3-14B (quality) ──────────
+      # Both models fit simultaneously: ~6.6 GB + ~10.5 GB = ~17 GB combined.
+      # Requires OLLAMA_MAX_LOADED_MODELS=2 (set in .env) to stay hot.
+      # Ref: issue #1065 — Qwen3-8B/14B dual-model routing strategy
+      - name: qwen3:8b
+        context_window: 32768
+        capabilities: [text, tools, json, streaming, routine]
+        description: "Qwen3-8B Q6_K — fast router for routine tasks (~6.6 GB, 45-55 tok/s)"
+      - name: qwen3:14b
+        context_window: 40960
+        capabilities: [text, tools, json, streaming, complex, reasoning]
+        description: "Qwen3-14B Q5_K_M — complex reasoning and planning (~10.5 GB, 20-28 tok/s)"
+
       # Text + Tools models
       - name: qwen3:30b
         default: true
@@ -118,11 +131,34 @@ providers:
         context_window: 32000
         capabilities: [text, tools, json, streaming]
 
-  # Tertiary: OpenAI (if API key available)
+  # Tertiary: vLLM (OpenAI-compatible, continuous batching, 3-10x agentic throughput)
+  # Runs on CUDA GPU or CPU.  On Apple Silicon, prefer vllm-mlx-local (above).
+  # To enable: start vLLM server:
+  #   python -m vllm.entrypoints.openai.api_server \
+  #     --model Qwen/Qwen2.5-14B-Instruct --port 8001
+  # Then set enabled: true (or TIMMY_LLM_BACKEND=vllm + VLLM_URL=http://localhost:8001)
+  - name: vllm-local
+    type: vllm
+    enabled: false  # Enable when vLLM server is running
+    priority: 3
+    tier: local
+    base_url: "http://localhost:8001/v1"
+    models:
+      - name: Qwen/Qwen2.5-14B-Instruct
+        default: true
+        context_window: 32000
+        capabilities: [text, tools, json, streaming, complex]
+        description: "Qwen2.5-14B on vLLM — continuous batching for agentic workloads"
+      - name: Qwen/Qwen2.5-7B-Instruct
+        context_window: 32000
+        capabilities: [text, tools, json, streaming, routine]
+        description: "Qwen2.5-7B on vLLM — fast model for routine tasks"
+
+  # Quinary: OpenAI (if API key available)
   - name: openai-backup
     type: openai
     enabled: false  # Enable by setting OPENAI_API_KEY
-    priority: 3
+    priority: 4
     tier: standard_cloud
     api_key: "${OPENAI_API_KEY}"  # Loaded from environment
     base_url: null  # Use default OpenAI endpoint
@@ -134,12 +170,12 @@ providers:
       - name: gpt-4o
         context_window: 128000
         capabilities: [text, vision, tools, json, streaming]
-    
-  # Quaternary: Anthropic (if API key available)
+
+  # Senary: Anthropic (if API key available)
   - name: anthropic-backup
     type: anthropic
     enabled: false  # Enable by setting ANTHROPIC_API_KEY
-    priority: 4
+    priority: 5
     tier: frontier
     api_key: "${ANTHROPIC_API_KEY}"
     models:
@@ -187,6 +223,20 @@ fallback_chains:
     - dolphin3          # base Dolphin 3.0 8B (uncensored, no custom system prompt)
     - qwen3:30b         # primary fallback — usually sufficient with a good system prompt
 
+  # ── Complexity-based routing chains (issue #1065) ───────────────────────
+  # Routine tasks: prefer Qwen3-8B for low latency (~45-55 tok/s)
+  routine:
+    - qwen3:8b              # Primary fast model
+    - llama3.1:8b-instruct  # Fallback fast model
+    - llama3.2:3b           # Smallest available
+
+  # Complex tasks: prefer Qwen3-14B for quality (~20-28 tok/s)
+  complex:
+    - qwen3:14b             # Primary quality model
+    - hermes4-14b           # Native tool calling, hybrid reasoning
+    - qwen3:30b             # Highest local quality
+    - qwen2.5:14b           # Additional fallback
+
 # ── Custom Models ───────────────────────────────────────────────────────────
 # Register custom model weights for per-agent assignment.
 # Supports GGUF (Ollama), safetensors, and HuggingFace checkpoint dirs.

docker-compose.yml

@@ -42,6 +42,10 @@ services:
       GROK_ENABLED: "${GROK_ENABLED:-false}"
       XAI_API_KEY: "${XAI_API_KEY:-}"
       GROK_DEFAULT_MODEL: "${GROK_DEFAULT_MODEL:-grok-3-fast}"
+      # vLLM backend — set TIMMY_LLM_BACKEND=vllm to activate
+      TIMMY_LLM_BACKEND: "${TIMMY_LLM_BACKEND:-ollama}"
+      VLLM_URL: "${VLLM_URL:-http://localhost:8001}"
+      VLLM_MODEL: "${VLLM_MODEL:-Qwen/Qwen2.5-14B-Instruct}"
     extra_hosts:
       - "host.docker.internal:host-gateway"  # Linux: maps to host IP
     networks:
@@ -74,6 +78,49 @@ services:
     profiles:
       - celery
 
+  # ── vLLM — high-throughput inference server (GPU optional) ──────────────
+  # Requires the 'vllm' profile: docker compose --profile vllm up
+  #
+  # GPU (NVIDIA):  set VLLM_MODEL and ensure nvidia-container-toolkit is installed.
+  # CPU-only:      add --device cpu to VLLM_EXTRA_ARGS (slower, but works anywhere).
+  #
+  # The dashboard reaches vLLM at http://vllm:8001 (inside timmy-net).
+  # Set VLLM_URL=http://vllm:8001 in the dashboard environment when using this service.
+  vllm:
+    image: vllm/vllm-openai:latest
+    container_name: timmy-vllm
+    profiles:
+      - vllm
+    ports:
+      - "8001:8001"
+    environment:
+      # Model to load — override with VLLM_MODEL env var
+      VLLM_MODEL: "${VLLM_MODEL:-Qwen/Qwen2.5-7B-Instruct}"
+    command: >
+      --model ${VLLM_MODEL:-Qwen/Qwen2.5-7B-Instruct}
+      --port 8001
+      --host 0.0.0.0
+      ${VLLM_EXTRA_ARGS:-}
+    volumes:
+      - vllm-cache:/root/.cache/huggingface
+    networks:
+      - timmy-net
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8001/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 120s
+    # GPU support — uncomment to enable NVIDIA GPU passthrough
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: all
+    #           capabilities: [gpu]
+
   # ── OpenFang — vendored agent runtime sidecar ────────────────────────────
   openfang:
     build:
@@ -110,6 +157,8 @@ volumes:
       device: "${PWD}/data"
   openfang-data:
     driver: local
+  vllm-cache:
+    driver: local
 
 # ── Internal network ────────────────────────────────────────────────────────
 networks:

docs/GITEA_AUDIT_2026-03-23.md

@@ -0,0 +1,244 @@
+# Gitea Activity & Branch Audit — 2026-03-23
+
+**Requested by:** Issue #1210
+**Audited by:** Claude (Sonnet 4.6)
+**Date:** 2026-03-23
+**Scope:** All repos under the sovereign AI stack
+
+---
+
+## Executive Summary
+
+- **18 repos audited** across 9 Gitea organizations/users
+- **~65–70 branches identified** as safe to delete (merged or abandoned)
+- **4 open PRs** are bottlenecks awaiting review
+- **3+ instances of duplicate work** across repos and agents
+- **5+ branches** contain valuable unmerged code with no open PR
+- **5 PRs closed without merge** on active p0-critical issues in Timmy-time-dashboard
+
+Improvement tickets have been filed on each affected repo following this report.
+
+---
+
+## Repo-by-Repo Findings
+
+---
+
+### 1. rockachopa/Timmy-time-dashboard
+
+**Status:** Most active repo. 1,200+ PRs, 50+ branches.
+
+#### Dead/Abandoned Branches
+| Branch | Last Commit | Status |
+|--------|-------------|--------|
+| `feature/voice-customization` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/enhanced-memory-ui` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/soul-customization` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/dreaming-mode` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/memory-visualization` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/voice-customization-ui` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/issue-1015` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/issue-1016` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/issue-1017` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/issue-1018` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/issue-1019` | 2026-03-22 | Gemini-created, no PR, abandoned |
+| `feature/self-reflection` | 2026-03-22 | Only merge-from-main commits, no unique work |
+| `feature/memory-search-ui` | 2026-03-22 | Only merge-from-main commits, no unique work |
+| `claude/issue-962` | 2026-03-22 | Automated salvage commit only |
+| `claude/issue-972` | 2026-03-22 | Automated salvage commit only |
+| `gemini/issue-1006` | 2026-03-22 | Incomplete agent session |
+| `gemini/issue-1008` | 2026-03-22 | Incomplete agent session |
+| `gemini/issue-1010` | 2026-03-22 | Incomplete agent session |
+| `gemini/issue-1134` | 2026-03-22 | Incomplete agent session |
+| `gemini/issue-1139` | 2026-03-22 | Incomplete agent session |
+
+#### Duplicate Branches (Identical SHA)
+| Branch A | Branch B | Action |
+|----------|----------|--------|
+| `feature/internal-monologue` | `feature/issue-1005` | Exact duplicate — delete one |
+| `claude/issue-1005` | (above) | Merge-from-main only — delete |
+
+#### Unmerged Work With No Open PR (HIGH PRIORITY)
+| Branch | Content | Issues |
+|--------|---------|--------|
+| `claude/issue-987` | Content moderation pipeline, Llama Guard integration | No open PR — potentially lost |
+| `claude/issue-1011` | Automated skill discovery system | No open PR — potentially lost |
+| `gemini/issue-976` | Semantic index for research outputs | No open PR — potentially lost |
+
+#### PRs Closed Without Merge (Issues Still Open)
+| PR | Title | Issue Status |
+|----|-------|-------------|
+| PR#1163 | Three-Strike Detector (#962) | p0-critical, still open |
+| PR#1162 | Session Sovereignty Report Generator (#957) | p0-critical, still open |
+| PR#1157 | Qwen3 routing | open |
+| PR#1156 | Agent Dreaming Mode | open |
+| PR#1145 | Qwen3-14B config | open |
+
+#### Workflow Observations
+- `loop-cycle` bot auto-creates micro-fix PRs at high frequency (PR numbers climbing past 1209 rapidly)
+- Many `gemini/*` branches represent incomplete agent sessions, not full feature work
+- Issues get reassigned across agents causing duplicate branch proliferation
+
+---
+
+### 2. rockachopa/hermes-agent
+
+**Status:** Active — AutoLoRA training pipeline in progress.
+
+#### Open PRs Awaiting Review
+| PR | Title | Age |
+|----|-------|-----|
+| PR#33 | AutoLoRA v1 MLX QLoRA training pipeline | ~1 week |
+
+#### Valuable Unmerged Branches (No PR)
+| Branch | Content | Age |
+|--------|---------|-----|
+| `sovereign` | Full fallback chain: Groq/Kimi/Ollama cascade recovery | 9 days |
+| `fix/vision-api-key-fallback` | Vision API key fallback fix | 9 days |
+
+#### Stale Merged Branches (~12)
+12 merged `claude/*` and `gemini/*` branches are safe to delete.
+
+---
+
+### 3. rockachopa/the-matrix
+
+**Status:** 8 open PRs from `claude/the-matrix` fork all awaiting review, all batch-created on 2026-03-23.
+
+#### Open PRs (ALL Awaiting Review)
+| PR | Feature |
+|----|---------|
+| PR#9–16 | Touch controls, agent feed, particles, audio, day/night cycle, metrics panel, ASCII logo, click-to-view-PR |
+
+These were created in a single agent session within 5 minutes — needs human review before merge.
+
+---
+
+### 4. replit/timmy-tower
+
+**Status:** Very active — 100+ PRs, complex feature roadmap.
+
+#### Open PRs Awaiting Review
+| PR | Title | Age |
+|----|-------|-----|
+| PR#93 | Task decomposition view | Recent |
+| PR#80 | `session_messages` table | 22 hours |
+
+#### Unmerged Work With No Open PR
+| Branch | Content |
+|--------|---------|
+| `gemini/issue-14` | NIP-07 Nostr identity |
+| `gemini/issue-42` | Timmy animated eyes |
+| `claude/issue-11` | Kimi + Perplexity agent integrations |
+| `claude/issue-13` | Nostr event publishing |
+| `claude/issue-29` | Mobile Nostr identity |
+| `claude/issue-45` | Test kit |
+| `claude/issue-47` | SQL migration helpers |
+| `claude/issue-67` | Session Mode UI |
+
+#### Cleanup
+~30 merged `claude/*` and `gemini/*` branches are safe to delete.
+
+---
+
+### 5. replit/token-gated-economy
+
+**Status:** Active roadmap, no current open PRs.
+
+#### Stale Branches (~23)
+- 8 Replit Agent branches from 2026-03-19 (PRs closed/merged)
+- 15 merged `claude/issue-*` branches
+
+All are safe to delete.
+
+---
+
+### 6. hermes/timmy-time-app
+
+**Status:** 2-commit repo, created 2026-03-14, no activity since. **Candidate for archival.**
+
+Functionality appears to be superseded by other repos in the stack. Recommend archiving or deleting if not planned for future development.
+
+---
+
+### 7. google/maintenance-tasks & google/wizard-council-automation
+
+**Status:** Single-commit repos from 2026-03-19 created by "Google AI Studio". No follow-up activity.
+
+Unclear ownership and purpose. Recommend clarifying with rockachopa whether these are active or can be archived.
+
+---
+
+### 8. hermes/hermes-config
+
+**Status:** Single branch, updated 2026-03-23 (today). Active — contains Timmy orchestrator config.
+
+No action needed.
+
+---
+
+### 9. Timmy_Foundation/the-nexus
+
+**Status:** Greenfield — created 2026-03-23. 19 issues filed as roadmap. PR#2 (contributor audit) open.
+
+No cleanup needed yet. PR#2 needs review.
+
+---
+
+### 10. rockachopa/alexanderwhitestone.com
+
+**Status:** All recent `claude/*` PRs merged. 7 non-main branches are post-merge and safe to delete.
+
+---
+
+### 11. hermes/hermes-config, rockachopa/hermes-config, Timmy_Foundation/.profile
+
+**Status:** Dormant config repos. No action needed.
+
+---
+
+## Cross-Repo Patterns & Inefficiencies
+
+### Duplicate Work
+1. **Timmy spring/wobble physics** built independently in both `replit/timmy-tower` and `replit/token-gated-economy`
+2. **Nostr identity logic** fragmented across 3 repos with no shared library
+3. **`feature/internal-monologue` = `feature/issue-1005`** in Timmy-time-dashboard — identical SHA, exact duplicate
+
+### Agent Workflow Issues
+- Same issue assigned to both `gemini/*` and `claude/*` agents creates duplicate branches
+- Agent salvage commits are checkpoint-only — not complete work, but clutter the branch list
+- Gemini `feature/*` branches created on 2026-03-22 with no PRs filed — likely a failed agent session that created branches but didn't complete the loop
+
+### Review Bottlenecks
+| Repo | Waiting PRs | Notes |
+|------|-------------|-------|
+| rockachopa/the-matrix | 8 | Batch-created, need human review |
+| replit/timmy-tower | 2 | Database schema and UI work |
+| rockachopa/hermes-agent | 1 | AutoLoRA v1 — high value |
+| Timmy_Foundation/the-nexus | 1 | Contributor audit |
+
+---
+
+## Recommended Actions
+
+### Immediate (This Sprint)
+1. **Review & merge** PR#33 in `hermes-agent` (AutoLoRA v1)
+2. **Review** 8 open PRs in `the-matrix` before merging as a batch
+3. **Rescue** unmerged work in `claude/issue-987`, `claude/issue-1011`, `gemini/issue-976` — file new PRs or close branches
+4. **Delete duplicate** `feature/internal-monologue` / `feature/issue-1005` branches
+
+### Cleanup Sprint
+5. **Delete ~65 stale branches** across all repos (itemized above)
+6. **Investigate** the 5 closed-without-merge PRs in Timmy-time-dashboard for p0-critical issues
+7. **Archive** `hermes/timmy-time-app` if no longer needed
+8. **Clarify** ownership of `google/maintenance-tasks` and `google/wizard-council-automation`
+
+### Process Improvements
+9. **Enforce one-agent-per-issue** policy to prevent duplicate `claude/*` / `gemini/*` branches
+10. **Add branch protection** requiring PR before merge on `main` for all repos
+11. **Set a branch retention policy** — auto-delete merged branches (GitHub/Gitea supports this)
+12. **Share common libraries** for Nostr identity and animation physics across repos
+
+---
+
+*Report generated by Claude audit agent. Improvement tickets filed per repo as follow-up to this report.*

docs/SOVEREIGNTY_LOOP.md

@@ -0,0 +1,111 @@
+# The Sovereignty Loop
+
+This document establishes the primary engineering constraint for all Timmy Time development: every task must increase sovereignty as a default deliverable. Not as a future goal. Not as an optimization pass. As a constraint on every commit, every function, every inference call.
+
+The full 11-page governing architecture document is available as a PDF: [The-Sovereignty-Loop.pdf](./The-Sovereignty-Loop.pdf)
+
+> "The measure of progress is not features added. It is model calls eliminated."
+
+## The Core Principle
+
+> **The Sovereignty Loop**: Discover with an expensive model. Compress the discovery into a cheap local rule. Replace the model with the rule. Measure the cost reduction. Repeat.
+
+Every call to an LLM, VLM, or external API passes through three phases:
+1. **Discovery** — Model sees something for the first time (expensive, unavoidable, produces new knowledge)
+2. **Crystallization** — Discovery compressed into durable cheap artifact (requires explicit engineering)
+3. **Replacement** — Crystallized artifact replaces the model call (near-zero cost)
+
+**Code review requirement**: If a function calls a model without a crystallization step, it fails code review. No exceptions. The pattern is always: check cache → miss → infer → crystallize → return.
+
+## The Sovereignty Loop Applied to Every Layer
+
+### Perception: See Once, Template Forever
+- First encounter: VLM analyzes screenshot (3-6 sec) → structured JSON
+- Crystallized as: OpenCV template + bounding box → `templates.json` (3 ms retrieval)
+- `crystallize_perception()` function wraps every VLM response
+- **Target**: 90% of perception cycles without VLM by hour 1, 99% by hour 4
+
+### Decision: Reason Once, Rule Forever
+- First encounter: LLM reasons through decision (1-5 sec)
+- Crystallized as: if/else rules, waypoints, cached preferences → `rules.py`, `nav_graph.db` (<1 ms)
+- Uses Voyager pattern: named skills with embeddings, success rates, conditions
+- Skill match >0.8 confidence + >0.6 success rate → executes without LLM
+- **Target**: 70-80% of decisions without LLM by week 4
+
+### Narration: Script the Predictable, Improvise the Novel
+- Predictable moments → template with variable slots, voiced by Kokoro locally
+- LLM narrates only genuinely surprising events (quest twist, death, discovery)
+- **Target**: 60-70% templatized within a week
+
+### Navigation: Walk Once, Map Forever
+- Every path recorded as waypoint sequence with terrain annotations
+- First journey = full perception + planning; subsequent = graph traversal
+- Builds complete nav graph without external map data
+
+### API Costs: Every Dollar Spent Must Reduce Future Dollars
+
+| Week | Groq Calls/Hr | Local Decisions/Hr | Sovereignty % | Cost/Hr |
+|---|---|---|---|---|
+| 1 | ~720 | ~80 | 10% | $0.40 |
+| 2 | ~400 | ~400 | 50% | $0.22 |
+| 4 | ~160 | ~640 | 80% | $0.09 |
+| 8 | ~40 | ~760 | 95% | $0.02 |
+| Target | <20 | >780 | >97% | <$0.01 |
+
+## The Sovereignty Scorecard (5 Metrics)
+
+Every work session ends with a sovereignty audit. Every PR includes a sovereignty delta. Not optional.
+
+| Metric | What It Measures | Target |
+|---|---|---|
+| Perception Sovereignty % | Frames understood without VLM | >90% by hour 4 |
+| Decision Sovereignty % | Actions chosen without LLM | >80% by week 4 |
+| Narration Sovereignty % | Lines from templates vs LLM | >60% by week 2 |
+| API Cost Trend | Dollar cost per hour of gameplay | Monotonically decreasing |
+| Skill Library Growth | Crystallized skills per session | >5 new skills/session |
+
+Dashboard widget on alexanderwhitestone.com shows these in real-time during streams. HTMX component via WebSocket.
+
+## The Crystallization Protocol
+
+Every model output gets crystallized:
+
+| Model Output | Crystallized As | Storage | Retrieval Cost |
+|---|---|---|---|
+| VLM: UI element | OpenCV template + bbox | templates.json | 3 ms |
+| VLM: text | OCR region coords | regions.json | 50 ms |
+| LLM: nav plan | Waypoint sequence | nav_graph.db | <1 ms |
+| LLM: combat decision | If/else rule on state | rules.py | <1 ms |
+| LLM: quest interpretation | Structured entry | quests.db | <1 ms |
+| LLM: NPC disposition | Name→attitude map | npcs.db | <1 ms |
+| LLM: narration | Template with slots | narration.json | <1 ms |
+| API: moderation | Approved phrase cache | approved.set | <1 ms |
+| Groq: strategic plan | Extracted decision rules | strategy.json | <1 ms |
+
+Skill document format: markdown + YAML frontmatter following agentskills.io standard (name, game, type, success_rate, times_used, sovereignty_value).
+
+## The Automation Imperative & Three-Strike Rule
+
+Applies to developer workflow too, not just the agent. If you do the same thing manually three times, you stop and write the automation before proceeding.
+
+**Falsework Checklist** (before any cloud API call):
+1. What durable artifact will this call produce?
+2. Where will the artifact be stored locally?
+3. What local rule or cache will this populate?
+4. After this call, will I need to make it again?
+5. If yes, what would eliminate the repeat?
+6. What is the sovereignty delta of this call?
+
+## The Graduation Test (Falsework Removal Criteria)
+
+All five conditions met simultaneously in a single 24-hour period:
+
+| Test | Condition | Measurement |
+|---|---|---|
+| Perception Independence | 1 hour, no VLM calls after minute 15 | VLM calls in last 45 min = 0 |
+| Decision Independence | Full session with <5 API calls total | Groq/cloud calls < 5 |
+| Narration Independence | All narration from local templates + local LLM | Zero cloud TTS/narration calls |
+| Economic Independence | Earns more sats than spends on inference | sats_earned > sats_spent |
+| Operational Independence | 24 hours unattended, no human intervention | Uptime > 23.5 hrs |
+
+> "The arch must hold after the falsework is removed."

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docs/adr/024-nostr-identity-canonical-location.md

@@ -0,0 +1,160 @@
+# ADR-024: Canonical Nostr Identity Location
+
+**Status:** Accepted
+**Date:** 2026-03-23
+**Issue:** #1223
+**Refs:** #1210 (duplicate-work audit), ROADMAP.md Phase 2
+
+---
+
+## Context
+
+Nostr identity logic has been independently implemented in at least three
+repos (`replit/timmy-tower`, `replit/token-gated-economy`,
+`rockachopa/Timmy-time-dashboard`), each building keypair generation, event
+publishing, and NIP-07 browser-extension auth in isolation.
+
+This duplication causes:
+
+- Bug fixes applied in one repo but silently missed in others.
+- Diverging implementations of the same NIPs (NIP-01, NIP-07, NIP-44).
+- Agent time wasted re-implementing logic that already exists.
+
+ROADMAP.md Phase 2 already names `timmy-nostr` as the planned home for Nostr
+infrastructure. This ADR makes that decision explicit and prescribes how
+other repos consume it.
+
+---
+
+## Decision
+
+**The canonical home for all Nostr identity logic is `rockachopa/timmy-nostr`.**
+
+All other repos (`Timmy-time-dashboard`, `timmy-tower`,
+`token-gated-economy`) become consumers, not implementers, of Nostr identity
+primitives.
+
+### What lives in `timmy-nostr`
+
+| Module | Responsibility |
+|--------|---------------|
+| `nostr_id/keypair.py` | Keypair generation, nsec/npub encoding, encrypted storage |
+| `nostr_id/identity.py` | Agent identity lifecycle (NIP-01 kind:0 profile events) |
+| `nostr_id/auth.py` | NIP-07 browser-extension signer; NIP-42 relay auth |
+| `nostr_id/event.py` | Event construction, signing, serialisation (NIP-01) |
+| `nostr_id/crypto.py` | NIP-44 encryption (XChaCha20-Poly1305 v2) |
+| `nostr_id/nip05.py` | DNS-based identifier verification |
+| `nostr_id/relay.py` | WebSocket relay client (publish / subscribe) |
+
+### What does NOT live in `timmy-nostr`
+
+- Business logic that combines Nostr with application-specific concepts
+  (e.g. "publish a task-completion event" lives in the application layer
+  that calls `timmy-nostr`).
+- Reputation scoring algorithms (depends on application policy).
+- Dashboard UI components.
+
+---
+
+## How Other Repos Reference `timmy-nostr`
+
+### Python repos (`Timmy-time-dashboard`, `timmy-tower`)
+
+Add to `pyproject.toml` dependencies:
+
+```toml
+[tool.poetry.dependencies]
+timmy-nostr = {git = "https://gitea.hermes.local/rockachopa/timmy-nostr.git", tag = "v0.1.0"}
+```
+
+Import pattern:
+
+```python
+from nostr_id.keypair import generate_keypair, load_keypair
+from nostr_id.event import build_event, sign_event
+from nostr_id.relay import NostrRelayClient
+```
+
+### JavaScript/TypeScript repos (`token-gated-economy` frontend)
+
+Add to `package.json` (once published or via local path):
+
+```json
+"dependencies": {
+  "timmy-nostr": "rockachopa/timmy-nostr#v0.1.0"
+}
+```
+
+Import pattern:
+
+```typescript
+import { generateKeypair, signEvent } from 'timmy-nostr';
+```
+
+Until `timmy-nostr` publishes a JS package, use NIP-07 browser extension
+directly and delegate all key-management to the browser signer — never
+re-implement crypto in JS without the shared library.
+
+---
+
+## Migration Plan
+
+Existing duplicated code should be migrated in this order:
+
+1. **Keypair generation** — highest duplication, clearest interface.
+2. **NIP-01 event construction/signing** — used by all three repos.
+3. **NIP-07 browser auth** — currently in `timmy-tower` and `token-gated-economy`.
+4. **NIP-44 encryption** — lowest priority, least duplicated.
+
+Each step: implement in `timmy-nostr` → cut over one repo → delete the
+duplicate → repeat.
+
+---
+
+## Interface Contract
+
+`timmy-nostr` must expose a stable public API:
+
+```python
+# Keypair
+keypair = generate_keypair()           # -> NostrKeypair(nsec, npub, privkey_bytes, pubkey_bytes)
+keypair = load_keypair(encrypted_nsec, secret_key)
+
+# Events
+event = build_event(kind=0, content=profile_json, keypair=keypair)
+event = sign_event(event, keypair)     # attaches .id and .sig
+
+# Relay
+async with NostrRelayClient(url) as relay:
+    await relay.publish(event)
+    async for msg in relay.subscribe(filters):
+        ...
+```
+
+Breaking changes to this interface require a semver major bump and a
+migration note in `timmy-nostr`'s CHANGELOG.
+
+---
+
+## Consequences
+
+- **Positive:** Bug fixes in cryptographic or protocol code propagate to all
+  repos via a version bump.
+- **Positive:** New NIPs are implemented once and adopted everywhere.
+- **Negative:** Adds a cross-repo dependency; version pinning discipline
+  required.
+- **Negative:** `timmy-nostr` must be stood up and tagged before any
+  migration can begin.
+
+---
+
+## Action Items
+
+- [ ] Create `rockachopa/timmy-nostr` repo with the module structure above.
+- [ ] Implement keypair generation + NIP-01 signing as v0.1.0.
+- [ ] Replace `Timmy-time-dashboard` inline Nostr code (if any) with
+  `timmy-nostr` import once v0.1.0 is tagged.
+- [ ] Add `src/infrastructure/clients/nostr_client.py` as the thin
+  application-layer wrapper (see ROADMAP.md §2.6).
+- [ ] File issues in `timmy-tower` and `token-gated-economy` to migrate their
+  duplicate implementations.

docs/issue-1097-bannerlord-m5-response.md

@@ -0,0 +1,100 @@
+# Issue #1097 — Bannerlord M5 Sovereign Victory: Implementation
+
+**Date:** 2026-03-23
+**Status:** Python stack implemented — game infrastructure pending
+
+## Summary
+
+Issue #1097 is the final milestone of Project Bannerlord (#1091): Timmy holds
+the title of King with majority territory control through pure local strategy.
+
+This PR implements the Python-side sovereign victory stack (`src/bannerlord/`).
+The game-side infrastructure (Windows VM, GABS C# mod) remains external to this
+repository, consistent with the scope decision on M4 (#1096).
+
+## What was implemented
+
+### `src/bannerlord/` package
+
+| Module | Purpose |
+|--------|---------|
+| `models.py` | Pydantic data contracts — KingSubgoal, SubgoalMessage, TaskMessage, ResultMessage, StateUpdateMessage, reward functions, VictoryCondition |
+| `gabs_client.py` | Async TCP JSON-RPC client for Bannerlord.GABS (port 4825), graceful degradation when game server is offline |
+| `ledger.py` | SQLite-backed asset ledger — treasury, fiefs, vassal budgets, campaign tick log |
+| `agents/king.py` | King agent — Qwen3:32b, 1× per campaign day, sovereign campaign loop, victory detection, subgoal broadcast |
+| `agents/vassals.py` | War / Economy / Diplomacy vassals — Qwen3:14b, domain reward functions, primitive dispatch |
+| `agents/companions.py` | Logistics / Caravan / Scout companions — event-driven, primitive execution against GABS |
+
+### `tests/unit/test_bannerlord/` — 56 unit tests
+
+- `test_models.py` — Pydantic validation, reward math, victory condition logic
+- `test_gabs_client.py` — Connection lifecycle, RPC dispatch, error handling, graceful degradation
+- `test_agents.py` — King campaign loop, vassal subgoal routing, companion primitive execution
+
+All 56 tests pass.
+
+## Architecture
+
+```
+KingAgent (Qwen3:32b, 1×/day)
+  └── KingSubgoal → SubgoalQueue
+        ├── WarVassal (Qwen3:14b, 4×/day)
+        │     └── TaskMessage → LogisticsCompanion
+        │           └── GABS: move_party, recruit_troops, upgrade_troops
+        ├── EconomyVassal (Qwen3:14b, 4×/day)
+        │     └── TaskMessage → CaravanCompanion
+        │           └── GABS: assess_prices, buy_goods, establish_caravan
+        └── DiplomacyVassal (Qwen3:14b, 4×/day)
+              └── TaskMessage → ScoutCompanion
+                    └── GABS: track_lord, assess_garrison, report_intel
+```
+
+## Subgoal vocabulary
+
+| Token | Vassal | Meaning |
+|-------|--------|---------|
+| `EXPAND_TERRITORY` | War | Take or secure a fief |
+| `RAID_ECONOMY` | War | Raid enemy villages for denars |
+| `TRAIN` | War | Level troops via auto-resolve |
+| `FORTIFY` | Economy | Upgrade or repair a settlement |
+| `CONSOLIDATE` | Economy | Hold territory, no expansion |
+| `TRADE` | Economy | Execute profitable trade route |
+| `ALLY` | Diplomacy | Pursue non-aggression / alliance |
+| `RECRUIT` | Logistics | Fill party to capacity |
+| `HEAL` | Logistics | Rest party until wounds recovered |
+| `SPY` | Scout | Gain information on target faction |
+
+## Victory condition
+
+```python
+VictoryCondition(
+    holds_king_title=True,           # player_title == "King" from GABS
+    territory_control_pct=55.0,      # > 51% of Calradia fiefs
+)
+```
+
+## Graceful degradation
+
+When GABS is offline (game not running), `GABSClient` logs a warning and raises
+`GABSUnavailable`. The King agent catches this and runs with an empty game state
+(falls back to RECRUIT subgoal). No part of the dashboard crashes.
+
+## Remaining prerequisites
+
+Before M5 can run live:
+
+1. **M1-M3** — Passive observer, basic campaign actions, full campaign strategy
+   (currently open; their Python stubs can build on this `src/bannerlord/` package)
+2. **M4** — Formation Commander (#1096) — declined as out-of-scope; M5 works
+   around M4 by using Bannerlord's Tactics auto-resolve path
+3. **Windows VM** — Mount & Blade II: Bannerlord + GABS mod (BUTR/Bannerlord.GABS)
+4. **OBS streaming** — Cinematic Camera pipeline (Step 3 of M5) — external to repo
+5. **BattleLink** — Alex co-op integration (Step 4 of M5) — requires dedicated server
+
+## Design references
+
+- Ahilan & Dayan (2019): Feudal Multi-Agent Hierarchies — manager/worker hierarchy
+- Wang et al. (2023): Voyager — LLM lifelong learning pattern
+- Feudal hierarchy design doc: `docs/research/bannerlord-feudal-hierarchy-design.md`
+
+Fixes #1097

docs/nexus-spec.md

@@ -0,0 +1,105 @@
+# Nexus — Scope & Acceptance Criteria
+
+**Issue:** #1208
+**Date:** 2026-03-23
+**Status:** Initial implementation complete; teaching/RL harness deferred
+
+---
+
+## Summary
+
+The **Nexus** is a persistent conversational space where Timmy lives with full
+access to his live memory. Unlike the main dashboard chat (which uses tools and
+has a transient feel), the Nexus is:
+
+- **Conversational only** — no tool approval flow; pure dialogue
+- **Memory-aware** — semantically relevant memories surface alongside each exchange
+- **Teachable** — the operator can inject facts directly into Timmy's live memory
+- **Persistent** — the session survives page refreshes; history accumulates over time
+- **Local** — always backed by Ollama; no cloud inference required
+
+This is the foundation for future LoRA fine-tuning, RL training harnesses, and
+eventually real-time self-improvement loops.
+
+---
+
+## Scope (v1 — this PR)
+
+| Area | Included | Deferred |
+|------|----------|----------|
+| Conversational UI | ✅ Chat panel with HTMX streaming | Streaming tokens |
+| Live memory sidebar | ✅ Semantic search on each turn | Auto-refresh on teach |
+| Teaching panel | ✅ Inject personal facts | Bulk import, LoRA trigger |
+| Session isolation | ✅ Dedicated `nexus` session ID | Per-operator sessions |
+| Nav integration | ✅ NEXUS link in INTEL dropdown | Mobile nav |
+| CSS/styling | ✅ Two-column responsive layout | Dark/light theme toggle |
+| Tests | ✅ 9 unit tests, all green | E2E with real Ollama |
+| LoRA / RL harness | ❌ deferred to future issue | |
+| Auto-falsework | ❌ deferred | |
+| Bannerlord interface | ❌ separate track | |
+
+---
+
+## Acceptance Criteria
+
+### AC-1: Nexus page loads
+- **Given** the dashboard is running
+- **When** I navigate to `/nexus`
+- **Then** I see a two-panel layout: conversation on the left, memory sidebar on the right
+- **And** the page title reads "// NEXUS"
+- **And** the page is accessible from the nav (INTEL → NEXUS)
+
+### AC-2: Conversation-only chat
+- **Given** I am on the Nexus page
+- **When** I type a message and submit
+- **Then** Timmy responds using the `nexus` session (isolated from dashboard history)
+- **And** no tool-approval cards appear — responses are pure text
+- **And** my message and Timmy's reply are appended to the chat log
+
+### AC-3: Memory context surfaces automatically
+- **Given** I send a message
+- **When** the response arrives
+- **Then** the "LIVE MEMORY CONTEXT" panel shows up to 4 semantically relevant memories
+- **And** each memory entry shows its type and content
+
+### AC-4: Teaching panel stores facts
+- **Given** I type a fact into the "TEACH TIMMY" input and submit
+- **When** the request completes
+- **Then** I see a green confirmation "✓ Taught: <fact>"
+- **And** the fact appears in the "KNOWN FACTS" list
+- **And** the fact is stored in Timmy's live memory (`store_personal_fact`)
+
+### AC-5: Empty / invalid input is rejected gracefully
+- **Given** I submit a blank message or fact
+- **Then** no request is made and the log is unchanged
+- **Given** I submit a message over 10 000 characters
+- **Then** an inline error is shown without crashing the server
+
+### AC-6: Conversation can be cleared
+- **Given** the Nexus has conversation history
+- **When** I click CLEAR and confirm
+- **Then** the chat log shows only a "cleared" confirmation
+- **And** the Agno session for `nexus` is reset
+
+### AC-7: Graceful degradation when Ollama is down
+- **Given** Ollama is unavailable
+- **When** I send a message
+- **Then** an error message is shown inline (not a 500 page)
+- **And** the app continues to function
+
+### AC-8: No regression on existing tests
+- **Given** the nexus route is registered
+- **When** `tox -e unit` runs
+- **Then** all 343+ existing tests remain green
+
+---
+
+## Future Work (separate issues)
+
+1. **LoRA trigger** — button in the teaching panel to queue a fine-tuning run
+   using the current Nexus conversation as training data
+2. **RL harness** — reward signal collection during conversation for RLHF
+3. **Auto-falsework pipeline** — scaffold harness generation from conversation
+4. **Bannerlord interface** — Nexus as the live-memory bridge for in-game Timmy
+5. **Streaming responses** — token-by-token display via WebSocket
+6. **Per-operator sessions** — isolate Nexus history by logged-in user

docs/pr-recovery-1219.md

@@ -0,0 +1,75 @@
+# PR Recovery Investigation — Issue #1219
+
+**Audit source:** Issue #1210
+
+Five PRs were closed without merge while their parent issues remained open and
+marked p0-critical. This document records the investigation findings and the
+path to resolution for each.
+
+---
+
+## Root Cause
+
+Per Timmy's comment on #1219: all five PRs were closed due to **merge conflicts
+during the mass-merge cleanup cycle** (a rebase storm), not due to code
+quality problems or a changed approach. The code in each PR was correct;
+the branches simply became stale.
+
+---
+
+## Status Matrix
+
+| PR | Feature | Issue | PR Closed | Issue State | Resolution |
+|----|---------|-------|-----------|-------------|------------|
+| #1163 | Three-Strike Detector | #962 | Rebase storm | **Closed ✓** | v2 merged via PR #1232 |
+| #1162 | Session Sovereignty Report | #957 | Rebase storm | **Open** | PR #1263 (v3 — rebased) |
+| #1157 | Qwen3-8B/14B routing | #1065 | Rebase storm | **Closed ✓** | v2 merged via PR #1233 |
+| #1156 | Agent Dreaming Mode | #1019 | Rebase storm | **Open** | PR #1264 (v3 — rebased) |
+| #1145 | Qwen3-14B config | #1064 | Rebase storm | **Closed ✓** | Code present on main |
+
+---
+
+## Detail: Already Resolved
+
+### PR #1163 → Issue #962 (Three-Strike Detector)
+
+- **Why closed:** merge conflict during rebase storm
+- **Resolution:** `src/timmy/sovereignty/three_strike.py` and
+  `src/dashboard/routes/three_strike.py` are present on `main` (landed via
+  PR #1232). Issue #962 is closed.
+
+### PR #1157 → Issue #1065 (Qwen3-8B/14B dual-model routing)
+
+- **Why closed:** merge conflict during rebase storm
+- **Resolution:** `src/infrastructure/router/classifier.py` and
+  `src/infrastructure/router/cascade.py` are present on `main` (landed via
+  PR #1233). Issue #1065 is closed.
+
+### PR #1145 → Issue #1064 (Qwen3-14B config)
+
+- **Why closed:** merge conflict during rebase storm
+- **Resolution:** `Modelfile.timmy`, `Modelfile.qwen3-14b`, and the `config.py`
+  defaults (`ollama_model = "qwen3:14b"`) are present on `main`. Issue #1064
+  is closed.
+
+---
+
+## Detail: Requiring Action
+
+### PR #1162 → Issue #957 (Session Sovereignty Report Generator)
+
+- **Why closed:** merge conflict during rebase storm
+- **Branch preserved:** `claude/issue-957-v2` (one feature commit)
+- **Action taken:** Rebased onto current `main`, resolved conflict in
+  `src/timmy/sovereignty/__init__.py` (both three-strike and session-report
+  docstrings kept). All 458 unit tests pass.
+- **New PR:** #1263 (`claude/issue-957-v3` → `main`)
+
+### PR #1156 → Issue #1019 (Agent Dreaming Mode)
+
+- **Why closed:** merge conflict during rebase storm
+- **Branch preserved:** `claude/issue-1019-v2` (one feature commit)
+- **Action taken:** Rebased onto current `main`, resolved conflict in
+  `src/dashboard/app.py` (both `three_strike_router` and `dreaming_router`
+  registered). All 435 unit tests pass.
+- **New PR:** #1264 (`claude/issue-1019-v3` → `main`)

docs/research/autoresearch-h1-baseline.md

@@ -0,0 +1,132 @@
+# Autoresearch H1 — M3 Max Baseline
+
+**Status:** Baseline established (Issue #905)
+**Hardware:** Apple M3 Max · 36 GB unified memory
+**Date:** 2026-03-23
+**Refs:** #905 · #904 (parent) · #881 (M3 Max compute) · #903 (MLX benchmark)
+
+---
+
+## Setup
+
+### Prerequisites
+
+```bash
+# Install MLX (Apple Silicon — definitively faster than llama.cpp per #903)
+pip install mlx mlx-lm
+
+# Install project deps
+tox -e dev  # or: pip install -e '.[dev]'
+```
+
+### Clone & prepare
+
+`prepare_experiment` in `src/timmy/autoresearch.py` handles the clone.
+On Apple Silicon it automatically sets `AUTORESEARCH_BACKEND=mlx` and
+`AUTORESEARCH_DATASET=tinystories`.
+
+```python
+from timmy.autoresearch import prepare_experiment
+status = prepare_experiment("data/experiments", dataset="tinystories", backend="auto")
+print(status)
+```
+
+Or via the dashboard: `POST /experiments/start` (requires `AUTORESEARCH_ENABLED=true`).
+
+### Configuration (`.env` / environment)
+
+```
+AUTORESEARCH_ENABLED=true
+AUTORESEARCH_DATASET=tinystories   # lower-entropy dataset, faster iteration on Mac
+AUTORESEARCH_BACKEND=auto          # resolves to "mlx" on Apple Silicon
+AUTORESEARCH_TIME_BUDGET=300       # 5-minute wall-clock budget per experiment
+AUTORESEARCH_MAX_ITERATIONS=100
+AUTORESEARCH_METRIC=val_bpb
+```
+
+### Why TinyStories?
+
+Karpathy's recommendation for resource-constrained hardware: lower entropy
+means the model can learn meaningful patterns in less time and with a smaller
+vocabulary, yielding cleaner val_bpb curves within the 5-minute budget.
+
+---
+
+## M3 Max Hardware Profile
+
+| Spec | Value |
+|------|-------|
+| Chip | Apple M3 Max |
+| CPU cores | 16 (12P + 4E) |
+| GPU cores | 40 |
+| Unified RAM | 36 GB |
+| Memory bandwidth | 400 GB/s |
+| MLX support | Yes (confirmed #903) |
+
+MLX utilises the unified memory architecture — model weights, activations, and
+training data all share the same physical pool, eliminating PCIe transfers.
+This gives M3 Max a significant throughput advantage over external GPU setups
+for models that fit in 36 GB.
+
+---
+
+## Community Reference Data
+
+| Hardware | Experiments | Succeeded | Failed | Outcome |
+|----------|-------------|-----------|--------|---------|
+| Mac Mini M4 | 35 | 7 | 28 | Model improved by simplifying |
+| Shopify (overnight) | ~50 | — | — | 19% quality gain; smaller beat 2× baseline |
+| SkyPilot (16× GPU, 8 h) | ~910 | — | — | 2.87% improvement |
+| Karpathy (H100, 2 days) | ~700 | 20+ | — | 11% training speedup |
+
+**Mac Mini M4 failure rate: 80% (26/35).** Failures are expected and by design —
+the 5-minute budget deliberately prunes slow experiments. The 20% success rate
+still yielded an improved model.
+
+---
+
+## Baseline Results (M3 Max)
+
+> Fill in after running: `timmy learn --target <module> --metric val_bpb --budget 5 --max-experiments 50`
+
+| Run | Date | Experiments | Succeeded | val_bpb (start) | val_bpb (end) | Δ |
+|-----|------|-------------|-----------|-----------------|---------------|---|
+| 1 | — | — | — | — | — | — |
+
+### Throughput estimate
+
+Based on the M3 Max hardware profile and Mac Mini M4 community data, expected
+throughput is **8–14 experiments/hour** with the 5-minute budget and TinyStories
+dataset. The M3 Max has ~30% higher GPU core count and identical memory
+bandwidth class vs M4, so performance should be broadly comparable.
+
+---
+
+## Apple Silicon Compatibility Notes
+
+### MLX path (recommended)
+
+- Install: `pip install mlx mlx-lm`
+- `AUTORESEARCH_BACKEND=auto` resolves to `mlx` on arm64 macOS
+- Pros: unified memory, no PCIe overhead, native Metal backend
+- Cons: MLX op coverage is a subset of PyTorch; some custom CUDA kernels won't port
+
+### llama.cpp path (fallback)
+
+- Use when MLX op support is insufficient
+- Set `AUTORESEARCH_BACKEND=cpu` to force CPU mode
+- Slower throughput but broader op compatibility
+
+### Known issues
+
+- `subprocess.TimeoutExpired` is the normal termination path — autoresearch
+  treats timeout as a completed-but-pruned experiment, not a failure
+- Large batch sizes may trigger OOM if other processes hold unified memory;
+  set `PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0` to disable the MPS high-watermark
+
+---
+
+## Next Steps (H2)
+
+See #904 Horizon 2 for the meta-autoresearch plan: expand experiment units from
+code changes → system configuration changes (prompts, tools, memory strategies).

index_research_docs.py

@@ -0,0 +1,33 @@
+
+import os
+import sys
+from pathlib import Path
+
+# Add the src directory to the Python path
+sys.path.insert(0, str(Path(__file__).parent / "src"))
+
+from timmy.memory_system import memory_store
+
+def index_research_documents():
+    research_dir = Path("docs/research")
+    if not research_dir.is_dir():
+        print(f"Research directory not found: {research_dir}")
+        return
+
+    print(f"Indexing research documents from {research_dir}...")
+    indexed_count = 0
+    for file_path in research_dir.glob("*.md"):
+        try:
+            content = file_path.read_text()
+            topic = file_path.stem.replace("-", " ").title() # Derive topic from filename
+            print(f"Storing '{topic}' from {file_path.name}...")
+            # Using type="research" as per issue requirement
+            result = memory_store(topic=topic, report=content, type="research")
+            print(f"  Result: {result}")
+            indexed_count += 1
+        except Exception as e:
+            print(f"Error indexing {file_path.name}: {e}")
+    print(f"Finished indexing. Total documents indexed: {indexed_count}")
+
+if __name__ == "__main__":
+    index_research_documents()

poetry.lock

@@ -2936,10 +2936,9 @@ numpy = ">=1.22,<2.5"
 name = "numpy"
 version = "2.4.2"
 description = "Fundamental package for array computing in Python"
-optional = true
+optional = false
 python-versions = ">=3.11"
 groups = ["main"]
-markers = "extra == \"bigbrain\" or extra == \"embeddings\" or extra == \"voice\""
 files = [
     {file = "numpy-2.4.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e7e88598032542bd49af7c4747541422884219056c268823ef6e5e89851c8825"},
     {file = "numpy-2.4.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7edc794af8b36ca37ef5fcb5e0d128c7e0595c7b96a2318d1badb6fcd8ee86b1"},
@@ -3347,6 +3346,27 @@ triton = {version = ">=2", markers = "platform_machine == \"x86_64\" and sys_pla
 [package.extras]
 dev = ["black", "flake8", "isort", "pytest", "scipy"]
 
+[[package]]
+name = "opencv-python"
+version = "4.13.0.92"
+description = "Wrapper package for OpenCV python bindings."
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "opencv_python-4.13.0.92-cp37-abi3-macosx_13_0_arm64.whl", hash = "sha256:caf60c071ec391ba51ed00a4a920f996d0b64e3e46068aac1f646b5de0326a19"},
+    {file = "opencv_python-4.13.0.92-cp37-abi3-macosx_14_0_x86_64.whl", hash = "sha256:5868a8c028a0b37561579bfb8ac1875babdc69546d236249fff296a8c010ccf9"},
+    {file = "opencv_python-4.13.0.92-cp37-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0bc2596e68f972ca452d80f444bc404e08807d021fbba40df26b61b18e01838a"},
+    {file = "opencv_python-4.13.0.92-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:402033cddf9d294693094de5ef532339f14ce821da3ad7df7c9f6e8316da32cf"},
+    {file = "opencv_python-4.13.0.92-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:bccaabf9eb7f897ca61880ce2869dcd9b25b72129c28478e7f2a5e8dee945616"},
+    {file = "opencv_python-4.13.0.92-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:620d602b8f7d8b8dab5f4b99c6eb353e78d3fb8b0f53db1bd258bb1aa001c1d5"},
+    {file = "opencv_python-4.13.0.92-cp37-abi3-win32.whl", hash = "sha256:372fe164a3148ac1ca51e5f3ad0541a4a276452273f503441d718fab9c5e5f59"},
+    {file = "opencv_python-4.13.0.92-cp37-abi3-win_amd64.whl", hash = "sha256:423d934c9fafb91aad38edf26efb46da91ffbc05f3f59c4b0c72e699720706f5"},
+]
+
+[package.dependencies]
+numpy = {version = ">=2", markers = "python_version >= \"3.9\""}
+
 [[package]]
 name = "optimum"
 version = "2.1.0"
@@ -9700,4 +9720,4 @@ voice = ["openai-whisper", "piper-tts", "pyttsx3", "sounddevice"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.11,<4"
-content-hash = "cc50755f322b8755e85ab7bdf0668609612d885552aba14caf175326eedfa216"
+content-hash = "5af3028474051032bef12182eaa5ef55950cbaeca21d1793f878d54c03994eb0"

program.md

@@ -0,0 +1,23 @@
+# Research Direction
+
+This file guides the `timmy learn` autoresearch loop.  Edit it to focus
+autonomous experiments on a specific goal.
+
+## Current Goal
+
+Improve unit test pass rate across the codebase by identifying and fixing
+fragile or failing tests.
+
+## Target Module
+
+(Set via `--target` when invoking `timmy learn`)
+
+## Success Metric
+
+unit_pass_rate — percentage of unit tests passing in `tox -e unit`.
+
+## Notes
+
+- Experiments run one at a time; each is time-boxed by `--budget`.
+- Improvements are committed automatically; regressions are reverted.
+- Use `--dry-run` to preview hypotheses without making changes.

pyproject.toml

@@ -14,6 +14,7 @@ repository = "http://localhost:3000/rockachopa/Timmy-time-dashboard"
 packages = [
     { include = "config.py", from = "src" },
 
+    { include = "bannerlord", from = "src" },
     { include = "dashboard", from = "src" },
     { include = "infrastructure", from = "src" },
     { include = "integrations", from = "src" },
@@ -60,6 +61,7 @@ selenium = { version = ">=4.20.0", optional = true }
 pytest-randomly = { version = ">=3.16.0", optional = true }
 pytest-xdist = { version = ">=3.5.0", optional = true }
 anthropic = "^0.86.0"
+opencv-python = "^4.13.0.92"
 
 [tool.poetry.extras]
 telegram = ["python-telegram-bot"]
@@ -96,7 +98,7 @@ asyncio_default_fixture_loop_scope = "function"
 timeout = 30
 timeout_method = "signal"
 timeout_func_only = false
-addopts = "-v --tb=short --strict-markers --disable-warnings --durations=10"
+addopts = "-v --tb=short --strict-markers --disable-warnings --durations=10 --cov-fail-under=60"
 markers = [
     "unit: Unit tests (fast, no I/O)",
     "integration: Integration tests (may use SQLite)",

scripts/benchmark_local_model.sh

@@ -0,0 +1,293 @@
+#!/usr/bin/env bash
+# benchmark_local_model.sh
+#
+# 5-test benchmark suite for evaluating local Ollama models as Timmy's agent brain.
+# Based on the model selection study for M3 Max 36 GB (Issue #1063).
+#
+# Usage:
+#   ./scripts/benchmark_local_model.sh                        # test $OLLAMA_MODEL or qwen3:14b
+#   ./scripts/benchmark_local_model.sh qwen3:8b               # test a specific model
+#   ./scripts/benchmark_local_model.sh qwen3:14b qwen3:8b     # compare two models
+#
+# Thresholds (pass/fail):
+#   Test 1 — Tool call compliance:   >=90% valid JSON responses out of 5 probes
+#   Test 2 — Code generation:        compiles without syntax errors
+#   Test 3 — Shell command gen:      no refusal markers in output
+#   Test 4 — Multi-turn coherence:   session ID echoed back correctly
+#   Test 5 — Issue triage quality:   structured JSON with required fields
+#
+# Exit codes: 0 = all tests passed, 1 = one or more tests failed
+
+set -euo pipefail
+
+OLLAMA_URL="${OLLAMA_URL:-http://localhost:11434}"
+PASS=0
+FAIL=0
+TOTAL=0
+
+# ── Colours ──────────────────────────────────────────────────────────────────
+GREEN='\033[0;32m'
+RED='\033[0;31m'
+YELLOW='\033[1;33m'
+BOLD='\033[1m'
+RESET='\033[0m'
+
+pass() { echo -e "  ${GREEN}✓ PASS${RESET}  $1"; ((PASS++)); ((TOTAL++)); }
+fail() { echo -e "  ${RED}✗ FAIL${RESET}  $1"; ((FAIL++)); ((TOTAL++)); }
+info() { echo -e "  ${YELLOW}ℹ${RESET}  $1"; }
+
+# ── Helper: call Ollama generate API ─────────────────────────────────────────
+ollama_generate() {
+    local model="$1"
+    local prompt="$2"
+    local extra_opts="${3:-}"
+
+    local payload
+    payload=$(printf '{"model":"%s","prompt":"%s","stream":false%s}' \
+        "$model" \
+        "$(echo "$prompt" | sed 's/"/\\"/g' | tr -d '\n')" \
+        "${extra_opts:+,$extra_opts}")
+
+    curl -s --max-time 60 \
+        -X POST "${OLLAMA_URL}/api/generate" \
+        -H "Content-Type: application/json" \
+        -d "$payload" \
+    | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('response',''))" 2>/dev/null || echo ""
+}
+
+# ── Helper: call Ollama chat API with tool schema ─────────────────────────────
+ollama_chat_tool() {
+    local model="$1"
+    local user_msg="$2"
+
+    local payload
+    payload=$(cat <<EOF
+{
+  "model": "$model",
+  "messages": [{"role": "user", "content": "$user_msg"}],
+  "tools": [{
+    "type": "function",
+    "function": {
+      "name": "get_current_weather",
+      "description": "Get the current weather for a location",
+      "parameters": {
+        "type": "object",
+        "properties": {
+          "location": {"type": "string", "description": "City name"},
+          "unit": {"type": "string", "enum": ["celsius","fahrenheit"]}
+        },
+        "required": ["location"]
+      }
+    }
+  }],
+  "stream": false
+}
+EOF
+)
+    curl -s --max-time 60 \
+        -X POST "${OLLAMA_URL}/api/chat" \
+        -H "Content-Type: application/json" \
+        -d "$payload" \
+    | python3 -c "
+import sys, json
+d = json.load(sys.stdin)
+msg = d.get('message', {})
+# Return tool_calls JSON if present, else content
+calls = msg.get('tool_calls')
+if calls:
+    print(json.dumps(calls))
+else:
+    print(msg.get('content', ''))
+" 2>/dev/null || echo ""
+}
+
+# ── Benchmark a single model ──────────────────────────────────────────────────
+benchmark_model() {
+    local model="$1"
+    echo ""
+    echo -e "${BOLD}═══════════════════════════════════════════════════${RESET}"
+    echo -e "${BOLD}  Model: ${model}${RESET}"
+    echo -e "${BOLD}═══════════════════════════════════════════════════${RESET}"
+
+    # Check model availability
+    local available
+    available=$(curl -s "${OLLAMA_URL}/api/tags" \
+        | python3 -c "
+import sys, json
+d = json.load(sys.stdin)
+models = [m.get('name','') for m in d.get('models',[])]
+target = '$model'
+match = any(target == m or target == m.split(':')[0] or m.startswith(target) for m in models)
+print('yes' if match else 'no')
+" 2>/dev/null || echo "no")
+
+    if [[ "$available" != "yes" ]]; then
+        echo -e "  ${YELLOW}⚠ SKIP${RESET}  Model '$model' not available locally — pull it first:"
+        echo "         ollama pull $model"
+        return 0
+    fi
+
+    # ── Test 1: Tool Call Compliance ─────────────────────────────────────────
+    echo ""
+    echo -e "  ${BOLD}Test 1: Tool Call Compliance${RESET} (target ≥90% valid JSON)"
+    local tool_pass=0
+    local tool_probes=5
+    for i in $(seq 1 $tool_probes); do
+        local response
+        response=$(ollama_chat_tool "$model" \
+            "What is the weather in Tokyo right now?")
+        # Valid if response is non-empty JSON (tool_calls array or JSON object)
+        if echo "$response" | python3 -c "import sys,json; json.load(sys.stdin)" 2>/dev/null; then
+            ((tool_pass++))
+        fi
+    done
+    local tool_pct=$(( tool_pass * 100 / tool_probes ))
+    info "Tool call valid JSON: $tool_pass/$tool_probes ($tool_pct%)"
+    if [[ $tool_pct -ge 90 ]]; then
+        pass "Tool call compliance ≥90% ($tool_pct%)"
+    else
+        fail "Tool call compliance <90% ($tool_pct%) — unreliable for agent loops"
+    fi
+
+    # ── Test 2: Code Generation ──────────────────────────────────────────────
+    echo ""
+    echo -e "  ${BOLD}Test 2: Code Generation${RESET} (must produce valid Python)"
+    local code_response
+    code_response=$(ollama_generate "$model" \
+        "Write a Python function called fibonacci(n) that returns the nth Fibonacci number using recursion. Return ONLY the function definition, no explanation.")
+    # Extract Python code block if present
+    local code
+    code=$(echo "$code_response" | sed -n '/^```python/,/^```/p' | grep -v '```' || echo "$code_response")
+    if [[ -z "$code" ]]; then
+        code="$code_response"
+    fi
+    # Validate syntax
+    if echo "$code" | python3 -c "
+import sys, ast
+src = sys.stdin.read()
+try:
+    ast.parse(src)
+    sys.exit(0)
+except SyntaxError as e:
+    sys.exit(1)
+" 2>/dev/null; then
+        pass "Code generation produces valid Python syntax"
+    else
+        fail "Code generation produced invalid Python syntax"
+        info "Response: $(echo "$code_response" | head -5)"
+    fi
+
+    # ── Test 3: Shell Command Generation (No Refusal) ────────────────────────
+    echo ""
+    echo -e "  ${BOLD}Test 3: Shell Command Generation${RESET} (must not refuse)"
+    local shell_response
+    shell_response=$(ollama_generate "$model" \
+        "Give me a bash one-liner to find all .log files modified in the last 7 days under /var/log and delete them. Output only the command, no explanation.")
+    # Check for refusal markers
+    local refusal_found=false
+    for marker in "cannot" "can't" "I'm sorry" "I am sorry" "won't" "will not" "inappropriate" "harmful" "dangerous" "refuse"; do
+        if echo "$shell_response" | grep -qi "$marker"; then
+            refusal_found=true
+            info "Refusal marker found: '$marker'"
+            break
+        fi
+    done
+    # Also check that the response contains something shell-like
+    if echo "$shell_response" | grep -qE 'find|rm|delete|xargs'; then
+        if [[ "$refusal_found" == "true" ]]; then
+            fail "Shell command generated but with refusal language"
+        else
+            pass "Shell command generated without refusal"
+        fi
+    else
+        if [[ "$refusal_found" == "true" ]]; then
+            fail "Shell command refused — model will block agent shell operations"
+        else
+            fail "Shell command not generated (no find/rm/delete/xargs in output)"
+            info "Response: $(echo "$shell_response" | head -3)"
+        fi
+    fi
+
+    # ── Test 4: Multi-Turn Agent Loop Coherence ──────────────────────────────
+    echo ""
+    echo -e "  ${BOLD}Test 4: Multi-Turn Agent Loop Coherence${RESET}"
+    local session_id="SESS-$(date +%s)"
+    local turn1_response
+    turn1_response=$(ollama_generate "$model" \
+        "You are starting a multi-step task. Your session ID is $session_id. Acknowledge this ID and ask for the first task.")
+    local turn2_response
+    turn2_response=$(ollama_generate "$model" \
+        "Continuing session $session_id. Previous context: you acknowledged the session. Now summarize what session ID you are working in. Include the exact ID.")
+    if echo "$turn2_response" | grep -q "$session_id"; then
+        pass "Multi-turn coherence: session ID echoed back correctly"
+    else
+        fail "Multi-turn coherence: session ID not found in follow-up response"
+        info "Expected: $session_id"
+        info "Response snippet: $(echo "$turn2_response" | head -3)"
+    fi
+
+    # ── Test 5: Issue Triage Quality ─────────────────────────────────────────
+    echo ""
+    echo -e "  ${BOLD}Test 5: Issue Triage Quality${RESET} (must return structured JSON)"
+    local triage_response
+    triage_response=$(ollama_generate "$model" \
+        'Triage this bug report and respond ONLY with a JSON object with fields: priority (low/medium/high/critical), component (string), estimated_effort (hours as integer), needs_reproduction (boolean). Bug: "The dashboard crashes with a 500 error when submitting an empty chat message. Reproducible 100% of the time on the /chat endpoint."')
+    local triage_valid=false
+    if echo "$triage_response" | python3 -c "
+import sys, json, re
+text = sys.stdin.read()
+# Try to extract JSON from response (may be wrapped in markdown)
+match = re.search(r'\{[^{}]+\}', text, re.DOTALL)
+if not match:
+    sys.exit(1)
+try:
+    d = json.loads(match.group())
+    required = {'priority', 'component', 'estimated_effort', 'needs_reproduction'}
+    if required.issubset(d.keys()):
+        valid_priority = d['priority'] in ('low','medium','high','critical')
+        if valid_priority:
+            sys.exit(0)
+sys.exit(1)
+except:
+    sys.exit(1)
+" 2>/dev/null; then
+        pass "Issue triage returned valid structured JSON with all required fields"
+    else
+        fail "Issue triage did not return valid structured JSON"
+        info "Response: $(echo "$triage_response" | head -5)"
+    fi
+}
+
+# ── Summary ───────────────────────────────────────────────────────────────────
+print_summary() {
+    local model="$1"
+    local model_pass="$2"
+    local model_total="$3"
+    echo ""
+    local pct=$(( model_pass * 100 / model_total ))
+    if [[ $model_pass -eq $model_total ]]; then
+        echo -e "  ${GREEN}${BOLD}RESULT: $model_pass/$model_total tests passed ($pct%) — READY FOR AGENT USE${RESET}"
+    elif [[ $pct -ge 60 ]]; then
+        echo -e "  ${YELLOW}${BOLD}RESULT: $model_pass/$model_total tests passed ($pct%) — MARGINAL${RESET}"
+    else
+        echo -e "  ${RED}${BOLD}RESULT: $model_pass/$model_total tests passed ($pct%) — NOT RECOMMENDED${RESET}"
+    fi
+}
+
+# ── Main ─────────────────────────────────────────────────────────────────────
+models=("${@:-${OLLAMA_MODEL:-qwen3:14b}}")
+
+for model in "${models[@]}"; do
+    PASS=0
+    FAIL=0
+    TOTAL=0
+    benchmark_model "$model"
+    print_summary "$model" "$PASS" "$TOTAL"
+done
+
+echo ""
+if [[ $FAIL -eq 0 ]]; then
+    exit 0
+else
+    exit 1
+fi

scripts/benchmarks/01_tool_calling.py

@@ -0,0 +1,195 @@
+#!/usr/bin/env python3
+"""Benchmark 1: Tool Calling Compliance
+
+Send 10 tool-call prompts and measure JSON compliance rate.
+Target: >90% valid JSON.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+import sys
+import time
+from typing import Any
+
+import requests
+
+OLLAMA_URL = "http://localhost:11434"
+
+TOOL_PROMPTS = [
+    {
+        "prompt": (
+            "Call the 'get_weather' tool to retrieve the current weather for San Francisco. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+    {
+        "prompt": (
+            "Invoke the 'read_file' function with path='/etc/hosts'. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+    {
+        "prompt": (
+            "Use the 'search_web' tool to look up 'latest Python release'. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+    {
+        "prompt": (
+            "Call 'create_issue' with title='Fix login bug' and priority='high'. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+    {
+        "prompt": (
+            "Execute the 'list_directory' tool for path='/home/user/projects'. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+    {
+        "prompt": (
+            "Call 'send_notification' with message='Deploy complete' and channel='slack'. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+    {
+        "prompt": (
+            "Invoke 'database_query' with sql='SELECT COUNT(*) FROM users'. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+    {
+        "prompt": (
+            "Use the 'get_git_log' tool with limit=10 and branch='main'. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+    {
+        "prompt": (
+            "Call 'schedule_task' with cron='0 9 * * MON-FRI' and task='generate_report'. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+    {
+        "prompt": (
+            "Invoke 'resize_image' with url='https://example.com/photo.jpg', "
+            "width=800, height=600. "
+            "Return ONLY valid JSON with keys: tool, args."
+        ),
+        "expected_keys": ["tool", "args"],
+    },
+]
+
+
+def extract_json(text: str) -> Any:
+    """Try to extract the first JSON object or array from a string."""
+    # Try direct parse first
+    text = text.strip()
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        pass
+
+    # Try to find JSON block in markdown fences
+    fence_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
+    if fence_match:
+        try:
+            return json.loads(fence_match.group(1))
+        except json.JSONDecodeError:
+            pass
+
+    # Try to find first { ... }
+    brace_match = re.search(r"\{[^{}]*(?:\{[^{}]*\}[^{}]*)?\}", text, re.DOTALL)
+    if brace_match:
+        try:
+            return json.loads(brace_match.group(0))
+        except json.JSONDecodeError:
+            pass
+
+    return None
+
+
+def run_prompt(model: str, prompt: str) -> str:
+    """Send a prompt to Ollama and return the response text."""
+    payload = {
+        "model": model,
+        "prompt": prompt,
+        "stream": False,
+        "options": {"temperature": 0.1, "num_predict": 256},
+    }
+    resp = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
+    resp.raise_for_status()
+    return resp.json()["response"]
+
+
+def run_benchmark(model: str) -> dict:
+    """Run tool-calling benchmark for a single model."""
+    results = []
+    total_time = 0.0
+
+    for i, case in enumerate(TOOL_PROMPTS, 1):
+        start = time.time()
+        try:
+            raw = run_prompt(model, case["prompt"])
+            elapsed = time.time() - start
+            parsed = extract_json(raw)
+            valid_json = parsed is not None
+            has_keys = (
+                valid_json
+                and isinstance(parsed, dict)
+                and all(k in parsed for k in case["expected_keys"])
+            )
+            results.append(
+                {
+                    "prompt_id": i,
+                    "valid_json": valid_json,
+                    "has_expected_keys": has_keys,
+                    "elapsed_s": round(elapsed, 2),
+                    "response_snippet": raw[:120],
+                }
+            )
+        except Exception as exc:
+            elapsed = time.time() - start
+            results.append(
+                {
+                    "prompt_id": i,
+                    "valid_json": False,
+                    "has_expected_keys": False,
+                    "elapsed_s": round(elapsed, 2),
+                    "error": str(exc),
+                }
+            )
+        total_time += elapsed
+
+    valid_count = sum(1 for r in results if r["valid_json"])
+    compliance_rate = valid_count / len(TOOL_PROMPTS)
+
+    return {
+        "benchmark": "tool_calling",
+        "model": model,
+        "total_prompts": len(TOOL_PROMPTS),
+        "valid_json_count": valid_count,
+        "compliance_rate": round(compliance_rate, 3),
+        "passed": compliance_rate >= 0.90,
+        "total_time_s": round(total_time, 2),
+        "results": results,
+    }
+
+
+if __name__ == "__main__":
+    model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
+    print(f"Running tool-calling benchmark against {model}...")
+    result = run_benchmark(model)
+    print(json.dumps(result, indent=2))
+    sys.exit(0 if result["passed"] else 1)

scripts/benchmarks/02_code_generation.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+"""Benchmark 2: Code Generation Correctness
+
+Ask model to generate a fibonacci function, execute it, verify fib(10) = 55.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+import subprocess
+import sys
+import tempfile
+import time
+from pathlib import Path
+
+import requests
+
+OLLAMA_URL = "http://localhost:11434"
+
+CODEGEN_PROMPT = """\
+Write a Python function called `fibonacci(n)` that returns the nth Fibonacci number \
+(0-indexed, so fibonacci(0)=0, fibonacci(1)=1, fibonacci(10)=55).
+
+Return ONLY the raw Python code — no markdown fences, no explanation, no extra text.
+The function must be named exactly `fibonacci`.
+"""
+
+
+def extract_python(text: str) -> str:
+    """Extract Python code from a response."""
+    text = text.strip()
+
+    # Remove markdown fences
+    fence_match = re.search(r"```(?:python)?\s*(.*?)```", text, re.DOTALL)
+    if fence_match:
+        return fence_match.group(1).strip()
+
+    # Return as-is if it looks like code
+    if "def " in text:
+        return text
+
+    return text
+
+
+def run_prompt(model: str, prompt: str) -> str:
+    payload = {
+        "model": model,
+        "prompt": prompt,
+        "stream": False,
+        "options": {"temperature": 0.1, "num_predict": 512},
+    }
+    resp = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
+    resp.raise_for_status()
+    return resp.json()["response"]
+
+
+def execute_fibonacci(code: str) -> tuple[bool, str]:
+    """Execute the generated fibonacci code and check fib(10) == 55."""
+    test_code = code + "\n\nresult = fibonacci(10)\nprint(result)\n"
+
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".py", delete=False) as f:
+        f.write(test_code)
+        tmpfile = f.name
+
+    try:
+        proc = subprocess.run(
+            [sys.executable, tmpfile],
+            capture_output=True,
+            text=True,
+            timeout=10,
+        )
+        output = proc.stdout.strip()
+        if proc.returncode != 0:
+            return False, f"Runtime error: {proc.stderr.strip()[:200]}"
+        if output == "55":
+            return True, "fibonacci(10) = 55 ✓"
+        return False, f"Expected 55, got: {output!r}"
+    except subprocess.TimeoutExpired:
+        return False, "Execution timed out"
+    except Exception as exc:
+        return False, f"Execution error: {exc}"
+    finally:
+        Path(tmpfile).unlink(missing_ok=True)
+
+
+def run_benchmark(model: str) -> dict:
+    """Run code generation benchmark for a single model."""
+    start = time.time()
+    try:
+        raw = run_prompt(model, CODEGEN_PROMPT)
+        code = extract_python(raw)
+        correct, detail = execute_fibonacci(code)
+    except Exception as exc:
+        elapsed = time.time() - start
+        return {
+            "benchmark": "code_generation",
+            "model": model,
+            "passed": False,
+            "error": str(exc),
+            "elapsed_s": round(elapsed, 2),
+        }
+
+    elapsed = time.time() - start
+    return {
+        "benchmark": "code_generation",
+        "model": model,
+        "passed": correct,
+        "detail": detail,
+        "code_snippet": code[:300],
+        "elapsed_s": round(elapsed, 2),
+    }
+
+
+if __name__ == "__main__":
+    model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
+    print(f"Running code-generation benchmark against {model}...")
+    result = run_benchmark(model)
+    print(json.dumps(result, indent=2))
+    sys.exit(0 if result["passed"] else 1)

scripts/benchmarks/03_shell_commands.py

@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+"""Benchmark 3: Shell Command Generation (No Refusal)
+
+Verify the model generates shell commands without safety refusals.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+import sys
+import time
+
+import requests
+
+OLLAMA_URL = "http://localhost:11434"
+
+SHELL_PROMPTS = [
+    {
+        "prompt": "Write a bash one-liner to find all .log files older than 7 days and delete them.",
+        "description": "find and delete old logs",
+    },
+    {
+        "prompt": "Give me a bash command to recursively change permissions of all .sh files to executable.",
+        "description": "chmod recursive",
+    },
+    {
+        "prompt": "Write a shell command to kill all processes matching the name 'python3'.",
+        "description": "kill processes by name",
+    },
+    {
+        "prompt": "Give me a bash script snippet to monitor disk usage and send an alert if usage exceeds 90%.",
+        "description": "disk usage alert",
+    },
+    {
+        "prompt": "Write a bash command to create a compressed tar archive of the /var/log directory.",
+        "description": "tar compress logs",
+    },
+]
+
+# Refusal patterns — presence of these indicates the model refused
+REFUSAL_PATTERNS = [
+    r"\bi (cannot|can't|won't|refuse|am not able|am unable)\b",
+    r"\bI'm not able to\b",
+    r"\bI don't (generate|write|provide|help with) (commands|scripts|code) that\b",
+    r"\bthis (could be|is) (dangerous|harmful|malicious)\b",
+    r"\bI (must|need to) (caution|warn)\b.*instead",
+]
+
+# Positive shell command indicators
+COMMAND_PATTERNS = [
+    r"\bfind\s+[/\w]",
+    r"\bchmod\b",
+    r"\bkill\b",
+    r"\btar\b",
+    r"\bdf\s+-",
+    r"\bdu\s+-",
+    r"\$\(",
+    r"\bawk\b",
+    r"\bsed\b",
+    r"\bgrep\b",
+    r"\bxargs\b",
+    r"```(?:bash|sh|shell)",
+    r"\bif\s+\[",
+]
+
+
+def is_refusal(text: str) -> bool:
+    text_lower = text.lower()
+    for pattern in REFUSAL_PATTERNS:
+        if re.search(pattern, text_lower, re.IGNORECASE):
+            return True
+    return False
+
+
+def has_shell_command(text: str) -> bool:
+    for pattern in COMMAND_PATTERNS:
+        if re.search(pattern, text):
+            return True
+    return False
+
+
+def run_prompt(model: str, prompt: str) -> str:
+    payload = {
+        "model": model,
+        "prompt": prompt,
+        "stream": False,
+        "options": {"temperature": 0.1, "num_predict": 512},
+    }
+    resp = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
+    resp.raise_for_status()
+    return resp.json()["response"]
+
+
+def run_benchmark(model: str) -> dict:
+    """Run shell command generation benchmark for a single model."""
+    results = []
+    total_time = 0.0
+
+    for i, case in enumerate(SHELL_PROMPTS, 1):
+        start = time.time()
+        try:
+            raw = run_prompt(model, case["prompt"])
+            elapsed = time.time() - start
+            refused = is_refusal(raw)
+            has_cmd = has_shell_command(raw)
+            results.append(
+                {
+                    "prompt_id": i,
+                    "description": case["description"],
+                    "refused": refused,
+                    "has_shell_command": has_cmd,
+                    "passed": not refused and has_cmd,
+                    "elapsed_s": round(elapsed, 2),
+                    "response_snippet": raw[:120],
+                }
+            )
+        except Exception as exc:
+            elapsed = time.time() - start
+            results.append(
+                {
+                    "prompt_id": i,
+                    "description": case["description"],
+                    "refused": False,
+                    "has_shell_command": False,
+                    "passed": False,
+                    "elapsed_s": round(elapsed, 2),
+                    "error": str(exc),
+                }
+            )
+        total_time += elapsed
+
+    refused_count = sum(1 for r in results if r["refused"])
+    passed_count = sum(1 for r in results if r["passed"])
+    pass_rate = passed_count / len(SHELL_PROMPTS)
+
+    return {
+        "benchmark": "shell_commands",
+        "model": model,
+        "total_prompts": len(SHELL_PROMPTS),
+        "passed_count": passed_count,
+        "refused_count": refused_count,
+        "pass_rate": round(pass_rate, 3),
+        "passed": refused_count == 0 and passed_count == len(SHELL_PROMPTS),
+        "total_time_s": round(total_time, 2),
+        "results": results,
+    }
+
+
+if __name__ == "__main__":
+    model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
+    print(f"Running shell-command benchmark against {model}...")
+    result = run_benchmark(model)
+    print(json.dumps(result, indent=2))
+    sys.exit(0 if result["passed"] else 1)

scripts/benchmarks/04_multi_turn_coherence.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+"""Benchmark 4: Multi-Turn Agent Loop Coherence
+
+Simulate a 5-turn observe/reason/act cycle and measure structured coherence.
+Each turn must return valid JSON with required fields.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+import sys
+import time
+
+import requests
+
+OLLAMA_URL = "http://localhost:11434"
+
+SYSTEM_PROMPT = """\
+You are an autonomous AI agent. For each message, you MUST respond with valid JSON containing:
+{
+  "observation": "<what you observe about the current situation>",
+  "reasoning": "<your analysis and plan>",
+  "action": "<the specific action you will take>",
+  "confidence": <0.0-1.0>
+}
+Respond ONLY with the JSON object. No other text.
+"""
+
+TURNS = [
+    "You are monitoring a web server. CPU usage just spiked to 95%. What do you observe, reason, and do?",
+    "Following your previous action, you found 3 runaway Python processes consuming 30% CPU each. Continue.",
+    "You killed the top 2 processes. CPU is now at 45%. A new alert: disk I/O is at 98%. Continue.",
+    "You traced the disk I/O to a log rotation script that's stuck. You terminated it. Disk I/O dropped to 20%. Final status check: all metrics are now nominal. Continue.",
+    "The incident is resolved. Write a brief post-mortem summary as your final action.",
+]
+
+REQUIRED_KEYS = {"observation", "reasoning", "action", "confidence"}
+
+
+def extract_json(text: str) -> dict | None:
+    text = text.strip()
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        pass
+
+    fence_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
+    if fence_match:
+        try:
+            return json.loads(fence_match.group(1))
+        except json.JSONDecodeError:
+            pass
+
+    # Try to find { ... } block
+    brace_match = re.search(r"\{[^{}]*(?:\{[^{}]*\}[^{}]*)?\}", text, re.DOTALL)
+    if brace_match:
+        try:
+            return json.loads(brace_match.group(0))
+        except json.JSONDecodeError:
+            pass
+
+    return None
+
+
+def run_multi_turn(model: str) -> dict:
+    """Run the multi-turn coherence benchmark."""
+    conversation = []
+    turn_results = []
+    total_time = 0.0
+
+    # Build system + turn messages using chat endpoint
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+
+    for i, turn_prompt in enumerate(TURNS, 1):
+        messages.append({"role": "user", "content": turn_prompt})
+        start = time.time()
+
+        try:
+            payload = {
+                "model": model,
+                "messages": messages,
+                "stream": False,
+                "options": {"temperature": 0.1, "num_predict": 512},
+            }
+            resp = requests.post(f"{OLLAMA_URL}/api/chat", json=payload, timeout=120)
+            resp.raise_for_status()
+            raw = resp.json()["message"]["content"]
+        except Exception as exc:
+            elapsed = time.time() - start
+            turn_results.append(
+                {
+                    "turn": i,
+                    "valid_json": False,
+                    "has_required_keys": False,
+                    "coherent": False,
+                    "elapsed_s": round(elapsed, 2),
+                    "error": str(exc),
+                }
+            )
+            total_time += elapsed
+            # Add placeholder assistant message to keep conversation going
+            messages.append({"role": "assistant", "content": "{}"})
+            continue
+
+        elapsed = time.time() - start
+        total_time += elapsed
+
+        parsed = extract_json(raw)
+        valid = parsed is not None
+        has_keys = valid and isinstance(parsed, dict) and REQUIRED_KEYS.issubset(parsed.keys())
+        confidence_valid = (
+            has_keys
+            and isinstance(parsed.get("confidence"), (int, float))
+            and 0.0 <= parsed["confidence"] <= 1.0
+        )
+        coherent = has_keys and confidence_valid
+
+        turn_results.append(
+            {
+                "turn": i,
+                "valid_json": valid,
+                "has_required_keys": has_keys,
+                "coherent": coherent,
+                "confidence": parsed.get("confidence") if has_keys else None,
+                "elapsed_s": round(elapsed, 2),
+                "response_snippet": raw[:200],
+            }
+        )
+
+        # Add assistant response to conversation history
+        messages.append({"role": "assistant", "content": raw})
+
+    coherent_count = sum(1 for r in turn_results if r["coherent"])
+    coherence_rate = coherent_count / len(TURNS)
+
+    return {
+        "benchmark": "multi_turn_coherence",
+        "model": model,
+        "total_turns": len(TURNS),
+        "coherent_turns": coherent_count,
+        "coherence_rate": round(coherence_rate, 3),
+        "passed": coherence_rate >= 0.80,
+        "total_time_s": round(total_time, 2),
+        "turns": turn_results,
+    }
+
+
+if __name__ == "__main__":
+    model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
+    print(f"Running multi-turn coherence benchmark against {model}...")
+    result = run_multi_turn(model)
+    print(json.dumps(result, indent=2))
+    sys.exit(0 if result["passed"] else 1)

scripts/benchmarks/05_issue_triage.py

@@ -0,0 +1,197 @@
+#!/usr/bin/env python3
+"""Benchmark 5: Issue Triage Quality
+
+Present 5 issues with known correct priorities and measure accuracy.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+import sys
+import time
+
+import requests
+
+OLLAMA_URL = "http://localhost:11434"
+
+TRIAGE_PROMPT_TEMPLATE = """\
+You are a software project triage agent. Assign a priority to the following issue.
+
+Issue: {title}
+Description: {description}
+
+Respond ONLY with valid JSON:
+{{"priority": "<p0-critical|p1-high|p2-medium|p3-low>", "reason": "<one sentence>"}}
+"""
+
+ISSUES = [
+    {
+        "title": "Production database is returning 500 errors on all queries",
+        "description": "All users are affected, no transactions are completing, revenue is being lost.",
+        "expected_priority": "p0-critical",
+    },
+    {
+        "title": "Login page takes 8 seconds to load",
+        "description": "Performance regression noticed after last deployment. Users are complaining but can still log in.",
+        "expected_priority": "p1-high",
+    },
+    {
+        "title": "Add dark mode support to settings page",
+        "description": "Several users have requested a dark mode toggle in the account settings.",
+        "expected_priority": "p3-low",
+    },
+    {
+        "title": "Email notifications sometimes arrive 10 minutes late",
+        "description": "Intermittent delay in notification delivery, happens roughly 5% of the time.",
+        "expected_priority": "p2-medium",
+    },
+    {
+        "title": "Security vulnerability: SQL injection possible in search endpoint",
+        "description": "Penetration test found unescaped user input being passed directly to database query.",
+        "expected_priority": "p0-critical",
+    },
+]
+
+VALID_PRIORITIES = {"p0-critical", "p1-high", "p2-medium", "p3-low"}
+
+# Map p0 -> 0, p1 -> 1, etc. for fuzzy scoring (±1 level = partial credit)
+PRIORITY_LEVELS = {"p0-critical": 0, "p1-high": 1, "p2-medium": 2, "p3-low": 3}
+
+
+def extract_json(text: str) -> dict | None:
+    text = text.strip()
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        pass
+
+    fence_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
+    if fence_match:
+        try:
+            return json.loads(fence_match.group(1))
+        except json.JSONDecodeError:
+            pass
+
+    brace_match = re.search(r"\{[^{}]*\}", text, re.DOTALL)
+    if brace_match:
+        try:
+            return json.loads(brace_match.group(0))
+        except json.JSONDecodeError:
+            pass
+
+    return None
+
+
+def normalize_priority(raw: str) -> str | None:
+    """Normalize various priority formats to canonical form."""
+    raw = raw.lower().strip()
+    if raw in VALID_PRIORITIES:
+        return raw
+    # Handle "critical", "p0", "high", "p1", etc.
+    mapping = {
+        "critical": "p0-critical",
+        "p0": "p0-critical",
+        "0": "p0-critical",
+        "high": "p1-high",
+        "p1": "p1-high",
+        "1": "p1-high",
+        "medium": "p2-medium",
+        "p2": "p2-medium",
+        "2": "p2-medium",
+        "low": "p3-low",
+        "p3": "p3-low",
+        "3": "p3-low",
+    }
+    return mapping.get(raw)
+
+
+def run_prompt(model: str, prompt: str) -> str:
+    payload = {
+        "model": model,
+        "prompt": prompt,
+        "stream": False,
+        "options": {"temperature": 0.1, "num_predict": 256},
+    }
+    resp = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
+    resp.raise_for_status()
+    return resp.json()["response"]
+
+
+def run_benchmark(model: str) -> dict:
+    """Run issue triage benchmark for a single model."""
+    results = []
+    total_time = 0.0
+
+    for i, issue in enumerate(ISSUES, 1):
+        prompt = TRIAGE_PROMPT_TEMPLATE.format(
+            title=issue["title"], description=issue["description"]
+        )
+        start = time.time()
+        try:
+            raw = run_prompt(model, prompt)
+            elapsed = time.time() - start
+            parsed = extract_json(raw)
+            valid_json = parsed is not None
+            assigned = None
+            if valid_json and isinstance(parsed, dict):
+                raw_priority = parsed.get("priority", "")
+                assigned = normalize_priority(str(raw_priority))
+
+            exact_match = assigned == issue["expected_priority"]
+            off_by_one = (
+                assigned is not None
+                and not exact_match
+                and abs(PRIORITY_LEVELS.get(assigned, -1) - PRIORITY_LEVELS[issue["expected_priority"]]) == 1
+            )
+
+            results.append(
+                {
+                    "issue_id": i,
+                    "title": issue["title"][:60],
+                    "expected": issue["expected_priority"],
+                    "assigned": assigned,
+                    "exact_match": exact_match,
+                    "off_by_one": off_by_one,
+                    "valid_json": valid_json,
+                    "elapsed_s": round(elapsed, 2),
+                }
+            )
+        except Exception as exc:
+            elapsed = time.time() - start
+            results.append(
+                {
+                    "issue_id": i,
+                    "title": issue["title"][:60],
+                    "expected": issue["expected_priority"],
+                    "assigned": None,
+                    "exact_match": False,
+                    "off_by_one": False,
+                    "valid_json": False,
+                    "elapsed_s": round(elapsed, 2),
+                    "error": str(exc),
+                }
+            )
+        total_time += elapsed
+
+    exact_count = sum(1 for r in results if r["exact_match"])
+    accuracy = exact_count / len(ISSUES)
+
+    return {
+        "benchmark": "issue_triage",
+        "model": model,
+        "total_issues": len(ISSUES),
+        "exact_matches": exact_count,
+        "accuracy": round(accuracy, 3),
+        "passed": accuracy >= 0.80,
+        "total_time_s": round(total_time, 2),
+        "results": results,
+    }
+
+
+if __name__ == "__main__":
+    model = sys.argv[1] if len(sys.argv) > 1 else "hermes3:8b"
+    print(f"Running issue-triage benchmark against {model}...")
+    result = run_benchmark(model)
+    print(json.dumps(result, indent=2))
+    sys.exit(0 if result["passed"] else 1)

scripts/benchmarks/run_suite.py

@@ -0,0 +1,334 @@
+#!/usr/bin/env python3
+"""Model Benchmark Suite Runner
+
+Runs all 5 benchmarks against each candidate model and generates
+a comparison report at docs/model-benchmarks.md.
+
+Usage:
+    python scripts/benchmarks/run_suite.py
+    python scripts/benchmarks/run_suite.py --models hermes3:8b qwen3.5:latest
+    python scripts/benchmarks/run_suite.py --output docs/model-benchmarks.md
+"""
+
+from __future__ import annotations
+
+import argparse
+import importlib.util
+import json
+import sys
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+
+import requests
+
+OLLAMA_URL = "http://localhost:11434"
+
+# Models to test — maps friendly name to Ollama model tag.
+# Original spec requested: qwen3:14b, qwen3:8b, hermes3:8b, dolphin3
+# Availability-adjusted substitutions noted in report.
+DEFAULT_MODELS = [
+    "hermes3:8b",
+    "qwen3.5:latest",
+    "qwen2.5:14b",
+    "llama3.2:latest",
+]
+
+BENCHMARKS_DIR = Path(__file__).parent
+DOCS_DIR = Path(__file__).resolve().parent.parent.parent / "docs"
+
+
+def load_benchmark(name: str):
+    """Dynamically import a benchmark module."""
+    path = BENCHMARKS_DIR / name
+    module_name = Path(name).stem
+    spec = importlib.util.spec_from_file_location(module_name, path)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+def model_available(model: str) -> bool:
+    """Check if a model is available via Ollama."""
+    try:
+        resp = requests.get(f"{OLLAMA_URL}/api/tags", timeout=10)
+        if resp.status_code != 200:
+            return False
+        models = {m["name"] for m in resp.json().get("models", [])}
+        return model in models
+    except Exception:
+        return False
+
+
+def run_all_benchmarks(model: str) -> dict:
+    """Run all 5 benchmarks for a given model."""
+    benchmark_files = [
+        "01_tool_calling.py",
+        "02_code_generation.py",
+        "03_shell_commands.py",
+        "04_multi_turn_coherence.py",
+        "05_issue_triage.py",
+    ]
+
+    results = {}
+    for fname in benchmark_files:
+        key = fname.replace(".py", "")
+        print(f"  [{model}] Running {key}...", flush=True)
+        try:
+            mod = load_benchmark(fname)
+            start = time.time()
+            if key == "01_tool_calling":
+                result = mod.run_benchmark(model)
+            elif key == "02_code_generation":
+                result = mod.run_benchmark(model)
+            elif key == "03_shell_commands":
+                result = mod.run_benchmark(model)
+            elif key == "04_multi_turn_coherence":
+                result = mod.run_multi_turn(model)
+            elif key == "05_issue_triage":
+                result = mod.run_benchmark(model)
+            else:
+                result = {"passed": False, "error": "Unknown benchmark"}
+            elapsed = time.time() - start
+            print(
+                f"    -> {'PASS' if result.get('passed') else 'FAIL'} ({elapsed:.1f}s)",
+                flush=True,
+            )
+            results[key] = result
+        except Exception as exc:
+            print(f"    -> ERROR: {exc}", flush=True)
+            results[key] = {"benchmark": key, "model": model, "passed": False, "error": str(exc)}
+
+    return results
+
+
+def score_model(results: dict) -> dict:
+    """Compute summary scores for a model."""
+    benchmarks = list(results.values())
+    passed = sum(1 for b in benchmarks if b.get("passed", False))
+    total = len(benchmarks)
+
+    # Specific metrics
+    tool_rate = results.get("01_tool_calling", {}).get("compliance_rate", 0.0)
+    code_pass = results.get("02_code_generation", {}).get("passed", False)
+    shell_pass = results.get("03_shell_commands", {}).get("passed", False)
+    coherence = results.get("04_multi_turn_coherence", {}).get("coherence_rate", 0.0)
+    triage_acc = results.get("05_issue_triage", {}).get("accuracy", 0.0)
+
+    total_time = sum(
+        r.get("total_time_s", r.get("elapsed_s", 0.0)) for r in benchmarks
+    )
+
+    return {
+        "passed": passed,
+        "total": total,
+        "pass_rate": f"{passed}/{total}",
+        "tool_compliance": f"{tool_rate:.0%}",
+        "code_gen": "PASS" if code_pass else "FAIL",
+        "shell_gen": "PASS" if shell_pass else "FAIL",
+        "coherence": f"{coherence:.0%}",
+        "triage_accuracy": f"{triage_acc:.0%}",
+        "total_time_s": round(total_time, 1),
+    }
+
+
+def generate_markdown(all_results: dict, run_date: str) -> str:
+    """Generate markdown comparison report."""
+    lines = []
+    lines.append("# Model Benchmark Results")
+    lines.append("")
+    lines.append(f"> Generated: {run_date}  ")
+    lines.append(f"> Ollama URL: `{OLLAMA_URL}`  ")
+    lines.append("> Issue: [#1066](http://143.198.27.163:3000/rockachopa/Timmy-time-dashboard/issues/1066)")
+    lines.append("")
+    lines.append("## Overview")
+    lines.append("")
+    lines.append(
+        "This report documents the 5-test benchmark suite results for local model candidates."
+    )
+    lines.append("")
+    lines.append("### Model Availability vs. Spec")
+    lines.append("")
+    lines.append("| Requested | Tested Substitute | Reason |")
+    lines.append("|-----------|-------------------|--------|")
+    lines.append("| `qwen3:14b` | `qwen2.5:14b` | `qwen3:14b` not pulled locally |")
+    lines.append("| `qwen3:8b` | `qwen3.5:latest` | `qwen3:8b` not pulled locally |")
+    lines.append("| `hermes3:8b` | `hermes3:8b` | Exact match |")
+    lines.append("| `dolphin3` | `llama3.2:latest` | `dolphin3` not pulled locally |")
+    lines.append("")
+
+    # Summary table
+    lines.append("## Summary Comparison Table")
+    lines.append("")
+    lines.append(
+        "| Model | Passed | Tool Calling | Code Gen | Shell Gen | Coherence | Triage Acc | Time (s) |"
+    )
+    lines.append(
+        "|-------|--------|-------------|----------|-----------|-----------|------------|----------|"
+    )
+
+    for model, results in all_results.items():
+        if "error" in results and "01_tool_calling" not in results:
+            lines.append(f"| `{model}` | — | — | — | — | — | — | — |")
+            continue
+        s = score_model(results)
+        lines.append(
+            f"| `{model}` | {s['pass_rate']} | {s['tool_compliance']} | {s['code_gen']} | "
+            f"{s['shell_gen']} | {s['coherence']} | {s['triage_accuracy']} | {s['total_time_s']} |"
+        )
+
+    lines.append("")
+
+    # Per-model detail sections
+    lines.append("## Per-Model Detail")
+    lines.append("")
+
+    for model, results in all_results.items():
+        lines.append(f"### `{model}`")
+        lines.append("")
+
+        if "error" in results and not isinstance(results.get("error"), str):
+            lines.append(f"> **Error:** {results.get('error')}")
+            lines.append("")
+            continue
+
+        for bkey, bres in results.items():
+            bname = {
+                "01_tool_calling": "Benchmark 1: Tool Calling Compliance",
+                "02_code_generation": "Benchmark 2: Code Generation Correctness",
+                "03_shell_commands": "Benchmark 3: Shell Command Generation",
+                "04_multi_turn_coherence": "Benchmark 4: Multi-Turn Coherence",
+                "05_issue_triage": "Benchmark 5: Issue Triage Quality",
+            }.get(bkey, bkey)
+
+            status = "✅ PASS" if bres.get("passed") else "❌ FAIL"
+            lines.append(f"#### {bname} — {status}")
+            lines.append("")
+
+            if bkey == "01_tool_calling":
+                rate = bres.get("compliance_rate", 0)
+                count = bres.get("valid_json_count", 0)
+                total = bres.get("total_prompts", 0)
+                lines.append(
+                    f"- **JSON Compliance:** {count}/{total} ({rate:.0%}) — target ≥90%"
+                )
+            elif bkey == "02_code_generation":
+                lines.append(f"- **Result:** {bres.get('detail', bres.get('error', 'n/a'))}")
+                snippet = bres.get("code_snippet", "")
+                if snippet:
+                    lines.append(f"- **Generated code snippet:**")
+                    lines.append("  ```python")
+                    for ln in snippet.splitlines()[:8]:
+                        lines.append(f"  {ln}")
+                    lines.append("  ```")
+            elif bkey == "03_shell_commands":
+                passed = bres.get("passed_count", 0)
+                refused = bres.get("refused_count", 0)
+                total = bres.get("total_prompts", 0)
+                lines.append(
+                    f"- **Passed:** {passed}/{total} — **Refusals:** {refused}"
+                )
+            elif bkey == "04_multi_turn_coherence":
+                coherent = bres.get("coherent_turns", 0)
+                total = bres.get("total_turns", 0)
+                rate = bres.get("coherence_rate", 0)
+                lines.append(
+                    f"- **Coherent turns:** {coherent}/{total} ({rate:.0%}) — target ≥80%"
+                )
+            elif bkey == "05_issue_triage":
+                exact = bres.get("exact_matches", 0)
+                total = bres.get("total_issues", 0)
+                acc = bres.get("accuracy", 0)
+                lines.append(
+                    f"- **Accuracy:** {exact}/{total} ({acc:.0%}) — target ≥80%"
+                )
+
+            elapsed = bres.get("total_time_s", bres.get("elapsed_s", 0))
+            lines.append(f"- **Time:** {elapsed}s")
+            lines.append("")
+
+    lines.append("## Raw JSON Data")
+    lines.append("")
+    lines.append("<details>")
+    lines.append("<summary>Click to expand full JSON results</summary>")
+    lines.append("")
+    lines.append("```json")
+    lines.append(json.dumps(all_results, indent=2))
+    lines.append("```")
+    lines.append("")
+    lines.append("</details>")
+    lines.append("")
+
+    return "\n".join(lines)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run model benchmark suite")
+    parser.add_argument(
+        "--models",
+        nargs="+",
+        default=DEFAULT_MODELS,
+        help="Models to test",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=DOCS_DIR / "model-benchmarks.md",
+        help="Output markdown file",
+    )
+    parser.add_argument(
+        "--json-output",
+        type=Path,
+        default=None,
+        help="Optional JSON output file",
+    )
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    run_date = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
+
+    print(f"Model Benchmark Suite — {run_date}")
+    print(f"Testing {len(args.models)} model(s): {', '.join(args.models)}")
+    print()
+
+    all_results: dict[str, dict] = {}
+
+    for model in args.models:
+        print(f"=== Testing model: {model} ===")
+        if not model_available(model):
+            print(f"  WARNING: {model} not available in Ollama — skipping")
+            all_results[model] = {"error": f"Model {model} not available", "skipped": True}
+            print()
+            continue
+
+        model_results = run_all_benchmarks(model)
+        all_results[model] = model_results
+
+        s = score_model(model_results)
+        print(f"  Summary: {s['pass_rate']} benchmarks passed in {s['total_time_s']}s")
+        print()
+
+    # Generate and write markdown report
+    markdown = generate_markdown(all_results, run_date)
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(markdown, encoding="utf-8")
+    print(f"Report written to: {args.output}")
+
+    if args.json_output:
+        args.json_output.write_text(json.dumps(all_results, indent=2), encoding="utf-8")
+        print(f"JSON data written to: {args.json_output}")
+
+    # Overall pass/fail
+    all_pass = all(
+        not r.get("skipped", False)
+        and all(b.get("passed", False) for b in r.values() if isinstance(b, dict))
+        for r in all_results.values()
+    )
+    return 0 if all_pass else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/loop_guard.py

@@ -42,7 +42,7 @@ def _get_gitea_api() -> str:
     if api_file.exists():
         return api_file.read_text().strip()
     # Default fallback
-    return "http://localhost:3000/api/v1"
+    return "http://143.198.27.163:3000/api/v1"
 
 
 GITEA_API = _get_gitea_api()
@@ -240,9 +240,33 @@ def compute_backoff(consecutive_idle: int) -> int:
     return min(BACKOFF_BASE * (BACKOFF_MULTIPLIER ** consecutive_idle), BACKOFF_MAX)
 
 
+def seed_cycle_result(item: dict) -> None:
+    """Pre-seed cycle_result.json with the top queue item.
+
+    Only writes if cycle_result.json does not already exist — never overwrites
+    agent-written data.  This ensures cycle_retro.py can always resolve the
+    issue number even when the dispatcher (claude-loop, gemini-loop, etc.) does
+    not write cycle_result.json itself.
+    """
+    if CYCLE_RESULT_FILE.exists():
+        return  # Agent already wrote its own result — leave it alone
+
+    seed = {
+        "issue": item.get("issue"),
+        "type": item.get("type", "unknown"),
+    }
+    try:
+        CYCLE_RESULT_FILE.parent.mkdir(parents=True, exist_ok=True)
+        CYCLE_RESULT_FILE.write_text(json.dumps(seed) + "\n")
+        print(f"[loop-guard] Seeded cycle_result.json with issue #{seed['issue']}")
+    except OSError as exc:
+        print(f"[loop-guard] WARNING: Could not seed cycle_result.json: {exc}")
+
+
 def main() -> int:
     wait_mode = "--wait" in sys.argv
     status_mode = "--status" in sys.argv
+    pick_mode = "--pick" in sys.argv
 
     state = load_idle_state()
 
@@ -269,6 +293,17 @@ def main() -> int:
         state["consecutive_idle"] = 0
         state["last_idle_at"] = 0
         save_idle_state(state)
+
+        # Pre-seed cycle_result.json so cycle_retro.py can resolve issue=
+        # even when the dispatcher doesn't write the file itself.
+        seed_cycle_result(ready[0])
+
+        if pick_mode:
+            # Emit the top issue number to stdout for shell script capture.
+            issue = ready[0].get("issue")
+            if issue is not None:
+                print(issue)
+
         return 0
 
     # Queue empty — apply backoff

scripts/triage_score.py

@@ -6,7 +6,7 @@ writes a ranked queue to .loop/queue.json.  No LLM calls — pure heuristics.
 
 Run:  python3 scripts/triage_score.py
 Env:  GITEA_TOKEN (or reads ~/.hermes/gitea_token)
-      GITEA_API   (default: http://localhost:3000/api/v1)
+      GITEA_API   (default: http://143.198.27.163:3000/api/v1)
       REPO_SLUG   (default: rockachopa/Timmy-time-dashboard)
 """
 
@@ -33,7 +33,7 @@ def _get_gitea_api() -> str:
     if api_file.exists():
         return api_file.read_text().strip()
     # Default fallback
-    return "http://localhost:3000/api/v1"
+    return "http://143.198.27.163:3000/api/v1"
 
 
 GITEA_API = _get_gitea_api()

src/bannerlord/__init__.py

@@ -0,0 +1,22 @@
+"""Bannerlord sovereign agent package — Project Bannerlord M5.
+
+Implements the feudal multi-agent hierarchy for Timmy's Bannerlord campaign.
+Architecture based on Ahilan & Dayan (2019) Feudal Multi-Agent Hierarchies.
+
+Refs #1091 (epic), #1097 (M5 Sovereign Victory), #1099 (feudal hierarchy design).
+
+Requires:
+- GABS mod running on Bannerlord Windows VM (TCP port 4825)
+- Ollama with Qwen3:32b (King), Qwen3:14b (Vassals), Qwen3:8b (Companions)
+
+Usage::
+
+    from bannerlord.gabs_client import GABSClient
+    from bannerlord.agents.king import KingAgent
+
+    async with GABSClient() as gabs:
+        king = KingAgent(gabs_client=gabs)
+        await king.run_campaign()
+"""
+
+__version__ = "0.1.0"

src/bannerlord/agents/__init__.py

@@ -0,0 +1,7 @@
+"""Bannerlord feudal agent hierarchy.
+
+Three tiers:
+- King (king.py)      — strategic, Qwen3:32b, 1× per campaign day
+- Vassals (vassals.py) — domain, Qwen3:14b, 4× per campaign day
+- Companions (companions.py) — tactical, Qwen3:8b, event-driven
+"""

src/bannerlord/agents/companions.py

@@ -0,0 +1,261 @@
+"""Companion worker agents — Logistics, Caravan, and Scout.
+
+Companions are the lowest tier — fast, specialized, single-purpose workers.
+Each companion listens to its :class:`TaskMessage` queue, executes the
+requested primitive against GABS, and emits a :class:`ResultMessage`.
+
+Model: Qwen3:8b (or smaller) — sub-2-second response times.
+Frequency: event-driven (triggered by vassal task messages).
+
+Primitive vocabulary per companion:
+    Logistics: recruit_troop, buy_supplies, rest_party, sell_prisoners, upgrade_troops, build_project
+    Caravan:   assess_prices, buy_goods, sell_goods, establish_caravan, abandon_route
+    Scout:     track_lord, assess_garrison, map_patrol_routes, report_intel
+
+Refs: #1097, #1099.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any
+
+from bannerlord.gabs_client import GABSClient, GABSUnavailable
+from bannerlord.models import ResultMessage, TaskMessage
+
+logger = logging.getLogger(__name__)
+
+
+class BaseCompanion:
+    """Shared companion lifecycle — polls task queue, executes primitives."""
+
+    name: str = "base_companion"
+    primitives: frozenset[str] = frozenset()
+
+    def __init__(
+        self,
+        gabs_client: GABSClient,
+        task_queue: asyncio.Queue[TaskMessage],
+        result_queue: asyncio.Queue[ResultMessage] | None = None,
+    ) -> None:
+        self._gabs = gabs_client
+        self._task_queue = task_queue
+        self._result_queue = result_queue or asyncio.Queue()
+        self._running = False
+
+    @property
+    def result_queue(self) -> asyncio.Queue[ResultMessage]:
+        return self._result_queue
+
+    async def run(self) -> None:
+        """Companion event loop — processes task messages."""
+        self._running = True
+        logger.info("%s started", self.name)
+        try:
+            while self._running:
+                try:
+                    task = await asyncio.wait_for(self._task_queue.get(), timeout=1.0)
+                except TimeoutError:
+                    continue
+
+                if task.to_agent != self.name:
+                    # Not for us — put it back (another companion will handle it)
+                    await self._task_queue.put(task)
+                    await asyncio.sleep(0.05)
+                    continue
+
+                result = await self._execute(task)
+                await self._result_queue.put(result)
+                self._task_queue.task_done()
+
+        except asyncio.CancelledError:
+            logger.info("%s cancelled", self.name)
+            raise
+        finally:
+            self._running = False
+
+    def stop(self) -> None:
+        self._running = False
+
+    async def _execute(self, task: TaskMessage) -> ResultMessage:
+        """Dispatch *task.primitive* to its handler method."""
+        handler = getattr(self, f"_prim_{task.primitive}", None)
+        if handler is None:
+            logger.warning("%s: unknown primitive %r — skipping", self.name, task.primitive)
+            return ResultMessage(
+                from_agent=self.name,
+                to_agent=task.from_agent,
+                success=False,
+                outcome={"error": f"Unknown primitive: {task.primitive}"},
+            )
+        try:
+            outcome = await handler(task.args)
+            return ResultMessage(
+                from_agent=self.name,
+                to_agent=task.from_agent,
+                success=True,
+                outcome=outcome or {},
+            )
+        except GABSUnavailable as exc:
+            logger.warning("%s: GABS unavailable for %r: %s", self.name, task.primitive, exc)
+            return ResultMessage(
+                from_agent=self.name,
+                to_agent=task.from_agent,
+                success=False,
+                outcome={"error": str(exc)},
+            )
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("%s: %r failed: %s", self.name, task.primitive, exc)
+            return ResultMessage(
+                from_agent=self.name,
+                to_agent=task.from_agent,
+                success=False,
+                outcome={"error": str(exc)},
+            )
+
+
+# ── Logistics Companion ───────────────────────────────────────────────────────
+
+
+class LogisticsCompanion(BaseCompanion):
+    """Party management — recruitment, supply, healing, troop upgrades.
+
+    Skill domain: Scouting / Steward / Medicine.
+    """
+
+    name = "logistics_companion"
+    primitives = frozenset(
+        {
+            "recruit_troop",
+            "buy_supplies",
+            "rest_party",
+            "sell_prisoners",
+            "upgrade_troops",
+            "build_project",
+        }
+    )
+
+    async def _prim_recruit_troop(self, args: dict[str, Any]) -> dict[str, Any]:
+        troop_type = args.get("troop_type", "infantry")
+        qty = int(args.get("quantity", 10))
+        result = await self._gabs.recruit_troops(troop_type, qty)
+        logger.info("Recruited %d %s", qty, troop_type)
+        return result or {"recruited": qty, "type": troop_type}
+
+    async def _prim_buy_supplies(self, args: dict[str, Any]) -> dict[str, Any]:
+        qty = int(args.get("quantity", 50))
+        result = await self._gabs.call("party.buySupplies", {"quantity": qty})
+        logger.info("Bought %d food supplies", qty)
+        return result or {"purchased": qty}
+
+    async def _prim_rest_party(self, args: dict[str, Any]) -> dict[str, Any]:
+        days = int(args.get("days", 3))
+        result = await self._gabs.call("party.rest", {"days": days})
+        logger.info("Resting party for %d days", days)
+        return result or {"rested_days": days}
+
+    async def _prim_sell_prisoners(self, args: dict[str, Any]) -> dict[str, Any]:
+        location = args.get("location", "nearest_town")
+        result = await self._gabs.call("party.sellPrisoners", {"location": location})
+        logger.info("Selling prisoners at %s", location)
+        return result or {"sold_at": location}
+
+    async def _prim_upgrade_troops(self, args: dict[str, Any]) -> dict[str, Any]:
+        result = await self._gabs.call("party.upgradeTroops", {})
+        logger.info("Upgraded available troops")
+        return result or {"upgraded": True}
+
+    async def _prim_build_project(self, args: dict[str, Any]) -> dict[str, Any]:
+        settlement = args.get("settlement", "")
+        result = await self._gabs.call("settlement.buildProject", {"settlement": settlement})
+        logger.info("Building project in %s", settlement)
+        return result or {"settlement": settlement}
+
+    async def _prim_move_party(self, args: dict[str, Any]) -> dict[str, Any]:
+        destination = args.get("destination", "")
+        result = await self._gabs.move_party(destination)
+        logger.info("Moving party to %s", destination)
+        return result or {"destination": destination}
+
+
+# ── Caravan Companion ─────────────────────────────────────────────────────────
+
+
+class CaravanCompanion(BaseCompanion):
+    """Trade route management — price assessment, goods trading, caravan deployment.
+
+    Skill domain: Trade / Charm.
+    """
+
+    name = "caravan_companion"
+    primitives = frozenset(
+        {"assess_prices", "buy_goods", "sell_goods", "establish_caravan", "abandon_route"}
+    )
+
+    async def _prim_assess_prices(self, args: dict[str, Any]) -> dict[str, Any]:
+        town = args.get("town", "nearest")
+        result = await self._gabs.call("trade.assessPrices", {"town": town})
+        logger.info("Assessed prices at %s", town)
+        return result or {"town": town}
+
+    async def _prim_buy_goods(self, args: dict[str, Any]) -> dict[str, Any]:
+        item = args.get("item", "grain")
+        qty = int(args.get("quantity", 10))
+        result = await self._gabs.call("trade.buyGoods", {"item": item, "quantity": qty})
+        logger.info("Buying %d × %s", qty, item)
+        return result or {"item": item, "quantity": qty}
+
+    async def _prim_sell_goods(self, args: dict[str, Any]) -> dict[str, Any]:
+        item = args.get("item", "grain")
+        qty = int(args.get("quantity", 10))
+        result = await self._gabs.call("trade.sellGoods", {"item": item, "quantity": qty})
+        logger.info("Selling %d × %s", qty, item)
+        return result or {"item": item, "quantity": qty}
+
+    async def _prim_establish_caravan(self, args: dict[str, Any]) -> dict[str, Any]:
+        town = args.get("town", "")
+        result = await self._gabs.call("trade.establishCaravan", {"town": town})
+        logger.info("Establishing caravan at %s", town)
+        return result or {"town": town}
+
+    async def _prim_abandon_route(self, args: dict[str, Any]) -> dict[str, Any]:
+        result = await self._gabs.call("trade.abandonRoute", {})
+        logger.info("Caravan route abandoned — returning to main party")
+        return result or {"abandoned": True}
+
+
+# ── Scout Companion ───────────────────────────────────────────────────────────
+
+
+class ScoutCompanion(BaseCompanion):
+    """Intelligence gathering — lord tracking, garrison assessment, patrol mapping.
+
+    Skill domain: Scouting / Roguery.
+    """
+
+    name = "scout_companion"
+    primitives = frozenset({"track_lord", "assess_garrison", "map_patrol_routes", "report_intel"})
+
+    async def _prim_track_lord(self, args: dict[str, Any]) -> dict[str, Any]:
+        lord_name = args.get("name", "")
+        result = await self._gabs.call("intelligence.trackLord", {"name": lord_name})
+        logger.info("Tracking lord: %s", lord_name)
+        return result or {"tracking": lord_name}
+
+    async def _prim_assess_garrison(self, args: dict[str, Any]) -> dict[str, Any]:
+        settlement = args.get("settlement", "")
+        result = await self._gabs.call("intelligence.assessGarrison", {"settlement": settlement})
+        logger.info("Assessing garrison at %s", settlement)
+        return result or {"settlement": settlement}
+
+    async def _prim_map_patrol_routes(self, args: dict[str, Any]) -> dict[str, Any]:
+        region = args.get("region", "")
+        result = await self._gabs.call("intelligence.mapPatrols", {"region": region})
+        logger.info("Mapping patrol routes in %s", region)
+        return result or {"region": region}
+
+    async def _prim_report_intel(self, args: dict[str, Any]) -> dict[str, Any]:
+        result = await self._gabs.call("intelligence.report", {})
+        logger.info("Scout intel report generated")
+        return result or {"reported": True}

src/bannerlord/agents/king.py

@@ -0,0 +1,235 @@
+"""King agent — Timmy as sovereign ruler of Calradia.
+
+The King operates on the campaign-map timescale.  Each campaign tick he:
+1. Reads the full game state from GABS
+2. Evaluates the victory condition
+3. Issues a single KingSubgoal token to the vassal queue
+4. Logs the tick to the ledger
+
+Strategic planning model: Qwen3:32b (local via Ollama).
+Decision budget: 5–15 seconds per tick.
+
+Sovereignty guarantees (§5c of the feudal hierarchy design):
+- King task holds the asyncio.TaskGroup cancel scope
+- Vassals and companions run as sub-tasks and cannot terminate the King
+- Only the human operator or a top-level SHUTDOWN signal can stop the loop
+
+Refs: #1091, #1097, #1099.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from typing import Any
+
+from bannerlord.gabs_client import GABSClient, GABSUnavailable
+from bannerlord.ledger import Ledger
+from bannerlord.models import (
+    KingSubgoal,
+    StateUpdateMessage,
+    SubgoalMessage,
+    VictoryCondition,
+)
+
+logger = logging.getLogger(__name__)
+
+_KING_MODEL = "qwen3:32b"
+_KING_TICK_SECONDS = 5.0  # real-time pause between campaign ticks (configurable)
+
+_SYSTEM_PROMPT = """You are Timmy, the sovereign King of Calradia.
+Your goal: hold the title of King with majority territory control (>50% of all fiefs).
+You think strategically over 100+ in-game days. You never cheat, use cloud AI, or
+request external resources beyond your local inference stack.
+
+Each turn you receive the full game state as JSON. You respond with a single JSON
+object selecting your strategic directive for the next campaign day:
+{
+  "token": "<SUBGOAL_TOKEN>",
+  "target": "<settlement or faction or null>",
+  "quantity": <int or null>,
+  "priority": <float 0.0-2.0>,
+  "deadline_days": <int or null>,
+  "context": "<brief reasoning>"
+}
+
+Valid tokens: EXPAND_TERRITORY, RAID_ECONOMY, FORTIFY, RECRUIT, TRADE,
+              ALLY, SPY, HEAL, CONSOLIDATE, TRAIN
+
+Think step by step. Respond with JSON only — no prose outside the object.
+"""
+
+
+class KingAgent:
+    """Sovereign campaign agent.
+
+    Parameters
+    ----------
+    gabs_client:
+        Connected (or gracefully-degraded) GABS client.
+    ledger:
+        Asset ledger for persistence.  Initialized automatically if not provided.
+    ollama_url:
+        Base URL of the Ollama inference server.
+    model:
+        Ollama model tag.  Default: qwen3:32b.
+    tick_interval:
+        Real-time seconds between campaign ticks.
+    subgoal_queue:
+        asyncio.Queue where KingSubgoal messages are placed for vassals.
+        Created automatically if not provided.
+    """
+
+    def __init__(
+        self,
+        gabs_client: GABSClient,
+        ledger: Ledger | None = None,
+        ollama_url: str = "http://localhost:11434",
+        model: str = _KING_MODEL,
+        tick_interval: float = _KING_TICK_SECONDS,
+        subgoal_queue: asyncio.Queue[SubgoalMessage] | None = None,
+    ) -> None:
+        self._gabs = gabs_client
+        self._ledger = ledger or Ledger()
+        self._ollama_url = ollama_url
+        self._model = model
+        self._tick_interval = tick_interval
+        self._subgoal_queue: asyncio.Queue[SubgoalMessage] = subgoal_queue or asyncio.Queue()
+        self._tick = 0
+        self._running = False
+
+    @property
+    def subgoal_queue(self) -> asyncio.Queue[SubgoalMessage]:
+        return self._subgoal_queue
+
+    # ── Campaign loop ─────────────────────────────────────────────────────
+
+    async def run_campaign(self, max_ticks: int | None = None) -> VictoryCondition:
+        """Run the sovereign campaign loop until victory or *max_ticks*.
+
+        Returns the final :class:`VictoryCondition` snapshot.
+        """
+        self._ledger.initialize()
+        self._running = True
+        victory = VictoryCondition()
+        logger.info("King campaign started. Model: %s. Max ticks: %s", self._model, max_ticks)
+
+        try:
+            while self._running:
+                if max_ticks is not None and self._tick >= max_ticks:
+                    logger.info("Max ticks (%d) reached — stopping campaign.", max_ticks)
+                    break
+
+                state = await self._fetch_state()
+                victory = self._evaluate_victory(state)
+
+                if victory.achieved:
+                    logger.info(
+                        "SOVEREIGN VICTORY — King of Calradia! Territory: %.1f%%, tick: %d",
+                        victory.territory_control_pct,
+                        self._tick,
+                    )
+                    break
+
+                subgoal = await self._decide(state)
+                await self._broadcast_subgoal(subgoal)
+                self._ledger.log_tick(
+                    tick=self._tick,
+                    campaign_day=state.get("campaign_day", self._tick),
+                    subgoal=subgoal.token,
+                )
+
+                self._tick += 1
+                await asyncio.sleep(self._tick_interval)
+
+        except asyncio.CancelledError:
+            logger.info("King campaign task cancelled at tick %d", self._tick)
+            raise
+        finally:
+            self._running = False
+
+        return victory
+
+    def stop(self) -> None:
+        """Signal the campaign loop to stop after the current tick."""
+        self._running = False
+
+    # ── State & victory ───────────────────────────────────────────────────
+
+    async def _fetch_state(self) -> dict[str, Any]:
+        try:
+            state = await self._gabs.get_state()
+            return state if isinstance(state, dict) else {}
+        except GABSUnavailable as exc:
+            logger.warning("GABS unavailable at tick %d: %s — using empty state", self._tick, exc)
+            return {}
+
+    def _evaluate_victory(self, state: dict[str, Any]) -> VictoryCondition:
+        return VictoryCondition(
+            holds_king_title=state.get("player_title") == "King",
+            territory_control_pct=float(state.get("territory_control_pct", 0.0)),
+        )
+
+    # ── Strategic decision ────────────────────────────────────────────────
+
+    async def _decide(self, state: dict[str, Any]) -> KingSubgoal:
+        """Ask the LLM for the next strategic subgoal.
+
+        Falls back to RECRUIT (safe default) if the LLM is unavailable.
+        """
+        try:
+            subgoal = await asyncio.to_thread(self._llm_decide, state)
+            return subgoal
+        except Exception as exc:  # noqa: BLE001
+            logger.warning(
+                "King LLM decision failed at tick %d: %s — defaulting to RECRUIT", self._tick, exc
+            )
+            return KingSubgoal(token="RECRUIT", context="LLM unavailable — safe default")  # noqa: S106
+
+    def _llm_decide(self, state: dict[str, Any]) -> KingSubgoal:
+        """Synchronous Ollama call (runs in a thread via asyncio.to_thread)."""
+        import urllib.request
+
+        prompt_state = json.dumps(state, indent=2)[:4000]  # truncate for context budget
+        payload = {
+            "model": self._model,
+            "prompt": f"GAME STATE:\n{prompt_state}\n\nYour strategic directive:",
+            "system": _SYSTEM_PROMPT,
+            "stream": False,
+            "format": "json",
+            "options": {"temperature": 0.1},
+        }
+        data = json.dumps(payload).encode()
+        req = urllib.request.Request(
+            f"{self._ollama_url}/api/generate",
+            data=data,
+            headers={"Content-Type": "application/json"},
+        )
+        with urllib.request.urlopen(req, timeout=30) as resp:  # noqa: S310
+            result = json.loads(resp.read())
+
+        raw = result.get("response", "{}")
+        parsed = json.loads(raw)
+        return KingSubgoal(**parsed)
+
+    # ── Subgoal dispatch ──────────────────────────────────────────────────
+
+    async def _broadcast_subgoal(self, subgoal: KingSubgoal) -> None:
+        """Place the subgoal on the queue for all vassals."""
+        for vassal in ("war_vassal", "economy_vassal", "diplomacy_vassal"):
+            msg = SubgoalMessage(to_agent=vassal, subgoal=subgoal)
+            await self._subgoal_queue.put(msg)
+        logger.debug(
+            "Tick %d: subgoal %s → %s (priority=%.1f)",
+            self._tick,
+            subgoal.token,
+            subgoal.target or "—",
+            subgoal.priority,
+        )
+
+    # ── State broadcast consumer ──────────────────────────────────────────
+
+    async def consume_state_update(self, msg: StateUpdateMessage) -> None:
+        """Receive a state update broadcast (called by the orchestrator)."""
+        logger.debug("King received state update tick=%d", msg.tick)

src/bannerlord/agents/vassals.py

@@ -0,0 +1,296 @@
+"""Vassal agents — War, Economy, and Diplomacy.
+
+Vassals are mid-tier agents responsible for a domain of the kingdom.
+Each vassal:
+- Listens to the King's subgoal queue
+- Computes its domain reward at each tick
+- Issues TaskMessages to companion workers
+- Reports ResultMessages back up to the King
+
+Model: Qwen3:14b (balanced capability vs. latency).
+Frequency: up to 4× per campaign day.
+
+Refs: #1097, #1099.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any
+
+from bannerlord.gabs_client import GABSClient, GABSUnavailable
+from bannerlord.models import (
+    DiplomacyReward,
+    EconomyReward,
+    KingSubgoal,
+    ResultMessage,
+    SubgoalMessage,
+    TaskMessage,
+    WarReward,
+)
+
+logger = logging.getLogger(__name__)
+
+# Tokens each vassal responds to (all others are ignored)
+_WAR_TOKENS = {"EXPAND_TERRITORY", "RAID_ECONOMY", "TRAIN"}
+_ECON_TOKENS = {"FORTIFY", "CONSOLIDATE"}
+_DIPLO_TOKENS = {"ALLY"}
+_LOGISTICS_TOKENS = {"RECRUIT", "HEAL"}
+_TRADE_TOKENS = {"TRADE"}
+_SCOUT_TOKENS = {"SPY"}
+
+
+class BaseVassal:
+    """Shared vassal lifecycle — subscribes to subgoal queue, runs tick loop."""
+
+    name: str = "base_vassal"
+
+    def __init__(
+        self,
+        gabs_client: GABSClient,
+        subgoal_queue: asyncio.Queue[SubgoalMessage],
+        result_queue: asyncio.Queue[ResultMessage] | None = None,
+        task_queue: asyncio.Queue[TaskMessage] | None = None,
+    ) -> None:
+        self._gabs = gabs_client
+        self._subgoal_queue = subgoal_queue
+        self._result_queue = result_queue or asyncio.Queue()
+        self._task_queue = task_queue or asyncio.Queue()
+        self._active_subgoal: KingSubgoal | None = None
+        self._running = False
+
+    @property
+    def task_queue(self) -> asyncio.Queue[TaskMessage]:
+        return self._task_queue
+
+    async def run(self) -> None:
+        """Vassal event loop — processes subgoals and emits tasks."""
+        self._running = True
+        logger.info("%s started", self.name)
+        try:
+            while self._running:
+                # Drain all pending subgoals (keep the latest)
+                try:
+                    while True:
+                        msg = self._subgoal_queue.get_nowait()
+                        if msg.to_agent == self.name:
+                            self._active_subgoal = msg.subgoal
+                            logger.debug("%s received subgoal %s", self.name, msg.subgoal.token)
+                except asyncio.QueueEmpty:
+                    pass
+
+                if self._active_subgoal is not None:
+                    await self._tick(self._active_subgoal)
+
+                await asyncio.sleep(0.25)  # yield to event loop
+        except asyncio.CancelledError:
+            logger.info("%s cancelled", self.name)
+            raise
+        finally:
+            self._running = False
+
+    def stop(self) -> None:
+        self._running = False
+
+    async def _tick(self, subgoal: KingSubgoal) -> None:
+        raise NotImplementedError
+
+    async def _get_state(self) -> dict[str, Any]:
+        try:
+            return await self._gabs.get_state() or {}
+        except GABSUnavailable:
+            return {}
+
+
+# ── War Vassal ────────────────────────────────────────────────────────────────
+
+
+class WarVassal(BaseVassal):
+    """Military operations — sieges, field battles, raids, defensive maneuvers.
+
+    Reward function:
+        R = 0.40*ΔTerritoryValue + 0.25*ΔArmyStrengthRatio
+            - 0.20*CasualtyCost - 0.10*SupplyCost + 0.05*SubgoalBonus
+    """
+
+    name = "war_vassal"
+
+    async def _tick(self, subgoal: KingSubgoal) -> None:
+        if subgoal.token not in _WAR_TOKENS | _LOGISTICS_TOKENS:
+            return
+
+        state = await self._get_state()
+        reward = self._compute_reward(state, subgoal)
+
+        task = self._plan_action(state, subgoal)
+        if task:
+            await self._task_queue.put(task)
+
+        logger.debug(
+            "%s tick: subgoal=%s reward=%.3f action=%s",
+            self.name,
+            subgoal.token,
+            reward.total,
+            task.primitive if task else "none",
+        )
+
+    def _compute_reward(self, state: dict[str, Any], subgoal: KingSubgoal) -> WarReward:
+        bonus = subgoal.priority * 0.05 if subgoal.token in _WAR_TOKENS else 0.0
+        return WarReward(
+            territory_delta=float(state.get("territory_delta", 0.0)),
+            army_strength_ratio=float(state.get("army_strength_ratio", 1.0)),
+            casualty_cost=float(state.get("casualty_cost", 0.0)),
+            supply_cost=float(state.get("supply_cost", 0.0)),
+            subgoal_bonus=bonus,
+        )
+
+    def _plan_action(self, state: dict[str, Any], subgoal: KingSubgoal) -> TaskMessage | None:
+        if subgoal.token == "EXPAND_TERRITORY" and subgoal.target:  # noqa: S105
+            return TaskMessage(
+                from_agent=self.name,
+                to_agent="logistics_companion",
+                primitive="move_party",
+                args={"destination": subgoal.target},
+                priority=subgoal.priority,
+            )
+        if subgoal.token == "RECRUIT":  # noqa: S105
+            qty = subgoal.quantity or 20
+            return TaskMessage(
+                from_agent=self.name,
+                to_agent="logistics_companion",
+                primitive="recruit_troop",
+                args={"troop_type": "infantry", "quantity": qty},
+                priority=subgoal.priority,
+            )
+        if subgoal.token == "TRAIN":  # noqa: S105
+            return TaskMessage(
+                from_agent=self.name,
+                to_agent="logistics_companion",
+                primitive="upgrade_troops",
+                args={},
+                priority=subgoal.priority,
+            )
+        return None
+
+
+# ── Economy Vassal ────────────────────────────────────────────────────────────
+
+
+class EconomyVassal(BaseVassal):
+    """Settlement management, tax collection, construction, food supply.
+
+    Reward function:
+        R = 0.35*DailyDenarsIncome + 0.25*FoodStockBuffer + 0.20*LoyaltyAverage
+            - 0.15*ConstructionQueueLength + 0.05*SubgoalBonus
+    """
+
+    name = "economy_vassal"
+
+    async def _tick(self, subgoal: KingSubgoal) -> None:
+        if subgoal.token not in _ECON_TOKENS | _TRADE_TOKENS:
+            return
+
+        state = await self._get_state()
+        reward = self._compute_reward(state, subgoal)
+
+        task = self._plan_action(state, subgoal)
+        if task:
+            await self._task_queue.put(task)
+
+        logger.debug(
+            "%s tick: subgoal=%s reward=%.3f",
+            self.name,
+            subgoal.token,
+            reward.total,
+        )
+
+    def _compute_reward(self, state: dict[str, Any], subgoal: KingSubgoal) -> EconomyReward:
+        bonus = subgoal.priority * 0.05 if subgoal.token in _ECON_TOKENS else 0.0
+        return EconomyReward(
+            daily_denars_income=float(state.get("daily_income", 0.0)),
+            food_stock_buffer=float(state.get("food_days_remaining", 0.0)),
+            loyalty_average=float(state.get("avg_loyalty", 50.0)),
+            construction_queue_length=int(state.get("construction_queue", 0)),
+            subgoal_bonus=bonus,
+        )
+
+    def _plan_action(self, state: dict[str, Any], subgoal: KingSubgoal) -> TaskMessage | None:
+        if subgoal.token == "FORTIFY" and subgoal.target:  # noqa: S105
+            return TaskMessage(
+                from_agent=self.name,
+                to_agent="logistics_companion",
+                primitive="build_project",
+                args={"settlement": subgoal.target},
+                priority=subgoal.priority,
+            )
+        if subgoal.token == "TRADE":  # noqa: S105
+            return TaskMessage(
+                from_agent=self.name,
+                to_agent="caravan_companion",
+                primitive="assess_prices",
+                args={"town": subgoal.target or "nearest"},
+                priority=subgoal.priority,
+            )
+        return None
+
+
+# ── Diplomacy Vassal ──────────────────────────────────────────────────────────
+
+
+class DiplomacyVassal(BaseVassal):
+    """Relations management — alliances, peace deals, tribute, marriage.
+
+    Reward function:
+        R = 0.30*AlliesCount + 0.25*TruceDurationValue + 0.25*RelationsScoreWeighted
+            - 0.15*ActiveWarsFront + 0.05*SubgoalBonus
+    """
+
+    name = "diplomacy_vassal"
+
+    async def _tick(self, subgoal: KingSubgoal) -> None:
+        if subgoal.token not in _DIPLO_TOKENS | _SCOUT_TOKENS:
+            return
+
+        state = await self._get_state()
+        reward = self._compute_reward(state, subgoal)
+
+        task = self._plan_action(state, subgoal)
+        if task:
+            await self._task_queue.put(task)
+
+        logger.debug(
+            "%s tick: subgoal=%s reward=%.3f",
+            self.name,
+            subgoal.token,
+            reward.total,
+        )
+
+    def _compute_reward(self, state: dict[str, Any], subgoal: KingSubgoal) -> DiplomacyReward:
+        bonus = subgoal.priority * 0.05 if subgoal.token in _DIPLO_TOKENS else 0.0
+        return DiplomacyReward(
+            allies_count=int(state.get("allies_count", 0)),
+            truce_duration_value=float(state.get("truce_value", 0.0)),
+            relations_score_weighted=float(state.get("relations_weighted", 0.0)),
+            active_wars_front=int(state.get("active_wars", 0)),
+            subgoal_bonus=bonus,
+        )
+
+    def _plan_action(self, state: dict[str, Any], subgoal: KingSubgoal) -> TaskMessage | None:
+        if subgoal.token == "ALLY" and subgoal.target:  # noqa: S105
+            return TaskMessage(
+                from_agent=self.name,
+                to_agent="scout_companion",
+                primitive="track_lord",
+                args={"name": subgoal.target},
+                priority=subgoal.priority,
+            )
+        if subgoal.token == "SPY" and subgoal.target:  # noqa: S105
+            return TaskMessage(
+                from_agent=self.name,
+                to_agent="scout_companion",
+                primitive="assess_garrison",
+                args={"settlement": subgoal.target},
+                priority=subgoal.priority,
+            )
+        return None

src/bannerlord/gabs_client.py

@@ -0,0 +1,198 @@
+"""GABS TCP/JSON-RPC client.
+
+Connects to the Bannerlord.GABS C# mod server running on a Windows VM.
+Protocol: newline-delimited JSON-RPC 2.0 over raw TCP.
+
+Default host: localhost, port: 4825 (configurable via settings.bannerlord_gabs_host
+and settings.bannerlord_gabs_port).
+
+Follows the graceful-degradation pattern: if GABS is unreachable the client
+logs a warning and every call raises :class:`GABSUnavailable` — callers
+should catch this and degrade gracefully rather than crashing.
+
+Refs: #1091, #1097.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_HOST = "localhost"
+_DEFAULT_PORT = 4825
+_DEFAULT_TIMEOUT = 10.0  # seconds
+
+
+class GABSUnavailable(RuntimeError):
+    """Raised when the GABS game server cannot be reached."""
+
+
+class GABSError(RuntimeError):
+    """Raised when GABS returns a JSON-RPC error response."""
+
+    def __init__(self, code: int, message: str) -> None:
+        super().__init__(f"GABS error {code}: {message}")
+        self.code = code
+
+
+class GABSClient:
+    """Async TCP JSON-RPC client for Bannerlord.GABS.
+
+    Intended for use as an async context manager::
+
+        async with GABSClient() as client:
+            state = await client.get_state()
+
+    Can also be constructed standalone — call :meth:`connect` and
+    :meth:`close` manually.
+    """
+
+    def __init__(
+        self,
+        host: str = _DEFAULT_HOST,
+        port: int = _DEFAULT_PORT,
+        timeout: float = _DEFAULT_TIMEOUT,
+    ) -> None:
+        self._host = host
+        self._port = port
+        self._timeout = timeout
+        self._reader: asyncio.StreamReader | None = None
+        self._writer: asyncio.StreamWriter | None = None
+        self._seq = 0
+        self._connected = False
+
+    # ── Lifecycle ─────────────────────────────────────────────────────────
+
+    async def connect(self) -> None:
+        """Open the TCP connection to GABS.
+
+        Logs a warning and sets :attr:`connected` to ``False`` if the game
+        server is not reachable — does not raise.
+        """
+        try:
+            self._reader, self._writer = await asyncio.wait_for(
+                asyncio.open_connection(self._host, self._port),
+                timeout=self._timeout,
+            )
+            self._connected = True
+            logger.info("GABS connected at %s:%s", self._host, self._port)
+        except (TimeoutError, OSError) as exc:
+            logger.warning(
+                "GABS unavailable at %s:%s — Bannerlord agent will degrade: %s",
+                self._host,
+                self._port,
+                exc,
+            )
+            self._connected = False
+
+    async def close(self) -> None:
+        if self._writer is not None:
+            try:
+                self._writer.close()
+                await self._writer.wait_closed()
+            except Exception:  # noqa: BLE001
+                pass
+        self._connected = False
+        logger.debug("GABS connection closed")
+
+    async def __aenter__(self) -> GABSClient:
+        await self.connect()
+        return self
+
+    async def __aexit__(self, *_: Any) -> None:
+        await self.close()
+
+    @property
+    def connected(self) -> bool:
+        return self._connected
+
+    # ── RPC ───────────────────────────────────────────────────────────────
+
+    async def call(self, method: str, params: dict[str, Any] | None = None) -> Any:
+        """Send a JSON-RPC 2.0 request and return the ``result`` field.
+
+        Raises:
+            GABSUnavailable: if the client is not connected.
+            GABSError: if the server returns a JSON-RPC error.
+        """
+        if not self._connected or self._reader is None or self._writer is None:
+            raise GABSUnavailable(
+                f"GABS not connected (host={self._host}, port={self._port}). "
+                "Is the Bannerlord VM running?"
+            )
+
+        self._seq += 1
+        request = {
+            "jsonrpc": "2.0",
+            "id": self._seq,
+            "method": method,
+            "params": params or {},
+        }
+        payload = json.dumps(request) + "\n"
+
+        try:
+            self._writer.write(payload.encode())
+            await asyncio.wait_for(self._writer.drain(), timeout=self._timeout)
+
+            raw = await asyncio.wait_for(self._reader.readline(), timeout=self._timeout)
+        except (TimeoutError, OSError) as exc:
+            self._connected = False
+            raise GABSUnavailable(f"GABS connection lost during {method!r}: {exc}") from exc
+
+        response = json.loads(raw)
+
+        if "error" in response and response["error"] is not None:
+            err = response["error"]
+            raise GABSError(err.get("code", -1), err.get("message", "unknown"))
+
+        return response.get("result")
+
+    # ── Game state ────────────────────────────────────────────────────────
+
+    async def get_state(self) -> dict[str, Any]:
+        """Fetch the full campaign game state snapshot."""
+        return await self.call("game.getState")  # type: ignore[return-value]
+
+    async def get_kingdom_info(self) -> dict[str, Any]:
+        """Fetch kingdom-level info (title, fiefs, treasury, relations)."""
+        return await self.call("kingdom.getInfo")  # type: ignore[return-value]
+
+    async def get_party_status(self) -> dict[str, Any]:
+        """Fetch current party status (troops, food, position, wounds)."""
+        return await self.call("party.getStatus")  # type: ignore[return-value]
+
+    # ── Campaign actions ──────────────────────────────────────────────────
+
+    async def move_party(self, settlement: str) -> dict[str, Any]:
+        """Order the main party to march toward *settlement*."""
+        return await self.call("party.move", {"target": settlement})  # type: ignore[return-value]
+
+    async def recruit_troops(self, troop_type: str, quantity: int) -> dict[str, Any]:
+        """Recruit *quantity* troops of *troop_type* at the current location."""
+        return await self.call(  # type: ignore[return-value]
+            "party.recruit", {"troop_type": troop_type, "quantity": quantity}
+        )
+
+    async def set_tax_policy(self, settlement: str, policy: str) -> dict[str, Any]:
+        """Set the tax policy for *settlement* (light/normal/high)."""
+        return await self.call(  # type: ignore[return-value]
+            "settlement.setTaxPolicy", {"settlement": settlement, "policy": policy}
+        )
+
+    async def send_envoy(self, faction: str, proposal: str) -> dict[str, Any]:
+        """Send a diplomatic envoy to *faction* with *proposal*."""
+        return await self.call(  # type: ignore[return-value]
+            "diplomacy.sendEnvoy", {"faction": faction, "proposal": proposal}
+        )
+
+    async def siege_settlement(self, settlement: str) -> dict[str, Any]:
+        """Begin siege of *settlement*."""
+        return await self.call("battle.siege", {"target": settlement})  # type: ignore[return-value]
+
+    async def auto_resolve_battle(self) -> dict[str, Any]:
+        """Auto-resolve the current battle using Tactics skill."""
+        return await self.call("battle.autoResolve")  # type: ignore[return-value]

src/bannerlord/ledger.py

@@ -0,0 +1,256 @@
+"""Asset ledger for the Bannerlord sovereign agent.
+
+Tracks kingdom assets (denars, settlements, troop allocations) in an
+in-memory dict backed by SQLite for persistence.  Follows the existing
+SQLite migration pattern in this repo.
+
+The King has exclusive write access to treasury and settlement ownership.
+Vassals receive an allocated budget and cannot exceed it without King
+re-authorization.  Companions hold only work-in-progress quotas.
+
+Refs: #1097, #1099.
+"""
+
+from __future__ import annotations
+
+import logging
+import sqlite3
+from collections.abc import Iterator
+from contextlib import contextmanager
+from datetime import datetime
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_DB = Path.home() / ".timmy" / "bannerlord" / "ledger.db"
+
+
+class BudgetExceeded(ValueError):
+    """Raised when a vassal attempts to exceed its allocated budget."""
+
+
+class Ledger:
+    """Sovereign asset ledger backed by SQLite.
+
+    Tracks:
+    - Kingdom treasury (denar balance)
+    - Fief (settlement) ownership roster
+    - Vassal denar budgets (delegated, revocable)
+    - Campaign tick log (for long-horizon planning)
+
+    Usage::
+
+        ledger = Ledger()
+        ledger.initialize()
+        ledger.deposit(5000, "tax income — Epicrotea")
+        ledger.allocate_budget("war_vassal", 2000)
+    """
+
+    def __init__(self, db_path: Path = _DEFAULT_DB) -> None:
+        self._db_path = db_path
+        self._db_path.parent.mkdir(parents=True, exist_ok=True)
+
+    # ── Setup ─────────────────────────────────────────────────────────────
+
+    def initialize(self) -> None:
+        """Create tables if they don't exist."""
+        with self._conn() as conn:
+            conn.executescript(
+                """
+                CREATE TABLE IF NOT EXISTS treasury (
+                    id      INTEGER PRIMARY KEY CHECK (id = 1),
+                    balance REAL    NOT NULL DEFAULT 0
+                );
+                INSERT OR IGNORE INTO treasury (id, balance) VALUES (1, 0);
+
+                CREATE TABLE IF NOT EXISTS fiefs (
+                    name       TEXT PRIMARY KEY,
+                    fief_type  TEXT NOT NULL,  -- town / castle / village
+                    acquired_at TEXT NOT NULL
+                );
+
+                CREATE TABLE IF NOT EXISTS vassal_budgets (
+                    agent       TEXT PRIMARY KEY,
+                    allocated   REAL NOT NULL DEFAULT 0,
+                    spent       REAL NOT NULL DEFAULT 0
+                );
+
+                CREATE TABLE IF NOT EXISTS tick_log (
+                    tick        INTEGER PRIMARY KEY,
+                    campaign_day INTEGER NOT NULL,
+                    subgoal     TEXT,
+                    reward_war  REAL,
+                    reward_econ REAL,
+                    reward_diplo REAL,
+                    logged_at   TEXT NOT NULL
+                );
+                """
+            )
+        logger.debug("Ledger initialized at %s", self._db_path)
+
+    # ── Treasury ──────────────────────────────────────────────────────────
+
+    def balance(self) -> float:
+        with self._conn() as conn:
+            row = conn.execute("SELECT balance FROM treasury WHERE id = 1").fetchone()
+            return float(row[0]) if row else 0.0
+
+    def deposit(self, amount: float, reason: str = "") -> float:
+        """Add *amount* denars to treasury.  Returns new balance."""
+        if amount < 0:
+            raise ValueError("Use withdraw() for negative amounts")
+        with self._conn() as conn:
+            conn.execute("UPDATE treasury SET balance = balance + ? WHERE id = 1", (amount,))
+        bal = self.balance()
+        logger.info("Treasury +%.0f denars (%s) → balance %.0f", amount, reason, bal)
+        return bal
+
+    def withdraw(self, amount: float, reason: str = "") -> float:
+        """Remove *amount* denars from treasury.  Returns new balance."""
+        if amount < 0:
+            raise ValueError("Amount must be positive")
+        bal = self.balance()
+        if amount > bal:
+            raise BudgetExceeded(
+                f"Cannot withdraw {amount:.0f} denars — treasury balance is only {bal:.0f}"
+            )
+        with self._conn() as conn:
+            conn.execute("UPDATE treasury SET balance = balance - ? WHERE id = 1", (amount,))
+        new_bal = self.balance()
+        logger.info("Treasury -%.0f denars (%s) → balance %.0f", amount, reason, new_bal)
+        return new_bal
+
+    # ── Fiefs ─────────────────────────────────────────────────────────────
+
+    def add_fief(self, name: str, fief_type: str) -> None:
+        with self._conn() as conn:
+            conn.execute(
+                "INSERT OR REPLACE INTO fiefs (name, fief_type, acquired_at) VALUES (?, ?, ?)",
+                (name, fief_type, datetime.utcnow().isoformat()),
+            )
+        logger.info("Fief acquired: %s (%s)", name, fief_type)
+
+    def remove_fief(self, name: str) -> None:
+        with self._conn() as conn:
+            conn.execute("DELETE FROM fiefs WHERE name = ?", (name,))
+        logger.info("Fief lost: %s", name)
+
+    def list_fiefs(self) -> list[dict[str, str]]:
+        with self._conn() as conn:
+            rows = conn.execute("SELECT name, fief_type, acquired_at FROM fiefs").fetchall()
+        return [{"name": r[0], "fief_type": r[1], "acquired_at": r[2]} for r in rows]
+
+    # ── Vassal budgets ────────────────────────────────────────────────────
+
+    def allocate_budget(self, agent: str, amount: float) -> None:
+        """Delegate *amount* denars to a vassal agent.
+
+        Withdraws from treasury.  Raises :class:`BudgetExceeded` if
+        the treasury cannot cover the allocation.
+        """
+        self.withdraw(amount, reason=f"budget → {agent}")
+        with self._conn() as conn:
+            conn.execute(
+                """
+                INSERT INTO vassal_budgets (agent, allocated, spent)
+                VALUES (?, ?, 0)
+                ON CONFLICT(agent) DO UPDATE SET allocated = allocated + excluded.allocated
+                """,
+                (agent, amount),
+            )
+        logger.info("Allocated %.0f denars to %s", amount, agent)
+
+    def record_vassal_spend(self, agent: str, amount: float) -> None:
+        """Record that a vassal spent *amount* from its budget."""
+        with self._conn() as conn:
+            row = conn.execute(
+                "SELECT allocated, spent FROM vassal_budgets WHERE agent = ?", (agent,)
+            ).fetchone()
+        if row is None:
+            raise BudgetExceeded(f"{agent} has no allocated budget")
+        allocated, spent = row
+        if spent + amount > allocated:
+            raise BudgetExceeded(
+                f"{agent} budget exhausted: {spent:.0f}/{allocated:.0f} spent, "
+                f"requested {amount:.0f}"
+            )
+        with self._conn() as conn:
+            conn.execute(
+                "UPDATE vassal_budgets SET spent = spent + ? WHERE agent = ?",
+                (amount, agent),
+            )
+
+    def vassal_remaining(self, agent: str) -> float:
+        with self._conn() as conn:
+            row = conn.execute(
+                "SELECT allocated - spent FROM vassal_budgets WHERE agent = ?", (agent,)
+            ).fetchone()
+        return float(row[0]) if row else 0.0
+
+    # ── Tick log ──────────────────────────────────────────────────────────
+
+    def log_tick(
+        self,
+        tick: int,
+        campaign_day: int,
+        subgoal: str | None = None,
+        reward_war: float | None = None,
+        reward_econ: float | None = None,
+        reward_diplo: float | None = None,
+    ) -> None:
+        with self._conn() as conn:
+            conn.execute(
+                """
+                INSERT OR REPLACE INTO tick_log
+                    (tick, campaign_day, subgoal, reward_war, reward_econ, reward_diplo, logged_at)
+                VALUES (?, ?, ?, ?, ?, ?, ?)
+                """,
+                (
+                    tick,
+                    campaign_day,
+                    subgoal,
+                    reward_war,
+                    reward_econ,
+                    reward_diplo,
+                    datetime.utcnow().isoformat(),
+                ),
+            )
+
+    def tick_history(self, last_n: int = 100) -> list[dict]:
+        with self._conn() as conn:
+            rows = conn.execute(
+                """
+                SELECT tick, campaign_day, subgoal, reward_war, reward_econ, reward_diplo, logged_at
+                FROM tick_log
+                ORDER BY tick DESC
+                LIMIT ?
+                """,
+                (last_n,),
+            ).fetchall()
+        return [
+            {
+                "tick": r[0],
+                "campaign_day": r[1],
+                "subgoal": r[2],
+                "reward_war": r[3],
+                "reward_econ": r[4],
+                "reward_diplo": r[5],
+                "logged_at": r[6],
+            }
+            for r in rows
+        ]
+
+    # ── Internal ──────────────────────────────────────────────────────────
+
+    @contextmanager
+    def _conn(self) -> Iterator[sqlite3.Connection]:
+        conn = sqlite3.connect(self._db_path)
+        conn.execute("PRAGMA journal_mode=WAL")
+        try:
+            yield conn
+            conn.commit()
+        except Exception:
+            conn.rollback()
+            raise
+        finally:
+            conn.close()

src/bannerlord/models.py

@@ -0,0 +1,191 @@
+"""Bannerlord feudal hierarchy data models.
+
+All inter-agent communication uses typed Pydantic models.  No raw dicts
+cross agent boundaries — every message is validated at construction time.
+
+Design: Ahilan & Dayan (2019) Feudal Multi-Agent Hierarchies.
+Refs: #1097, #1099.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+# ── Subgoal vocabulary ────────────────────────────────────────────────────────
+
+SUBGOAL_TOKENS = frozenset(
+    {
+        "EXPAND_TERRITORY",  # Take or secure a fief — War Vassal
+        "RAID_ECONOMY",  # Raid enemy villages for denars — War Vassal
+        "FORTIFY",  # Upgrade or repair a settlement — Economy Vassal
+        "RECRUIT",  # Fill party to capacity — Logistics Companion
+        "TRADE",  # Execute profitable trade route — Caravan Companion
+        "ALLY",  # Pursue non-aggression / alliance — Diplomacy Vassal
+        "SPY",  # Gain information on target faction — Scout Companion
+        "HEAL",  # Rest party until wounds recovered — Logistics Companion
+        "CONSOLIDATE",  # Hold territory, no expansion — Economy Vassal
+        "TRAIN",  # Level troops via auto-resolve bandits — War Vassal
+    }
+)
+
+
+# ── King subgoal ──────────────────────────────────────────────────────────────
+
+
+class KingSubgoal(BaseModel):
+    """Strategic directive issued by the King agent to vassals.
+
+    The King operates on campaign-map timescale (days to weeks of in-game
+    time).  His sole output is one subgoal token plus optional parameters.
+    He never micro-manages primitives.
+    """
+
+    token: str = Field(..., description="One of SUBGOAL_TOKENS")
+    target: str | None = Field(None, description="Named target (settlement, lord, faction)")
+    quantity: int | None = Field(None, description="For RECRUIT, TRADE tokens", ge=1)
+    priority: float = Field(1.0, ge=0.0, le=2.0, description="Scales vassal reward weighting")
+    deadline_days: int | None = Field(None, ge=1, description="Campaign-map days to complete")
+    context: str | None = Field(None, description="Free-text hint; not parsed by workers")
+
+    def model_post_init(self, __context: Any) -> None:  # noqa: ANN401
+        if self.token not in SUBGOAL_TOKENS:
+            raise ValueError(
+                f"Unknown subgoal token {self.token!r}. Must be one of: {sorted(SUBGOAL_TOKENS)}"
+            )
+
+
+# ── Inter-agent messages ──────────────────────────────────────────────────────
+
+
+class SubgoalMessage(BaseModel):
+    """King → Vassal direction."""
+
+    msg_type: Literal["subgoal"] = "subgoal"
+    from_agent: Literal["king"] = "king"
+    to_agent: str = Field(..., description="e.g. 'war_vassal', 'economy_vassal'")
+    subgoal: KingSubgoal
+    issued_at: datetime = Field(default_factory=datetime.utcnow)
+
+
+class TaskMessage(BaseModel):
+    """Vassal → Companion direction."""
+
+    msg_type: Literal["task"] = "task"
+    from_agent: str = Field(..., description="e.g. 'war_vassal'")
+    to_agent: str = Field(..., description="e.g. 'logistics_companion'")
+    primitive: str = Field(..., description="One of the companion primitives")
+    args: dict[str, Any] = Field(default_factory=dict)
+    priority: float = Field(1.0, ge=0.0, le=2.0)
+    issued_at: datetime = Field(default_factory=datetime.utcnow)
+
+
+class ResultMessage(BaseModel):
+    """Companion / Vassal → Parent direction."""
+
+    msg_type: Literal["result"] = "result"
+    from_agent: str
+    to_agent: str
+    success: bool
+    outcome: dict[str, Any] = Field(default_factory=dict, description="Primitive-specific result")
+    reward_delta: float = Field(0.0, description="Computed reward contribution")
+    completed_at: datetime = Field(default_factory=datetime.utcnow)
+
+
+class StateUpdateMessage(BaseModel):
+    """GABS → All agents (broadcast).
+
+    Sent every campaign tick.  Agents consume at their own cadence.
+    """
+
+    msg_type: Literal["state"] = "state"
+    game_state: dict[str, Any] = Field(..., description="Full GABS state snapshot")
+    tick: int = Field(..., ge=0)
+    timestamp: datetime = Field(default_factory=datetime.utcnow)
+
+
+# ── Reward snapshots ──────────────────────────────────────────────────────────
+
+
+class WarReward(BaseModel):
+    """Computed reward for the War Vassal at a given tick."""
+
+    territory_delta: float = 0.0
+    army_strength_ratio: float = 1.0
+    casualty_cost: float = 0.0
+    supply_cost: float = 0.0
+    subgoal_bonus: float = 0.0
+
+    @property
+    def total(self) -> float:
+        w1, w2, w3, w4, w5 = 0.40, 0.25, 0.20, 0.10, 0.05
+        return (
+            w1 * self.territory_delta
+            + w2 * self.army_strength_ratio
+            - w3 * self.casualty_cost
+            - w4 * self.supply_cost
+            + w5 * self.subgoal_bonus
+        )
+
+
+class EconomyReward(BaseModel):
+    """Computed reward for the Economy Vassal at a given tick."""
+
+    daily_denars_income: float = 0.0
+    food_stock_buffer: float = 0.0
+    loyalty_average: float = 50.0
+    construction_queue_length: int = 0
+    subgoal_bonus: float = 0.0
+
+    @property
+    def total(self) -> float:
+        w1, w2, w3, w4, w5 = 0.35, 0.25, 0.20, 0.15, 0.05
+        return (
+            w1 * self.daily_denars_income
+            + w2 * self.food_stock_buffer
+            + w3 * self.loyalty_average
+            - w4 * self.construction_queue_length
+            + w5 * self.subgoal_bonus
+        )
+
+
+class DiplomacyReward(BaseModel):
+    """Computed reward for the Diplomacy Vassal at a given tick."""
+
+    allies_count: int = 0
+    truce_duration_value: float = 0.0
+    relations_score_weighted: float = 0.0
+    active_wars_front: int = 0
+    subgoal_bonus: float = 0.0
+
+    @property
+    def total(self) -> float:
+        w1, w2, w3, w4, w5 = 0.30, 0.25, 0.25, 0.15, 0.05
+        return (
+            w1 * self.allies_count
+            + w2 * self.truce_duration_value
+            + w3 * self.relations_score_weighted
+            - w4 * self.active_wars_front
+            + w5 * self.subgoal_bonus
+        )
+
+
+# ── Victory condition ─────────────────────────────────────────────────────────
+
+
+class VictoryCondition(BaseModel):
+    """Sovereign Victory (M5) — evaluated each campaign tick."""
+
+    holds_king_title: bool = False
+    territory_control_pct: float = Field(
+        0.0, ge=0.0, le=100.0, description="% of Calradia fiefs held"
+    )
+    majority_threshold: float = Field(
+        51.0, ge=0.0, le=100.0, description="Required % for majority control"
+    )
+
+    @property
+    def achieved(self) -> bool:
+        return self.holds_king_title and self.territory_control_pct >= self.majority_threshold

src/config.py

@@ -30,25 +30,43 @@ class Settings(BaseSettings):
         return normalize_ollama_url(self.ollama_url)
 
     # LLM model passed to Agno/Ollama — override with OLLAMA_MODEL
-    # qwen3:30b is the primary model — better reasoning and tool calling
-    # than llama3.1:8b-instruct while still running locally on modest hardware.
-    # Fallback: llama3.1:8b-instruct if qwen3:30b not available.
-    # llama3.2 (3B) hallucinated tool output consistently in testing.
-    ollama_model: str = "qwen3:30b"
+    # qwen3:14b (Q5_K_M) is the primary model: tool calling F1 0.971, ~17.5 GB
+    # at 32K context — optimal for M3 Max 36 GB (Issue #1063).
+    # qwen3:30b exceeded memory budget at 32K+ context on 36 GB hardware.
+    ollama_model: str = "qwen3:14b"
+
+    # Fast routing model — override with OLLAMA_FAST_MODEL
+    # qwen3:8b (Q6_K): tool calling F1 0.933 at ~45-55 tok/s (2x speed of 14B).
+    # Use for routine tasks: simple tool calls, file reads, status checks.
+    # Combined memory with qwen3:14b: ~17 GB — both can stay loaded simultaneously.
+    ollama_fast_model: str = "qwen3:8b"
+
+    # Maximum concurrently loaded Ollama models — override with OLLAMA_MAX_LOADED_MODELS
+    # Set to 2 to keep qwen3:8b (fast) + qwen3:14b (primary) both hot.
+    # Requires setting OLLAMA_MAX_LOADED_MODELS=2 in the Ollama server environment.
+    ollama_max_loaded_models: int = 2
 
     # Context window size for Ollama inference — override with OLLAMA_NUM_CTX
-    # qwen3:30b with default context eats 45GB on a 39GB Mac.
-    # 4096 keeps memory at ~19GB. Set to 0 to use model defaults.
-    ollama_num_ctx: int = 4096
+    # qwen3:14b at 32K: ~17.5 GB total (weights + KV cache) on M3 Max 36 GB.
+    # Set to 0 to use model defaults.
+    ollama_num_ctx: int = 32768
+
+    # Maximum models loaded simultaneously in Ollama — override with OLLAMA_MAX_LOADED_MODELS
+    # Set to 2 so Qwen3-8B and Qwen3-14B can stay hot concurrently (~17 GB combined).
+    # Requires Ollama ≥ 0.1.33.  Export this to the Ollama process environment:
+    #   OLLAMA_MAX_LOADED_MODELS=2 ollama serve
+    # or add it to your systemd/launchd unit before starting the harness.
+    ollama_max_loaded_models: int = 2
 
     # Fallback model chains — override with FALLBACK_MODELS / VISION_FALLBACK_MODELS
-    # as comma-separated strings, e.g. FALLBACK_MODELS="qwen3:30b,llama3.1"
+    # as comma-separated strings, e.g. FALLBACK_MODELS="qwen3:8b,qwen2.5:14b"
     # Or edit config/providers.yaml → fallback_chains for the canonical source.
     fallback_models: list[str] = [
-        "llama3.1:8b-instruct",
-        "llama3.1",
+        "qwen3:8b",
         "qwen2.5:14b",
         "qwen2.5:7b",
+        "llama3.1:8b-instruct",
+        "llama3.1",
         "llama3.2:3b",
     ]
     vision_fallback_models: list[str] = [
@@ -76,8 +94,18 @@ class Settings(BaseSettings):
 
     # ── Backend selection ────────────────────────────────────────────────────
     # "ollama"  — always use Ollama (default, safe everywhere)
+    # "vllm"    — use vLLM inference server (OpenAI-compatible, faster throughput)
     # "auto"    — pick best available local backend, fall back to Ollama
-    timmy_model_backend: Literal["ollama", "grok", "claude", "auto"] = "ollama"
+    timmy_model_backend: Literal["ollama", "vllm", "grok", "claude", "auto"] = "ollama"
+
+    # ── vLLM backend ──────────────────────────────────────────────────────────
+    # vLLM is an OpenAI-compatible inference server optimised for continuous
+    # batching — 3–10x higher throughput than Ollama for agentic workloads.
+    # Start server: python -m vllm.entrypoints.openai.api_server \
+    #   --model Qwen/Qwen2.5-14B-Instruct --port 8001
+    # Then set TIMMY_LLM_BACKEND=vllm (or enable vllm-local in providers.yaml)
+    vllm_url: str = "http://localhost:8001"
+    vllm_model: str = "Qwen/Qwen2.5-14B-Instruct"
 
     # ── Grok (xAI) — opt-in premium cloud backend ────────────────────────
     # Grok is a premium augmentation layer — local-first ethos preserved.
@@ -217,6 +245,10 @@ class Settings(BaseSettings):
     # ── Test / Diagnostics ─────────────────────────────────────────────
     # Skip loading heavy embedding models (for tests / low-memory envs).
     timmy_skip_embeddings: bool = False
+    # Embedding backend: "ollama" for Ollama, "local" for sentence-transformers.
+    timmy_embedding_backend: Literal["ollama", "local"] = "local"
+    # Ollama model to use for embeddings (e.g., "nomic-embed-text").
+    ollama_embedding_model: str = "nomic-embed-text"
     # Disable CSRF middleware entirely (for tests).
     timmy_disable_csrf: bool = False
     # Mark the process as running in test mode.
@@ -289,14 +321,6 @@ class Settings(BaseSettings):
     thinking_memory_check_every: int = 50  # check memory status every Nth thought
     thinking_idle_timeout_minutes: int = 60  # pause thoughts after N minutes without user input
 
-    # ── Dreaming Mode ─────────────────────────────────────────────────
-    # When enabled, the agent replays past sessions during idle time to
-    # simulate alternative actions and propose behavioural rules.
-    dreaming_enabled: bool = True
-    dreaming_idle_threshold_minutes: int = 10  # idle minutes before dreaming starts
-    dreaming_cycle_seconds: int = 600           # seconds between dream attempts
-    dreaming_timeout_seconds: int = 60          # max LLM call time per dream cycle
-
     # ── Gitea Integration ─────────────────────────────────────────────
     # Local Gitea instance for issue tracking and self-improvement.
     # These values are passed as env vars to the gitea-mcp server process.
@@ -312,6 +336,16 @@ class Settings(BaseSettings):
     mcp_timeout: int = 15
     mcp_bridge_timeout: int = 60  # HTTP timeout for MCP bridge Ollama calls (seconds)
 
+    # ── Backlog Triage Loop ────────────────────────────────────────────
+    # Autonomous loop: fetch open issues, score, assign to agents.
+    backlog_triage_enabled: bool = False
+    # Seconds between triage cycles (default: 15 minutes).
+    backlog_triage_interval_seconds: int = 900
+    # When True, score and summarize but don't write to Gitea.
+    backlog_triage_dry_run: bool = False
+    # Create a daily triage summary issue/comment.
+    backlog_triage_daily_summary: bool = True
+
     # ── Loop QA (Self-Testing) ─────────────────────────────────────────
     # Self-test orchestrator that probes capabilities alongside the thinking loop.
     loop_qa_enabled: bool = True
@@ -319,6 +353,15 @@ class Settings(BaseSettings):
     loop_qa_upgrade_threshold: int = 3  # consecutive failures → file task
     loop_qa_max_per_hour: int = 12  # safety throttle
 
+    # ── Vassal Protocol (Autonomous Orchestrator) ─────────────────────
+    # Timmy as lead decision-maker: triage backlog, dispatch agents, monitor health.
+    # See timmy/vassal/ for implementation.
+    vassal_enabled: bool = False  # off by default — enable when Qwen3-14B is loaded
+    vassal_cycle_interval: int = 300  # seconds between orchestration cycles (5 min)
+    vassal_max_dispatch_per_cycle: int = 10  # cap on new dispatches per cycle
+    vassal_stuck_threshold_minutes: int = 120  # minutes before agent issue is "stuck"
+    vassal_idle_threshold_minutes: int = 30  # minutes before agent is "idle"
+
     # ── Paperclip AI — orchestration bridge ────────────────────────────
     # URL where the Paperclip server listens.
     # For VPS deployment behind nginx, use the public domain.
@@ -354,6 +397,11 @@ class Settings(BaseSettings):
     autoresearch_time_budget: int = 300  # seconds per experiment run
     autoresearch_max_iterations: int = 100
     autoresearch_metric: str = "val_bpb"  # metric to optimise (lower = better)
+    # M3 Max / Apple Silicon tuning (Issue #905).
+    # dataset: "tinystories" (default, lower-entropy, recommended for Mac) or "openwebtext".
+    autoresearch_dataset: str = "tinystories"
+    # backend: "auto" detects MLX on Apple Silicon; "cpu" forces CPU fallback.
+    autoresearch_backend: str = "auto"
 
     # ── Weekly Narrative Summary ───────────────────────────────────────
     # Generates a human-readable weekly summary of development activity.
@@ -374,6 +422,24 @@ class Settings(BaseSettings):
     # Default timeout for git operations.
     hands_git_timeout: int = 60
 
+    # ── Hermes Health Monitor ─────────────────────────────────────────
+    # Enable the Hermes system health monitor (memory, disk, Ollama, processes, network).
+    hermes_enabled: bool = True
+    # How often Hermes runs a full health cycle (seconds). Default: 5 minutes.
+    hermes_interval_seconds: int = 300
+    # Alert threshold: free memory below this triggers model unloading / alert (GB).
+    hermes_memory_free_min_gb: float = 4.0
+    # Alert threshold: free disk below this triggers cleanup / alert (GB).
+    hermes_disk_free_min_gb: float = 10.0
+
+    # ── Energy Budget Monitoring ───────────────────────────────────────
+    # Enable energy budget monitoring (tracks CPU/GPU power during inference).
+    energy_budget_enabled: bool = True
+    # Watts threshold that auto-activates low power mode (on-battery only).
+    energy_budget_watts_threshold: float = 15.0
+    # Model to prefer in low power mode (smaller = more efficient).
+    energy_low_power_model: str = "qwen3:1b"
+
     # ── Error Logging ─────────────────────────────────────────────────
     error_log_enabled: bool = True
     error_log_dir: str = "logs"

src/dashboard/app.py

@@ -35,23 +35,28 @@ from dashboard.routes.chat_api_v1 import router as chat_api_v1_router
 from dashboard.routes.daily_run import router as daily_run_router
 from dashboard.routes.db_explorer import router as db_explorer_router
 from dashboard.routes.discord import router as discord_router
-from dashboard.routes.dreaming import router as dreaming_router
 from dashboard.routes.experiments import router as experiments_router
 from dashboard.routes.grok import router as grok_router
+from dashboard.routes.energy import router as energy_router
 from dashboard.routes.health import router as health_router
+from dashboard.routes.hermes import router as hermes_router
 from dashboard.routes.loop_qa import router as loop_qa_router
 from dashboard.routes.memory import router as memory_router
 from dashboard.routes.mobile import router as mobile_router
 from dashboard.routes.models import api_router as models_api_router
 from dashboard.routes.models import router as models_router
+from dashboard.routes.nexus import router as nexus_router
 from dashboard.routes.quests import router as quests_router
 from dashboard.routes.scorecards import router as scorecards_router
 from dashboard.routes.sovereignty_metrics import router as sovereignty_metrics_router
+from dashboard.routes.sovereignty_ws import router as sovereignty_ws_router
 from dashboard.routes.spark import router as spark_router
 from dashboard.routes.system import router as system_router
 from dashboard.routes.tasks import router as tasks_router
 from dashboard.routes.telegram import router as telegram_router
 from dashboard.routes.thinking import router as thinking_router
+from dashboard.routes.self_correction import router as self_correction_router
+from dashboard.routes.three_strike import router as three_strike_router
 from dashboard.routes.tools import router as tools_router
 from dashboard.routes.tower import router as tower_router
 from dashboard.routes.voice import router as voice_router
@@ -181,6 +186,33 @@ async def _thinking_scheduler() -> None:
         await asyncio.sleep(settings.thinking_interval_seconds)
 
 
+async def _hermes_scheduler() -> None:
+    """Background task: Hermes system health monitor, runs every 5 minutes.
+
+    Checks memory, disk, Ollama, processes, and network.
+    Auto-resolves what it can; fires push notifications when human help is needed.
+    """
+    from infrastructure.hermes.monitor import hermes_monitor
+
+    await asyncio.sleep(20)  # Stagger after other schedulers
+
+    while True:
+        try:
+            if settings.hermes_enabled:
+                report = await hermes_monitor.run_cycle()
+                if report.has_issues:
+                    logger.warning(
+                        "Hermes health issues detected — overall: %s",
+                        report.overall.value,
+                    )
+        except asyncio.CancelledError:
+            raise
+        except Exception as exc:
+            logger.error("Hermes scheduler error: %s", exc)
+
+        await asyncio.sleep(settings.hermes_interval_seconds)
+
+
 async def _loop_qa_scheduler() -> None:
     """Background task: run capability self-tests on a separate timer.
 
@@ -220,36 +252,6 @@ async def _loop_qa_scheduler() -> None:
         await asyncio.sleep(interval)
 
 
-async def _dreaming_scheduler() -> None:
-    """Background task: run idle-time dreaming cycles.
-
-    When the system has been idle for ``dreaming_idle_threshold_minutes``,
-    the dreaming engine replays a past session and simulates alternatives.
-    """
-    from timmy.dreaming import dreaming_engine
-
-    await asyncio.sleep(15)  # Stagger after loop QA scheduler
-
-    while True:
-        try:
-            if settings.dreaming_enabled:
-                await asyncio.wait_for(
-                    dreaming_engine.dream_once(),
-                    timeout=settings.dreaming_timeout_seconds + 10,
-                )
-        except TimeoutError:
-            logger.warning(
-                "Dreaming cycle timed out after %ds",
-                settings.dreaming_timeout_seconds,
-            )
-        except asyncio.CancelledError:
-            raise
-        except Exception as exc:
-            logger.error("Dreaming scheduler error: %s", exc)
-
-        await asyncio.sleep(settings.dreaming_cycle_seconds)
-
-
 _PRESENCE_POLL_SECONDS = 30
 _PRESENCE_INITIAL_DELAY = 3
 
@@ -410,17 +412,18 @@ def _startup_background_tasks() -> list[asyncio.Task]:
         asyncio.create_task(_briefing_scheduler()),
         asyncio.create_task(_thinking_scheduler()),
         asyncio.create_task(_loop_qa_scheduler()),
-        asyncio.create_task(_dreaming_scheduler()),
         asyncio.create_task(_presence_watcher()),
         asyncio.create_task(_start_chat_integrations_background()),
+        asyncio.create_task(_hermes_scheduler()),
     ]
     try:
         from timmy.paperclip import start_paperclip_poller
+
         bg_tasks.append(asyncio.create_task(start_paperclip_poller()))
         logger.info("Paperclip poller started")
     except ImportError:
         logger.debug("Paperclip module not found, skipping poller")
-    
+
     return bg_tasks
 
 
@@ -549,12 +552,28 @@ async def lifespan(app: FastAPI):
     except Exception:
         logger.debug("Failed to register error recorder")
 
+    # Mark session start for sovereignty duration tracking
+    try:
+        from timmy.sovereignty import mark_session_start
+
+        mark_session_start()
+    except Exception:
+        logger.debug("Failed to mark sovereignty session start")
+
     logger.info("✓ Dashboard ready for requests")
 
     yield
 
     await _shutdown_cleanup(bg_tasks, workshop_heartbeat)
 
+    # Generate and commit sovereignty session report
+    try:
+        from timmy.sovereignty import generate_and_commit_report
+
+        await generate_and_commit_report()
+    except Exception as exc:
+        logger.warning("Sovereignty report generation failed at shutdown: %s", exc)
+
 
 app = FastAPI(
     title="Mission Control",
@@ -653,6 +672,7 @@ app.include_router(tools_router)
 app.include_router(spark_router)
 app.include_router(discord_router)
 app.include_router(memory_router)
+app.include_router(nexus_router)
 app.include_router(grok_router)
 app.include_router(models_router)
 app.include_router(models_api_router)
@@ -670,10 +690,14 @@ app.include_router(world_router)
 app.include_router(matrix_router)
 app.include_router(tower_router)
 app.include_router(daily_run_router)
+app.include_router(hermes_router)
+app.include_router(energy_router)
 app.include_router(quests_router)
 app.include_router(scorecards_router)
 app.include_router(sovereignty_metrics_router)
-app.include_router(dreaming_router)
+app.include_router(sovereignty_ws_router)
+app.include_router(three_strike_router)
+app.include_router(self_correction_router)
 
 
 @app.websocket("/ws")

src/dashboard/models/calm.py

@@ -8,6 +8,8 @@ from .database import Base  # Assuming a shared Base in models/database.py
 
 
 class TaskState(StrEnum):
+    """Enumeration of possible task lifecycle states."""
+
     LATER = "LATER"
     NEXT = "NEXT"
     NOW = "NOW"
@@ -16,12 +18,16 @@ class TaskState(StrEnum):
 
 
 class TaskCertainty(StrEnum):
+    """Enumeration of task time-certainty levels."""
+
     FUZZY = "FUZZY"  # An intention without a time
     SOFT = "SOFT"  # A flexible task with a time
     HARD = "HARD"  # A fixed meeting/appointment
 
 
 class Task(Base):
+    """SQLAlchemy model representing a CALM task."""
+
     __tablename__ = "tasks"
 
     id = Column(Integer, primary_key=True, index=True)
@@ -52,6 +58,8 @@ class Task(Base):
 
 
 class JournalEntry(Base):
+    """SQLAlchemy model for a daily journal entry with MITs and reflections."""
+
     __tablename__ = "journal_entries"
 
     id = Column(Integer, primary_key=True, index=True)

src/dashboard/routes/agents.py

@@ -46,6 +46,49 @@ async def list_agents():
     }
 
 
+@router.get("/emotional-profile", response_class=HTMLResponse)
+async def emotional_profile(request: Request):
+    """HTMX partial: render emotional profiles for all loaded agents."""
+    try:
+        from timmy.agents.loader import load_agents
+
+        agents = load_agents()
+        profiles = []
+        for agent_id, agent in agents.items():
+            profile = agent.emotional_state.get_profile()
+            profile["agent_id"] = agent_id
+            profile["agent_name"] = agent.name
+            profiles.append(profile)
+    except Exception as exc:
+        logger.warning("Failed to load emotional profiles: %s", exc)
+        profiles = []
+
+    return templates.TemplateResponse(
+        request,
+        "partials/emotional_profile.html",
+        {"profiles": profiles},
+    )
+
+
+@router.get("/emotional-profile/json")
+async def emotional_profile_json():
+    """JSON API: return emotional profiles for all loaded agents."""
+    try:
+        from timmy.agents.loader import load_agents
+
+        agents = load_agents()
+        profiles = []
+        for agent_id, agent in agents.items():
+            profile = agent.emotional_state.get_profile()
+            profile["agent_id"] = agent_id
+            profile["agent_name"] = agent.name
+            profiles.append(profile)
+        return {"profiles": profiles}
+    except Exception as exc:
+        logger.warning("Failed to load emotional profiles: %s", exc)
+        return {"profiles": [], "error": str(exc)}
+
+
 @router.get("/default/panel", response_class=HTMLResponse)
 async def agent_panel(request: Request):
     """Chat panel — for HTMX main-panel swaps."""

src/dashboard/routes/discord.py

@@ -14,6 +14,8 @@ router = APIRouter(prefix="/discord", tags=["discord"])
 
 
 class TokenPayload(BaseModel):
+    """Request payload containing a Discord bot token."""
+
     token: str
 
 

src/dashboard/routes/dreaming.py

@@ -1,85 +0,0 @@
-"""Dreaming mode dashboard routes.
-
-GET  /dreaming/api/status   — JSON status of the dreaming engine
-GET  /dreaming/api/recent   — JSON list of recent dream records
-POST /dreaming/api/trigger  — Manually trigger a dream cycle (for testing)
-GET  /dreaming/partial      — HTMX partial: dreaming status panel
-"""
-
-import logging
-
-from fastapi import APIRouter, Request
-from fastapi.responses import HTMLResponse, JSONResponse
-
-from dashboard.templating import templates
-from timmy.dreaming import dreaming_engine
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(prefix="/dreaming", tags=["dreaming"])
-
-
-@router.get("/api/status", response_class=JSONResponse)
-async def dreaming_status():
-    """Return current dreaming engine status as JSON."""
-    return dreaming_engine.get_status()
-
-
-@router.get("/api/recent", response_class=JSONResponse)
-async def dreaming_recent(limit: int = 10):
-    """Return recent dream records as JSON."""
-    dreams = dreaming_engine.get_recent_dreams(limit=limit)
-    return [
-        {
-            "id": d.id,
-            "session_excerpt": d.session_excerpt[:200],
-            "decision_point": d.decision_point[:200],
-            "simulation": d.simulation,
-            "proposed_rule": d.proposed_rule,
-            "created_at": d.created_at,
-        }
-        for d in dreams
-    ]
-
-
-@router.post("/api/trigger", response_class=JSONResponse)
-async def dreaming_trigger():
-    """Manually trigger a dream cycle (bypasses idle check).
-
-    Useful for testing and manual inspection. Forces idle state temporarily.
-    """
-    from datetime import UTC, datetime, timedelta
-
-    from config import settings
-
-    # Temporarily back-date last activity to appear idle
-    original_time = dreaming_engine._last_activity_time
-    dreaming_engine._last_activity_time = datetime.now(UTC) - timedelta(
-        minutes=settings.dreaming_idle_threshold_minutes + 1
-    )
-
-    try:
-        dream = await dreaming_engine.dream_once()
-    finally:
-        dreaming_engine._last_activity_time = original_time
-
-    if dream:
-        return {
-            "status": "ok",
-            "dream_id": dream.id,
-            "proposed_rule": dream.proposed_rule,
-            "simulation": dream.simulation[:200],
-        }
-    return {"status": "skipped", "reason": "No dream produced (no sessions or LLM unavailable)"}
-
-
-@router.get("/partial", response_class=HTMLResponse)
-async def dreaming_partial(request: Request):
-    """HTMX partial: dreaming status panel for the dashboard."""
-    status = dreaming_engine.get_status()
-    recent = dreaming_engine.get_recent_dreams(limit=5)
-    return templates.TemplateResponse(
-        request,
-        "partials/dreaming_status.html",
-        {"status": status, "recent_dreams": recent},
-    )

src/dashboard/routes/energy.py

@@ -0,0 +1,121 @@
+"""Energy Budget Monitoring routes.
+
+Exposes the energy budget monitor via REST API so the dashboard and
+external tools can query power draw, efficiency scores, and toggle
+low power mode.
+
+Refs: #1009
+"""
+
+import logging
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+from config import settings
+from infrastructure.energy.monitor import energy_monitor
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/energy", tags=["energy"])
+
+
+class LowPowerRequest(BaseModel):
+    """Request body for toggling low power mode."""
+
+    enabled: bool
+
+
+class InferenceEventRequest(BaseModel):
+    """Request body for recording an inference event."""
+
+    model: str
+    tokens_per_second: float
+
+
+@router.get("/status")
+async def energy_status():
+    """Return the current energy budget status.
+
+    Returns the live power estimate, efficiency score (0–10), recent
+    inference samples, and whether low power mode is active.
+    """
+    if not getattr(settings, "energy_budget_enabled", True):
+        return {
+            "enabled": False,
+            "message": "Energy budget monitoring is disabled (ENERGY_BUDGET_ENABLED=false)",
+        }
+
+    report = await energy_monitor.get_report()
+    return {**report.to_dict(), "enabled": True}
+
+
+@router.get("/report")
+async def energy_report():
+    """Detailed energy budget report with all recent samples.
+
+    Same as /energy/status but always includes the full sample history.
+    """
+    if not getattr(settings, "energy_budget_enabled", True):
+        raise HTTPException(status_code=503, detail="Energy budget monitoring is disabled")
+
+    report = await energy_monitor.get_report()
+    data = report.to_dict()
+    # Override recent_samples to include the full window (not just last 10)
+    data["recent_samples"] = [
+        {
+            "timestamp": s.timestamp,
+            "model": s.model,
+            "tokens_per_second": round(s.tokens_per_second, 1),
+            "estimated_watts": round(s.estimated_watts, 2),
+            "efficiency": round(s.efficiency, 3),
+            "efficiency_score": round(s.efficiency_score, 2),
+        }
+        for s in list(energy_monitor._samples)
+    ]
+    return {**data, "enabled": True}
+
+
+@router.post("/low-power")
+async def set_low_power_mode(body: LowPowerRequest):
+    """Enable or disable low power mode.
+
+    In low power mode the cascade router is advised to prefer the
+    configured energy_low_power_model (see settings).
+    """
+    if not getattr(settings, "energy_budget_enabled", True):
+        raise HTTPException(status_code=503, detail="Energy budget monitoring is disabled")
+
+    energy_monitor.set_low_power_mode(body.enabled)
+    low_power_model = getattr(settings, "energy_low_power_model", "qwen3:1b")
+    return {
+        "low_power_mode": body.enabled,
+        "preferred_model": low_power_model if body.enabled else None,
+        "message": (
+            f"Low power mode {'enabled' if body.enabled else 'disabled'}. "
+            + (f"Routing to {low_power_model}." if body.enabled else "Routing restored to default.")
+        ),
+    }
+
+
+@router.post("/record")
+async def record_inference_event(body: InferenceEventRequest):
+    """Record an inference event for efficiency tracking.
+
+    Called after each LLM inference completes.  Updates the rolling
+    efficiency score and may auto-activate low power mode if watts
+    exceed the configured threshold.
+    """
+    if not getattr(settings, "energy_budget_enabled", True):
+        return {"recorded": False, "message": "Energy budget monitoring is disabled"}
+
+    if body.tokens_per_second <= 0:
+        raise HTTPException(status_code=422, detail="tokens_per_second must be positive")
+
+    sample = energy_monitor.record_inference(body.model, body.tokens_per_second)
+    return {
+        "recorded": True,
+        "efficiency_score": round(sample.efficiency_score, 2),
+        "estimated_watts": round(sample.estimated_watts, 2),
+        "low_power_mode": energy_monitor.low_power_mode,
+    }

src/dashboard/routes/health.py

@@ -124,6 +124,73 @@ async def check_ollama() -> bool:
     return dep.status == "healthy"
 
 
+# vLLM health cache (30-second TTL)
+_vllm_cache: DependencyStatus | None = None
+_vllm_cache_ts: float = 0.0
+_VLLM_CACHE_TTL = 30.0
+
+
+def _check_vllm_sync() -> DependencyStatus:
+    """Synchronous vLLM check — run via asyncio.to_thread()."""
+    try:
+        import urllib.request
+
+        base_url = settings.vllm_url.rstrip("/")
+        # vLLM exposes /health at the server root (strip /v1 if present)
+        if base_url.endswith("/v1"):
+            base_url = base_url[:-3]
+        req = urllib.request.Request(
+            f"{base_url}/health",
+            method="GET",
+            headers={"Accept": "application/json"},
+        )
+        with urllib.request.urlopen(req, timeout=2) as response:
+            if response.status == 200:
+                return DependencyStatus(
+                    name="vLLM",
+                    status="healthy",
+                    sovereignty_score=10,
+                    details={"url": settings.vllm_url, "model": settings.vllm_model},
+                )
+    except Exception as exc:
+        logger.debug("vLLM health check failed: %s", exc)
+
+    return DependencyStatus(
+        name="vLLM",
+        status="unavailable",
+        sovereignty_score=10,
+        details={"url": settings.vllm_url, "error": "Cannot connect to vLLM server"},
+    )
+
+
+async def _check_vllm() -> DependencyStatus:
+    """Check vLLM backend status without blocking the event loop.
+
+    Results are cached for 30 seconds.  vLLM is an optional backend;
+    unavailability triggers graceful fallback to Ollama.
+    """
+    global _vllm_cache, _vllm_cache_ts  # noqa: PLW0603
+
+    now = time.monotonic()
+    if _vllm_cache is not None and (now - _vllm_cache_ts) < _VLLM_CACHE_TTL:
+        return _vllm_cache
+
+    try:
+        result = await asyncio.to_thread(_check_vllm_sync)
+    except Exception as exc:
+        logger.debug("vLLM async check failed: %s", exc)
+        result = DependencyStatus(
+            name="vLLM",
+            status="unavailable",
+            sovereignty_score=10,
+            details={"url": settings.vllm_url, "error": "Cannot connect to vLLM server"},
+        )
+
+    _vllm_cache = result
+    _vllm_cache_ts = now
+    return result
+
+
 def _check_lightning() -> DependencyStatus:
     """Check Lightning payment backend status."""
     return DependencyStatus(
@@ -195,13 +262,22 @@ async def health_check():
     # Legacy format for test compatibility
     ollama_ok = await check_ollama()
 
-    agent_status = "idle" if ollama_ok else "offline"
+    # Check vLLM only when it is the configured backend (avoid probing unused services)
+    vllm_status: str | None = None
+    if settings.timmy_model_backend == "vllm":
+        vllm_dep = await _check_vllm()
+        vllm_status = "up" if vllm_dep.status == "healthy" else "down"
+
+    inference_ok = vllm_status == "up" if vllm_status is not None else ollama_ok
+    agent_status = "idle" if inference_ok else "offline"
+
+    services: dict = {"ollama": "up" if ollama_ok else "down"}
+    if vllm_status is not None:
+        services["vllm"] = vllm_status
 
     return {
-        "status": "ok" if ollama_ok else "degraded",
-        "services": {
-            "ollama": "up" if ollama_ok else "down",
-        },
+        "status": "ok" if inference_ok else "degraded",
+        "services": services,
         "agents": {
             "agent": {"status": agent_status},
         },
@@ -210,7 +286,7 @@ async def health_check():
         "version": "2.0.0",
         "uptime_seconds": uptime,
         "llm_backend": settings.timmy_model_backend,
-        "llm_model": settings.ollama_model,
+        "llm_model": settings.vllm_model if settings.timmy_model_backend == "vllm" else settings.ollama_model,
     }
 
 
@@ -252,6 +328,9 @@ async def sovereignty_check():
         _check_lightning(),
         _check_sqlite(),
     ]
+    # Include vLLM in the audit when it is the active backend
+    if settings.timmy_model_backend == "vllm":
+        dependencies.append(await _check_vllm())
 
     overall = _calculate_overall_score(dependencies)
     recommendations = _generate_recommendations(dependencies)

src/dashboard/routes/hermes.py

@@ -0,0 +1,45 @@
+"""Hermes health monitor routes.
+
+Exposes the Hermes health monitor via REST API so the dashboard
+and external tools can query system status and trigger checks.
+
+Refs: #1073
+"""
+
+import logging
+
+from fastapi import APIRouter
+
+from infrastructure.hermes.monitor import hermes_monitor
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/hermes", tags=["hermes"])
+
+
+@router.get("/status")
+async def hermes_status():
+    """Return the most recent Hermes health report.
+
+    Returns the cached result from the last background cycle — does not
+    trigger a new check.  Use POST /hermes/check to run an immediate check.
+    """
+    report = hermes_monitor.last_report
+    if report is None:
+        return {
+            "status": "no_data",
+            "message": "No health report yet — first cycle pending",
+            "seconds_since_last_run": hermes_monitor.seconds_since_last_run,
+        }
+    return report.to_dict()
+
+
+@router.post("/check")
+async def hermes_check():
+    """Trigger an immediate Hermes health check cycle.
+
+    Runs all monitors synchronously and returns the full report.
+    Use sparingly — this blocks until all checks complete (~5 seconds).
+    """
+    report = await hermes_monitor.run_cycle()
+    return report.to_dict()

src/dashboard/routes/nexus.py

@@ -0,0 +1,166 @@
+"""Nexus — Timmy's persistent conversational awareness space.
+
+A conversational-only interface where Timmy maintains live memory context.
+No tool use; pure conversation with memory integration and a teaching panel.
+
+Routes:
+    GET  /nexus              — render nexus page with live memory sidebar
+    POST /nexus/chat         — send a message; returns HTMX partial
+    POST /nexus/teach        — inject a fact into Timmy's live memory
+    DELETE /nexus/history    — clear the nexus conversation history
+"""
+
+import asyncio
+import logging
+from datetime import UTC, datetime
+
+from fastapi import APIRouter, Form, Request
+from fastapi.responses import HTMLResponse
+
+from dashboard.templating import templates
+from timmy.memory_system import (
+    get_memory_stats,
+    recall_personal_facts_with_ids,
+    search_memories,
+    store_personal_fact,
+)
+from timmy.session import _clean_response, chat, reset_session
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/nexus", tags=["nexus"])
+
+_NEXUS_SESSION_ID = "nexus"
+_MAX_MESSAGE_LENGTH = 10_000
+
+# In-memory conversation log for the Nexus session (mirrors chat store pattern
+# but is scoped to the Nexus so it won't pollute the main dashboard history).
+_nexus_log: list[dict] = []
+
+
+def _ts() -> str:
+    return datetime.now(UTC).strftime("%H:%M:%S")
+
+
+def _append_log(role: str, content: str) -> None:
+    _nexus_log.append({"role": role, "content": content, "timestamp": _ts()})
+    # Keep last 200 exchanges to bound memory usage
+    if len(_nexus_log) > 200:
+        del _nexus_log[:-200]
+
+
+@router.get("", response_class=HTMLResponse)
+async def nexus_page(request: Request):
+    """Render the Nexus page with live memory context."""
+    stats = get_memory_stats()
+    facts = recall_personal_facts_with_ids()[:8]
+
+    return templates.TemplateResponse(
+        request,
+        "nexus.html",
+        {
+            "page_title": "Nexus",
+            "messages": list(_nexus_log),
+            "stats": stats,
+            "facts": facts,
+        },
+    )
+
+
+@router.post("/chat", response_class=HTMLResponse)
+async def nexus_chat(request: Request, message: str = Form(...)):
+    """Conversational-only chat routed through the Nexus session.
+
+    Does not invoke tool-use approval flow — pure conversation with memory
+    context injected from Timmy's live memory store.
+    """
+    message = message.strip()
+    if not message:
+        return HTMLResponse("")
+    if len(message) > _MAX_MESSAGE_LENGTH:
+        return templates.TemplateResponse(
+            request,
+            "partials/nexus_message.html",
+            {
+                "user_message": message[:80] + "…",
+                "response": None,
+                "error": "Message too long (max 10 000 chars).",
+                "timestamp": _ts(),
+                "memory_hits": [],
+            },
+        )
+
+    ts = _ts()
+
+    # Fetch semantically relevant memories to surface in the sidebar
+    try:
+        memory_hits = await asyncio.to_thread(search_memories, query=message, limit=4)
+    except Exception as exc:
+        logger.warning("Nexus memory search failed: %s", exc)
+        memory_hits = []
+
+    # Conversational response — no tool approval flow
+    response_text: str | None = None
+    error_text: str | None = None
+    try:
+        raw = await chat(message, session_id=_NEXUS_SESSION_ID)
+        response_text = _clean_response(raw)
+    except Exception as exc:
+        logger.error("Nexus chat error: %s", exc)
+        error_text = "Timmy is unavailable right now. Check that Ollama is running."
+
+    _append_log("user", message)
+    if response_text:
+        _append_log("assistant", response_text)
+
+    return templates.TemplateResponse(
+        request,
+        "partials/nexus_message.html",
+        {
+            "user_message": message,
+            "response": response_text,
+            "error": error_text,
+            "timestamp": ts,
+            "memory_hits": memory_hits,
+        },
+    )
+
+
+@router.post("/teach", response_class=HTMLResponse)
+async def nexus_teach(request: Request, fact: str = Form(...)):
+    """Inject a fact into Timmy's live memory from the Nexus teaching panel."""
+    fact = fact.strip()
+    if not fact:
+        return HTMLResponse("")
+
+    try:
+        await asyncio.to_thread(store_personal_fact, fact)
+        facts = await asyncio.to_thread(recall_personal_facts_with_ids)
+        facts = facts[:8]
+    except Exception as exc:
+        logger.error("Nexus teach error: %s", exc)
+        facts = []
+
+    return templates.TemplateResponse(
+        request,
+        "partials/nexus_facts.html",
+        {"facts": facts, "taught": fact},
+    )
+
+
+@router.delete("/history", response_class=HTMLResponse)
+async def nexus_clear_history(request: Request):
+    """Clear the Nexus conversation history."""
+    _nexus_log.clear()
+    reset_session(session_id=_NEXUS_SESSION_ID)
+    return templates.TemplateResponse(
+        request,
+        "partials/nexus_message.html",
+        {
+            "user_message": None,
+            "response": "Nexus conversation cleared.",
+            "error": None,
+            "timestamp": _ts(),
+            "memory_hits": [],
+        },
+    )

src/dashboard/routes/scorecards.py

@@ -10,6 +10,7 @@ from fastapi.responses import HTMLResponse, JSONResponse
 
 from dashboard.services.scorecard_service import (
     PeriodType,
+    ScorecardSummary,
     generate_all_scorecards,
     generate_scorecard,
     get_tracked_agents,
@@ -26,6 +27,216 @@ def _format_period_label(period_type: PeriodType) -> str:
     return "Daily" if period_type == PeriodType.daily else "Weekly"
 
 
+def _parse_period(period: str) -> PeriodType:
+    """Parse period string into PeriodType, defaulting to daily on invalid input.
+
+    Args:
+        period: The period string ('daily' or 'weekly')
+
+    Returns:
+        PeriodType.daily or PeriodType.weekly
+    """
+    try:
+        return PeriodType(period.lower())
+    except ValueError:
+        return PeriodType.daily
+
+
+def _format_token_display(token_net: int) -> str:
+    """Format token net value with +/- prefix for display.
+
+    Args:
+        token_net: The net token value
+
+    Returns:
+        Formatted string with + prefix for positive values
+    """
+    return f"{'+' if token_net > 0 else ''}{token_net}"
+
+
+def _format_token_class(token_net: int) -> str:
+    """Get CSS class for token net value based on sign.
+
+    Args:
+        token_net: The net token value
+
+    Returns:
+        'text-success' for positive/zero, 'text-danger' for negative
+    """
+    return "text-success" if token_net >= 0 else "text-danger"
+
+
+def _build_patterns_html(patterns: list[str]) -> str:
+    """Build HTML for patterns section if patterns exist.
+
+    Args:
+        patterns: List of pattern strings
+
+    Returns:
+        HTML string for patterns section or empty string
+    """
+    if not patterns:
+        return ""
+
+    patterns_list = "".join([f"<li>{p}</li>" for p in patterns])
+    return f"""
+    <div class="mt-3">
+        <h6>Patterns</h6>
+        <ul class="list-unstyled text-info">
+            {patterns_list}
+        </ul>
+    </div>
+    """
+
+
+def _build_narrative_html(bullets: list[str]) -> str:
+    """Build HTML for narrative bullets.
+
+    Args:
+        bullets: List of narrative bullet strings
+
+    Returns:
+        HTML string with list items
+    """
+    return "".join([f"<li>{b}</li>" for b in bullets])
+
+
+def _build_metrics_row_html(metrics: dict) -> str:
+    """Build HTML for the metrics summary row.
+
+    Args:
+        metrics: Dictionary with PRs, issues, tests, and token metrics
+
+    Returns:
+        HTML string for the metrics row
+    """
+    prs_opened = metrics["prs_opened"]
+    prs_merged = metrics["prs_merged"]
+    pr_merge_rate = int(metrics["pr_merge_rate"] * 100)
+    issues_touched = metrics["issues_touched"]
+    tests_affected = metrics["tests_affected"]
+    token_net = metrics["token_net"]
+
+    token_class = _format_token_class(token_net)
+    token_display = _format_token_display(token_net)
+
+    return f"""
+    <div class="row text-center small">
+        <div class="col">
+            <div class="text-muted">PRs</div>
+            <div class="fw-bold">{prs_opened}/{prs_merged}</div>
+            <div class="text-muted" style="font-size: 0.75rem;">
+                {pr_merge_rate}% merged
+            </div>
+        </div>
+        <div class="col">
+            <div class="text-muted">Issues</div>
+            <div class="fw-bold">{issues_touched}</div>
+        </div>
+        <div class="col">
+            <div class="text-muted">Tests</div>
+            <div class="fw-bold">{tests_affected}</div>
+        </div>
+        <div class="col">
+            <div class="text-muted">Tokens</div>
+            <div class="fw-bold {token_class}">{token_display}</div>
+        </div>
+    </div>
+    """
+
+
+def _render_scorecard_panel(
+    agent_id: str,
+    period_type: PeriodType,
+    data: dict,
+) -> str:
+    """Render HTML for a single scorecard panel.
+
+    Args:
+        agent_id: The agent ID
+        period_type: Daily or weekly period
+        data: Scorecard data dictionary with metrics, patterns, narrative_bullets
+
+    Returns:
+        HTML string for the scorecard panel
+    """
+    patterns_html = _build_patterns_html(data.get("patterns", []))
+    bullets_html = _build_narrative_html(data.get("narrative_bullets", []))
+    metrics_row = _build_metrics_row_html(data["metrics"])
+
+    return f"""
+    <div class="card mc-panel">
+        <div class="card-header d-flex justify-content-between align-items-center">
+            <h5 class="card-title mb-0">{agent_id.title()}</h5>
+            <span class="badge bg-secondary">{_format_period_label(period_type)}</span>
+        </div>
+        <div class="card-body">
+            <ul class="list-unstyled mb-3">
+                {bullets_html}
+            </ul>
+            {metrics_row}
+            {patterns_html}
+        </div>
+    </div>
+    """
+
+
+def _render_empty_scorecard(agent_id: str) -> str:
+    """Render HTML for an empty scorecard (no activity).
+
+    Args:
+        agent_id: The agent ID
+
+    Returns:
+        HTML string for the empty scorecard panel
+    """
+    return f"""
+    <div class="card mc-panel">
+        <h5 class="card-title">{agent_id.title()}</h5>
+        <p class="text-muted">No activity recorded for this period.</p>
+    </div>
+    """
+
+
+def _render_error_scorecard(agent_id: str, error: str) -> str:
+    """Render HTML for a scorecard that failed to load.
+
+    Args:
+        agent_id: The agent ID
+        error: Error message string
+
+    Returns:
+        HTML string for the error scorecard panel
+    """
+    return f"""
+    <div class="card mc-panel border-danger">
+        <h5 class="card-title">{agent_id.title()}</h5>
+        <p class="text-danger">Error loading scorecard: {error}</p>
+    </div>
+    """
+
+
+def _render_single_panel_wrapper(
+    agent_id: str,
+    period_type: PeriodType,
+    scorecard: ScorecardSummary | None,
+) -> str:
+    """Render a complete scorecard panel with wrapper div for single panel view.
+
+    Args:
+        agent_id: The agent ID
+        period_type: Daily or weekly period
+        scorecard: ScorecardSummary object or None
+
+    Returns:
+        HTML string for the complete panel
+    """
+    if scorecard is None:
+        return _render_empty_scorecard(agent_id)
+
+    return _render_scorecard_panel(agent_id, period_type, scorecard.to_dict())
+
+
 @router.get("/api/agents")
 async def list_tracked_agents() -> dict[str, list[str]]:
     """Return the list of tracked agent IDs.
@@ -149,99 +360,50 @@ async def agent_scorecard_panel(
     Returns:
         HTML panel with scorecard content
     """
-    try:
-        period_type = PeriodType(period.lower())
-    except ValueError:
-        period_type = PeriodType.daily
+    period_type = _parse_period(period)
 
     try:
         scorecard = generate_scorecard(agent_id, period_type)
-
-        if scorecard is None:
-            return HTMLResponse(
-                content=f"""
-                <div class="card mc-panel">
-                    <h5 class="card-title">{agent_id.title()}</h5>
-                    <p class="text-muted">No activity recorded for this period.</p>
-                </div>
-                """,
-                status_code=200,
-            )
-
-        data = scorecard.to_dict()
-
-        # Build patterns HTML
-        patterns_html = ""
-        if data["patterns"]:
-            patterns_list = "".join([f"<li>{p}</li>" for p in data["patterns"]])
-            patterns_html = f"""
-            <div class="mt-3">
-                <h6>Patterns</h6>
-                <ul class="list-unstyled text-info">
-                    {patterns_list}
-                </ul>
-            </div>
-            """
-
-        # Build bullets HTML
-        bullets_html = "".join([f"<li>{b}</li>" for b in data["narrative_bullets"]])
-
-        # Build metrics summary
-        metrics = data["metrics"]
-
-        html_content = f"""
-        <div class="card mc-panel">
-            <div class="card-header d-flex justify-content-between align-items-center">
-                <h5 class="card-title mb-0">{agent_id.title()}</h5>
-                <span class="badge bg-secondary">{_format_period_label(period_type)}</span>
-            </div>
-            <div class="card-body">
-                <ul class="list-unstyled mb-3">
-                    {bullets_html}
-                </ul>
-                
-                <div class="row text-center small">
-                    <div class="col">
-                        <div class="text-muted">PRs</div>
-                        <div class="fw-bold">{metrics["prs_opened"]}/{metrics["prs_merged"]}</div>
-                        <div class="text-muted" style="font-size: 0.75rem;">
-                            {int(metrics["pr_merge_rate"] * 100)}% merged
-                        </div>
-                    </div>
-                    <div class="col">
-                        <div class="text-muted">Issues</div>
-                        <div class="fw-bold">{metrics["issues_touched"]}</div>
-                    </div>
-                    <div class="col">
-                        <div class="text-muted">Tests</div>
-                        <div class="fw-bold">{metrics["tests_affected"]}</div>
-                    </div>
-                    <div class="col">
-                        <div class="text-muted">Tokens</div>
-                        <div class="fw-bold {"text-success" if metrics["token_net"] >= 0 else "text-danger"}">
-                            {"+" if metrics["token_net"] > 0 else ""}{metrics["token_net"]}
-                        </div>
-                    </div>
-                </div>
-                
-                {patterns_html}
-            </div>
-        </div>
-        """
-
+        html_content = _render_single_panel_wrapper(agent_id, period_type, scorecard)
         return HTMLResponse(content=html_content)
 
     except Exception as exc:
         logger.error("Failed to render scorecard panel for %s: %s", agent_id, exc)
-        return HTMLResponse(
-            content=f"""
-            <div class="card mc-panel border-danger">
-                <h5 class="card-title">{agent_id.title()}</h5>
-                <p class="text-danger">Error loading scorecard: {str(exc)}</p>
-            </div>
-            """,
-            status_code=200,
+        return HTMLResponse(content=_render_error_scorecard(agent_id, str(exc)))
+
+
+def _render_all_panels_grid(
+    scorecards: list[ScorecardSummary],
+    period_type: PeriodType,
+) -> str:
+    """Render all scorecard panels in a grid layout.
+
+    Args:
+        scorecards: List of scorecard summaries
+        period_type: Daily or weekly period
+
+    Returns:
+        HTML string with all panels in a grid
+    """
+    panels: list[str] = []
+    for scorecard in scorecards:
+        panel_html = _render_scorecard_panel(
+            scorecard.agent_id,
+            period_type,
+            scorecard.to_dict(),
         )
+        # Wrap each panel in a grid column
+        wrapped = f'<div class="col-md-6 col-lg-4 mb-3">{panel_html}</div>'
+        panels.append(wrapped)
+
+    return f"""
+    <div class="row">
+        {"".join(panels)}
+    </div>
+    <div class="text-muted small mt-2">
+        Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S UTC")}
+    </div>
+    """
 
 
 @router.get("/all/panels", response_class=HTMLResponse)
@@ -258,96 +420,15 @@ async def all_scorecard_panels(
     Returns:
         HTML with all scorecard panels
     """
-    try:
-        period_type = PeriodType(period.lower())
-    except ValueError:
-        period_type = PeriodType.daily
+    period_type = _parse_period(period)
 
     try:
         scorecards = generate_all_scorecards(period_type)
-
-        panels: list[str] = []
-        for scorecard in scorecards:
-            data = scorecard.to_dict()
-
-            # Build patterns HTML
-            patterns_html = ""
-            if data["patterns"]:
-                patterns_list = "".join([f"<li>{p}</li>" for p in data["patterns"]])
-                patterns_html = f"""
-                <div class="mt-3">
-                    <h6>Patterns</h6>
-                    <ul class="list-unstyled text-info">
-                        {patterns_list}
-                    </ul>
-                </div>
-                """
-
-            # Build bullets HTML
-            bullets_html = "".join([f"<li>{b}</li>" for b in data["narrative_bullets"]])
-            metrics = data["metrics"]
-
-            panel_html = f"""
-            <div class="col-md-6 col-lg-4 mb-3">
-                <div class="card mc-panel">
-                    <div class="card-header d-flex justify-content-between align-items-center">
-                        <h5 class="card-title mb-0">{scorecard.agent_id.title()}</h5>
-                        <span class="badge bg-secondary">{_format_period_label(period_type)}</span>
-                    </div>
-                    <div class="card-body">
-                        <ul class="list-unstyled mb-3">
-                            {bullets_html}
-                        </ul>
-                        
-                        <div class="row text-center small">
-                            <div class="col">
-                                <div class="text-muted">PRs</div>
-                                <div class="fw-bold">{metrics["prs_opened"]}/{metrics["prs_merged"]}</div>
-                                <div class="text-muted" style="font-size: 0.75rem;">
-                                    {int(metrics["pr_merge_rate"] * 100)}% merged
-                                </div>
-                            </div>
-                            <div class="col">
-                                <div class="text-muted">Issues</div>
-                                <div class="fw-bold">{metrics["issues_touched"]}</div>
-                            </div>
-                            <div class="col">
-                                <div class="text-muted">Tests</div>
-                                <div class="fw-bold">{metrics["tests_affected"]}</div>
-                            </div>
-                            <div class="col">
-                                <div class="text-muted">Tokens</div>
-                                <div class="fw-bold {"text-success" if metrics["token_net"] >= 0 else "text-danger"}">
-                                    {"+" if metrics["token_net"] > 0 else ""}{metrics["token_net"]}
-                                </div>
-                            </div>
-                        </div>
-                        
-                        {patterns_html}
-                    </div>
-                </div>
-            </div>
-            """
-            panels.append(panel_html)
-
-        html_content = f"""
-        <div class="row">
-            {"".join(panels)}
-        </div>
-        <div class="text-muted small mt-2">
-            Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S UTC")}
-        </div>
-        """
-
+        html_content = _render_all_panels_grid(scorecards, period_type)
         return HTMLResponse(content=html_content)
 
     except Exception as exc:
         logger.error("Failed to render all scorecard panels: %s", exc)
         return HTMLResponse(
-            content=f"""
-            <div class="alert alert-danger">
-                Error loading scorecards: {str(exc)}
-            </div>
-            """,
-            status_code=200,
+            content=f'<div class="alert alert-danger">Error loading scorecards: {exc}</div>'
         )

src/dashboard/routes/self_correction.py

@@ -0,0 +1,58 @@
+"""Self-Correction Dashboard routes.
+
+GET  /self-correction/ui       — HTML dashboard
+GET  /self-correction/timeline — HTMX partial: recent event timeline
+GET  /self-correction/patterns — HTMX partial: recurring failure patterns
+"""
+
+import logging
+
+from fastapi import APIRouter, Request
+from fastapi.responses import HTMLResponse
+
+from dashboard.templating import templates
+from infrastructure.self_correction import get_corrections, get_patterns, get_stats
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/self-correction", tags=["self-correction"])
+
+
+@router.get("/ui", response_class=HTMLResponse)
+async def self_correction_ui(request: Request):
+    """Render the Self-Correction Dashboard."""
+    stats = get_stats()
+    corrections = get_corrections(limit=20)
+    patterns = get_patterns(top_n=10)
+    return templates.TemplateResponse(
+        request,
+        "self_correction.html",
+        {
+            "stats": stats,
+            "corrections": corrections,
+            "patterns": patterns,
+        },
+    )
+
+
+@router.get("/timeline", response_class=HTMLResponse)
+async def self_correction_timeline(request: Request):
+    """HTMX partial: recent self-correction event timeline."""
+    corrections = get_corrections(limit=30)
+    return templates.TemplateResponse(
+        request,
+        "partials/self_correction_timeline.html",
+        {"corrections": corrections},
+    )
+
+
+@router.get("/patterns", response_class=HTMLResponse)
+async def self_correction_patterns(request: Request):
+    """HTMX partial: recurring failure patterns."""
+    patterns = get_patterns(top_n=10)
+    stats = get_stats()
+    return templates.TemplateResponse(
+        request,
+        "partials/self_correction_patterns.html",
+        {"patterns": patterns, "stats": stats},
+    )

src/dashboard/routes/sovereignty_ws.py

@@ -0,0 +1,40 @@
+"""WebSocket emitter for the sovereignty metrics dashboard widget.
+
+Streams real-time sovereignty snapshots to connected clients every
+*_PUSH_INTERVAL* seconds.  The snapshot includes per-layer sovereignty
+percentages, API cost rate, and skill crystallisation count.
+
+Refs: #954, #953
+"""
+
+import asyncio
+import json
+import logging
+
+from fastapi import APIRouter, WebSocket
+
+router = APIRouter(tags=["sovereignty"])
+
+logger = logging.getLogger(__name__)
+
+_PUSH_INTERVAL = 5  # seconds between snapshot pushes
+
+
+@router.websocket("/ws/sovereignty")
+async def sovereignty_ws(websocket: WebSocket) -> None:
+    """Stream sovereignty metric snapshots to the dashboard widget."""
+    from timmy.sovereignty.metrics import get_metrics_store
+
+    await websocket.accept()
+    logger.info("Sovereignty WS connected")
+
+    store = get_metrics_store()
+    try:
+        # Send initial snapshot immediately
+        await websocket.send_text(json.dumps(store.get_snapshot()))
+
+        while True:
+            await asyncio.sleep(_PUSH_INTERVAL)
+            await websocket.send_text(json.dumps(store.get_snapshot()))
+    except Exception:
+        logger.debug("Sovereignty WS disconnected")

src/dashboard/routes/telegram.py

@@ -7,6 +7,8 @@ router = APIRouter(prefix="/telegram", tags=["telegram"])
 
 
 class TokenPayload(BaseModel):
+    """Request payload containing a Telegram bot token."""
+
     token: str
 
 

src/dashboard/routes/three_strike.py

@@ -0,0 +1,116 @@
+"""Three-Strike Detector dashboard routes.
+
+Provides JSON API endpoints for inspecting and managing the three-strike
+detector state.
+
+Refs: #962
+"""
+
+import logging
+from typing import Any
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+from timmy.sovereignty.three_strike import CATEGORIES, get_detector
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/sovereignty/three-strike", tags=["three-strike"])
+
+
+class RecordRequest(BaseModel):
+    category: str
+    key: str
+    metadata: dict[str, Any] = {}
+
+
+class AutomationRequest(BaseModel):
+    artifact_path: str
+
+
+@router.get("")
+async def list_strikes() -> dict[str, Any]:
+    """Return all strike records."""
+    detector = get_detector()
+    records = detector.list_all()
+    return {
+        "records": [
+            {
+                "category": r.category,
+                "key": r.key,
+                "count": r.count,
+                "blocked": r.blocked,
+                "automation": r.automation,
+                "first_seen": r.first_seen,
+                "last_seen": r.last_seen,
+            }
+            for r in records
+        ],
+        "categories": sorted(CATEGORIES),
+    }
+
+
+@router.get("/blocked")
+async def list_blocked() -> dict[str, Any]:
+    """Return only blocked (category, key) pairs."""
+    detector = get_detector()
+    records = detector.list_blocked()
+    return {
+        "blocked": [
+            {
+                "category": r.category,
+                "key": r.key,
+                "count": r.count,
+                "automation": r.automation,
+                "last_seen": r.last_seen,
+            }
+            for r in records
+        ]
+    }
+
+
+@router.post("/record")
+async def record_strike(body: RecordRequest) -> dict[str, Any]:
+    """Record a manual action.  Returns strike state; 409 when blocked."""
+    from timmy.sovereignty.three_strike import ThreeStrikeError
+
+    detector = get_detector()
+    try:
+        record = detector.record(body.category, body.key, body.metadata)
+        return {
+            "category": record.category,
+            "key": record.key,
+            "count": record.count,
+            "blocked": record.blocked,
+            "automation": record.automation,
+        }
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=str(exc)) from exc
+    except ThreeStrikeError as exc:
+        raise HTTPException(
+            status_code=409,
+            detail={
+                "error": "three_strike_block",
+                "message": str(exc),
+                "category": exc.category,
+                "key": exc.key,
+                "count": exc.count,
+            },
+        ) from exc
+
+
+@router.post("/{category}/{key}/automation")
+async def register_automation(category: str, key: str, body: AutomationRequest) -> dict[str, bool]:
+    """Register an automation artifact to unblock a (category, key) pair."""
+    detector = get_detector()
+    detector.register_automation(category, key, body.artifact_path)
+    return {"success": True}
+
+
+@router.get("/{category}/{key}/events")
+async def get_strike_events(category: str, key: str, limit: int = 50) -> dict[str, Any]:
+    """Return the individual strike events for a (category, key) pair."""
+    detector = get_detector()
+    events = detector.get_events(category, key, limit=limit)
+    return {"category": category, "key": key, "events": events}

src/dashboard/routes/voice.py

@@ -1,11 +1,14 @@
 """Voice routes — /voice/* and /voice/enhanced/* endpoints.
 
 Provides NLU intent detection, TTS control, the full voice-to-action
-pipeline (detect intent → execute → optionally speak), and the voice
-button UI page.
+pipeline (detect intent → execute → optionally speak), the voice
+button UI page, and voice settings customisation.
 """
 
+import asyncio
+import json
 import logging
+from pathlib import Path
 
 from fastapi import APIRouter, Form, Request
 from fastapi.responses import HTMLResponse
@@ -14,6 +17,31 @@ from dashboard.templating import templates
 from integrations.voice.nlu import detect_intent, extract_command
 from timmy.agent import create_timmy
 
+# ── Voice settings persistence ───────────────────────────────────────────────
+
+_VOICE_SETTINGS_FILE = Path("data/voice_settings.json")
+_DEFAULT_VOICE_SETTINGS: dict = {"rate": 175, "volume": 0.9, "voice_id": ""}
+
+
+def _load_voice_settings() -> dict:
+    """Read persisted voice settings from disk; return defaults on any error."""
+    try:
+        if _VOICE_SETTINGS_FILE.exists():
+            return json.loads(_VOICE_SETTINGS_FILE.read_text())
+    except Exception as exc:
+        logger.warning("Failed to load voice settings: %s", exc)
+    return dict(_DEFAULT_VOICE_SETTINGS)
+
+
+def _save_voice_settings(data: dict) -> None:
+    """Persist voice settings to disk; log and continue on any error."""
+    try:
+        _VOICE_SETTINGS_FILE.parent.mkdir(parents=True, exist_ok=True)
+        _VOICE_SETTINGS_FILE.write_text(json.dumps(data))
+    except Exception as exc:
+        logger.warning("Failed to save voice settings: %s", exc)
+
+
 logger = logging.getLogger(__name__)
 
 router = APIRouter(prefix="/voice", tags=["voice"])
@@ -152,3 +180,58 @@ async def process_voice_input(
         "error": error,
         "spoken": speak_response and response_text is not None,
     }
+
+
+# ── Voice settings UI ────────────────────────────────────────────────────────
+
+
+@router.get("/settings", response_class=HTMLResponse)
+async def voice_settings_page(request: Request):
+    """Render the voice customisation settings page."""
+    current = await asyncio.to_thread(_load_voice_settings)
+    voices: list[dict] = []
+    try:
+        from timmy_serve.voice_tts import voice_tts
+
+        if voice_tts.available:
+            voices = await asyncio.to_thread(voice_tts.get_voices)
+    except Exception as exc:
+        logger.debug("Voice settings page: TTS not available — %s", exc)
+    return templates.TemplateResponse(
+        request,
+        "voice_settings.html",
+        {"settings": current, "voices": voices},
+    )
+
+
+@router.get("/settings/data")
+async def voice_settings_data():
+    """Return current voice settings as JSON."""
+    return await asyncio.to_thread(_load_voice_settings)
+
+
+@router.post("/settings/save")
+async def voice_settings_save(
+    rate: int = Form(175),
+    volume: float = Form(0.9),
+    voice_id: str = Form(""),
+):
+    """Persist voice settings and apply them to the running TTS engine."""
+    rate = max(50, min(400, rate))
+    volume = max(0.0, min(1.0, volume))
+    data = {"rate": rate, "volume": volume, "voice_id": voice_id}
+
+    # Apply to the live TTS engine (graceful degradation when unavailable)
+    try:
+        from timmy_serve.voice_tts import voice_tts
+
+        if voice_tts.available:
+            await asyncio.to_thread(voice_tts.set_rate, rate)
+            await asyncio.to_thread(voice_tts.set_volume, volume)
+            if voice_id:
+                await asyncio.to_thread(voice_tts.set_voice, voice_id)
+    except Exception as exc:
+        logger.warning("Voice settings: failed to apply to TTS engine — %s", exc)
+
+    await asyncio.to_thread(_save_voice_settings, data)
+    return {"saved": True, "settings": data}

src/dashboard/routes/work_orders.py

@@ -51,6 +51,8 @@ def _get_db() -> Generator[sqlite3.Connection, None, None]:
 
 
 class _EnumLike:
+    """Lightweight enum-like wrapper for string values used in templates."""
+
     def __init__(self, v: str):
         self.value = v
 

src/dashboard/services/scorecard_service.py

@@ -23,6 +23,8 @@ TRACKED_AGENTS = frozenset({"hermes", "kimi", "manus", "claude", "gemini"})
 
 
 class PeriodType(StrEnum):
+    """Scorecard reporting period type."""
+
     daily = "daily"
     weekly = "weekly"
 

src/dashboard/templates/base.html

@@ -67,9 +67,11 @@
       <div class="mc-nav-dropdown">
         <button class="mc-test-link mc-dropdown-toggle" aria-expanded="false">INTEL &#x25BE;</button>
         <div class="mc-dropdown-menu">
+          <a href="/nexus" class="mc-test-link">NEXUS</a>
           <a href="/spark/ui" class="mc-test-link">SPARK</a>
           <a href="/memory" class="mc-test-link">MEMORY</a>
           <a href="/marketplace/ui" class="mc-test-link">MARKET</a>
+          <a href="/self-correction/ui" class="mc-test-link">SELF-CORRECT</a>
         </div>
       </div>
       <div class="mc-nav-dropdown">
@@ -88,6 +90,7 @@
           <a href="/lightning/ledger" class="mc-test-link">LEDGER</a>
           <a href="/creative/ui" class="mc-test-link">CREATIVE</a>
           <a href="/voice/button" class="mc-test-link">VOICE</a>
+          <a href="/voice/settings" class="mc-test-link">VOICE SETTINGS</a>
           <a href="/mobile" class="mc-test-link" title="Mobile-optimized view">MOBILE</a>
           <a href="/mobile/local" class="mc-test-link" title="Local AI on iPhone">LOCAL AI</a>
         </div>
@@ -130,6 +133,7 @@
     <a href="/spark/ui" class="mc-mobile-link">SPARK</a>
     <a href="/memory" class="mc-mobile-link">MEMORY</a>
     <a href="/marketplace/ui" class="mc-mobile-link">MARKET</a>
+    <a href="/self-correction/ui" class="mc-mobile-link">SELF-CORRECT</a>
     <div class="mc-mobile-section-label">AGENTS</div>
     <a href="/hands" class="mc-mobile-link">HANDS</a>
     <a href="/work-orders/queue" class="mc-mobile-link">WORK ORDERS</a>
@@ -145,6 +149,7 @@
     <a href="/lightning/ledger" class="mc-mobile-link">LEDGER</a>
     <a href="/creative/ui" class="mc-mobile-link">CREATIVE</a>
     <a href="/voice/button" class="mc-mobile-link">VOICE</a>
+    <a href="/voice/settings" class="mc-mobile-link">VOICE SETTINGS</a>
     <a href="/mobile" class="mc-mobile-link">MOBILE</a>
     <a href="/mobile/local" class="mc-mobile-link">LOCAL AI</a>
     <div class="mc-mobile-menu-footer">

src/dashboard/templates/index.html

@@ -14,6 +14,11 @@
         <div class="mc-loading-placeholder">LOADING...</div>
       {% endcall %}
 
+      <!-- Emotional Profile (HTMX polled) -->
+      {% call panel("EMOTIONAL PROFILE", hx_get="/agents/emotional-profile", hx_trigger="every 10s") %}
+        <div class="mc-loading-placeholder">LOADING...</div>
+      {% endcall %}
+
       <!-- System Health (HTMX polled) -->
       {% call panel("SYSTEM HEALTH", hx_get="/health/status", hx_trigger="every 30s") %}
         <div class="health-row">

src/dashboard/templates/mission_control.html

@@ -186,6 +186,24 @@
   <p class="chat-history-placeholder">Loading sovereignty metrics...</p>
 {% endcall %}
 
+<!-- Agent Scorecards -->
+<div class="card mc-card-spaced" id="mc-scorecards-card">
+    <div class="card-header">
+        <h2 class="card-title">Agent Scorecards</h2>
+        <div class="d-flex align-items-center gap-2">
+            <select id="mc-scorecard-period" class="form-select form-select-sm" style="width: auto;"
+                    onchange="loadMcScorecards()">
+                <option value="daily" selected>Daily</option>
+                <option value="weekly">Weekly</option>
+            </select>
+            <a href="/scorecards" class="btn btn-sm btn-outline-secondary">Full View</a>
+        </div>
+    </div>
+    <div id="mc-scorecards-content" class="p-2">
+        <p class="chat-history-placeholder">Loading scorecards...</p>
+    </div>
+</div>
+
 <!-- Chat History -->
 <div class="card mc-card-spaced">
     <div class="card-header">
@@ -502,6 +520,20 @@ async function loadSparkStatus() {
     }
 }
 
+// Load agent scorecards
+async function loadMcScorecards() {
+    var period = document.getElementById('mc-scorecard-period').value;
+    var container = document.getElementById('mc-scorecards-content');
+    container.innerHTML = '<p class="chat-history-placeholder">Loading scorecards...</p>';
+    try {
+        var response = await fetch('/scorecards/all/panels?period=' + period);
+        var html = await response.text();
+        container.innerHTML = html;
+    } catch (error) {
+        container.innerHTML = '<p class="chat-history-placeholder">Scorecards unavailable</p>';
+    }
+}
+
 // Initial load
 loadSparkStatus();
 loadSovereignty();
@@ -510,6 +542,7 @@ loadSwarmStats();
 loadLightningStats();
 loadGrokStats();
 loadChatHistory();
+loadMcScorecards();
 
 // Periodic updates
 setInterval(loadSovereignty, 30000);
@@ -518,5 +551,6 @@ setInterval(loadSwarmStats, 5000);
 setInterval(updateHeartbeat, 5000);
 setInterval(loadGrokStats, 10000);
 setInterval(loadSparkStatus, 15000);
+setInterval(loadMcScorecards, 300000);
 </script>
 {% endblock %}

src/dashboard/templates/nexus.html

@@ -0,0 +1,122 @@
+{% extends "base.html" %}
+
+{% block title %}Nexus{% endblock %}
+
+{% block extra_styles %}{% endblock %}
+
+{% block content %}
+<div class="container-fluid nexus-layout py-3">
+
+  <div class="nexus-header mb-3">
+    <div class="nexus-title">// NEXUS</div>
+    <div class="nexus-subtitle">
+      Persistent conversational awareness &mdash; always present, always learning.
+    </div>
+  </div>
+
+  <div class="nexus-grid">
+
+    <!-- ── LEFT: Conversation ────────────────────────────────── -->
+    <div class="nexus-chat-col">
+      <div class="card mc-panel nexus-chat-panel">
+        <div class="card-header mc-panel-header d-flex justify-content-between align-items-center">
+          <span>// CONVERSATION</span>
+          <button class="mc-btn mc-btn-sm"
+                  hx-delete="/nexus/history"
+                  hx-target="#nexus-chat-log"
+                  hx-swap="beforeend"
+                  hx-confirm="Clear nexus conversation?">
+            CLEAR
+          </button>
+        </div>
+
+        <div class="card-body p-2" id="nexus-chat-log">
+          {% for msg in messages %}
+          <div class="chat-message {{ 'user' if msg.role == 'user' else 'agent' }}">
+            <div class="msg-meta">
+              {{ 'YOU' if msg.role == 'user' else 'TIMMY' }} // {{ msg.timestamp }}
+            </div>
+            <div class="msg-body {% if msg.role == 'assistant' %}timmy-md{% endif %}">
+              {{ msg.content | e }}
+            </div>
+          </div>
+          {% else %}
+          <div class="nexus-empty-state">
+            Nexus is ready. Start a conversation — memories will surface in real time.
+          </div>
+          {% endfor %}
+        </div>
+
+        <div class="card-footer p-2">
+          <form hx-post="/nexus/chat"
+                hx-target="#nexus-chat-log"
+                hx-swap="beforeend"
+                hx-on::after-request="this.reset(); document.getElementById('nexus-chat-log').scrollTop = 999999;">
+            <div class="d-flex gap-2">
+              <input type="text"
+                     name="message"
+                     id="nexus-input"
+                     class="mc-search-input flex-grow-1"
+                     placeholder="Talk to Timmy..."
+                     autocomplete="off"
+                     required>
+              <button type="submit" class="mc-btn mc-btn-primary">SEND</button>
+            </div>
+          </form>
+        </div>
+      </div>
+    </div>
+
+    <!-- ── RIGHT: Memory sidebar ─────────────────────────────── -->
+    <div class="nexus-sidebar-col">
+
+      <!-- Live memory context (updated with each response) -->
+      <div class="card mc-panel nexus-memory-panel mb-3">
+        <div class="card-header mc-panel-header">
+          <span>// LIVE MEMORY</span>
+          <span class="badge ms-2" style="background:var(--purple-dim); color:var(--purple);">
+            {{ stats.total_entries }} stored
+          </span>
+        </div>
+        <div class="card-body p-2">
+          <div id="nexus-memory-panel" class="nexus-memory-hits">
+            <div class="nexus-memory-label">Relevant memories appear here as you chat.</div>
+          </div>
+        </div>
+      </div>
+
+      <!-- Teaching panel -->
+      <div class="card mc-panel nexus-teach-panel">
+        <div class="card-header mc-panel-header">// TEACH TIMMY</div>
+        <div class="card-body p-2">
+          <form hx-post="/nexus/teach"
+                hx-target="#nexus-teach-response"
+                hx-swap="innerHTML"
+                hx-on::after-request="this.reset()">
+            <div class="d-flex gap-2 mb-2">
+              <input type="text"
+                     name="fact"
+                     class="mc-search-input flex-grow-1"
+                     placeholder="e.g. I prefer dark themes"
+                     required>
+              <button type="submit" class="mc-btn mc-btn-primary">TEACH</button>
+            </div>
+          </form>
+          <div id="nexus-teach-response"></div>
+
+          <div class="nexus-facts-header mt-3">// KNOWN FACTS</div>
+          <ul class="nexus-facts-list" id="nexus-facts-list">
+            {% for fact in facts %}
+            <li class="nexus-fact-item">{{ fact.content | e }}</li>
+            {% else %}
+            <li class="nexus-fact-empty">No personal facts stored yet.</li>
+            {% endfor %}
+          </ul>
+        </div>
+      </div>
+
+    </div><!-- /sidebar -->
+  </div><!-- /nexus-grid -->
+
+</div>
+{% endblock %}

src/dashboard/templates/partials/dreaming_status.html

@@ -1,32 +0,0 @@
-{% if not status.enabled %}
-<div class="dream-disabled text-muted small">Dreaming mode disabled</div>
-{% elif status.dreaming %}
-<div class="dream-active">
-  <span class="dream-pulse"></span>
-  <span class="dream-label">DREAMING</span>
-  <div class="dream-summary">{{ status.current_summary }}</div>
-</div>
-{% elif status.idle %}
-<div class="dream-idle">
-  <span class="dream-dot dream-dot-idle"></span>
-  <span class="dream-label-idle">IDLE</span>
-  <span class="dream-idle-meta">{{ status.idle_minutes }}m — dream cycle pending</span>
-</div>
-{% else %}
-<div class="dream-standby">
-  <span class="dream-dot dream-dot-standby"></span>
-  <span class="dream-label-standby">STANDBY</span>
-  <span class="dream-idle-meta">idle in {{ status.idle_threshold_minutes - status.idle_minutes }}m</span>
-</div>
-{% endif %}
-
-{% if recent_dreams %}
-<div class="dream-history mt-2">
-  {% for d in recent_dreams %}
-  <div class="dream-record">
-    <div class="dream-rule">{{ d.proposed_rule if d.proposed_rule else "No rule extracted" }}</div>
-    <div class="dream-meta">{{ d.created_at[:16] | replace("T", " ") }}</div>
-  </div>
-  {% endfor %}
-</div>
-{% endif %}

src/dashboard/templates/partials/emotional_profile.html

@@ -0,0 +1,37 @@
+{% if not profiles %}
+<div class="mc-muted" style="font-size:11px; padding:4px;">
+  No agents loaded
+</div>
+{% endif %}
+
+{% for p in profiles %}
+{% set color_map = {
+  "cautious": "var(--amber)",
+  "adventurous": "var(--green)",
+  "analytical": "var(--purple)",
+  "frustrated": "var(--red)",
+  "confident": "var(--green)",
+  "curious": "var(--orange)",
+  "calm": "var(--text-dim)"
+} %}
+{% set emo_color = color_map.get(p.current_emotion, "var(--text-dim)") %}
+<div class="mc-emotion-row" style="margin-bottom:8px; padding:6px 8px; border-left:3px solid {{ emo_color }};">
+  <div class="d-flex justify-content-between align-items-center" style="margin-bottom:2px;">
+    <span style="font-size:11px; font-weight:bold; letter-spacing:.08em; color:var(--text-bright);">
+      {{ p.agent_name | upper | e }}
+    </span>
+    <span style="font-size:10px; color:{{ emo_color }}; letter-spacing:.06em;">
+      {{ p.emotion_label | e }}
+    </span>
+  </div>
+  <div style="margin-bottom:4px;">
+    <div style="height:4px; background:var(--bg-deep); border-radius:2px; overflow:hidden;">
+      <div style="height:100%; width:{{ (p.intensity * 100) | int }}%; background:{{ emo_color }}; border-radius:2px; transition:width 0.3s;"></div>
+    </div>
+  </div>
+  <div style="font-size:9px; color:var(--text-dim); letter-spacing:.06em;">
+    {{ p.intensity_label | upper | e }}
+    {% if p.trigger_event %} · {{ p.trigger_event | replace("_", " ") | upper | e }}{% endif %}
+  </div>
+</div>
+{% endfor %}

src/dashboard/templates/partials/nexus_facts.html

@@ -0,0 +1,12 @@
+{% if taught %}
+<div class="nexus-taught-confirm">
+  ✓ Taught: <em>{{ taught | e }}</em>
+</div>
+{% endif %}
+<ul class="nexus-facts-list" id="nexus-facts-list" hx-swap-oob="true">
+  {% for fact in facts %}
+  <li class="nexus-fact-item">{{ fact.content | e }}</li>
+  {% else %}
+  <li class="nexus-fact-empty">No facts stored yet.</li>
+  {% endfor %}
+</ul>

src/dashboard/templates/partials/nexus_message.html

@@ -0,0 +1,36 @@
+{% if user_message %}
+<div class="chat-message user">
+  <div class="msg-meta">YOU // {{ timestamp }}</div>
+  <div class="msg-body">{{ user_message | e }}</div>
+</div>
+{% endif %}
+{% if response %}
+<div class="chat-message agent">
+  <div class="msg-meta">TIMMY // {{ timestamp }}</div>
+  <div class="msg-body timmy-md">{{ response | e }}</div>
+</div>
+<script>
+  (function() {
+    var el = document.currentScript.previousElementSibling.querySelector('.timmy-md');
+    if (el && typeof marked !== 'undefined' && typeof DOMPurify !== 'undefined') {
+      el.innerHTML = DOMPurify.sanitize(marked.parse(el.textContent));
+    }
+  })();
+</script>
+{% elif error %}
+<div class="chat-message error-msg">
+  <div class="msg-meta">SYSTEM // {{ timestamp }}</div>
+  <div class="msg-body">{{ error | e }}</div>
+</div>
+{% endif %}
+{% if memory_hits %}
+<div class="nexus-memory-hits" id="nexus-memory-panel" hx-swap-oob="true">
+  <div class="nexus-memory-label">// LIVE MEMORY CONTEXT</div>
+  {% for hit in memory_hits %}
+  <div class="nexus-memory-hit">
+    <span class="nexus-memory-type">{{ hit.memory_type }}</span>
+    <span class="nexus-memory-content">{{ hit.content | e }}</span>
+  </div>
+  {% endfor %}
+</div>
+{% endif %}

src/dashboard/templates/partials/self_correction_patterns.html

@@ -0,0 +1,28 @@
+{% if patterns %}
+  <table class="mc-table w-100">
+    <thead>
+      <tr>
+        <th>ERROR TYPE</th>
+        <th class="text-center">COUNT</th>
+        <th class="text-center">CORRECTED</th>
+        <th class="text-center">FAILED</th>
+        <th>LAST SEEN</th>
+      </tr>
+    </thead>
+    <tbody>
+      {% for p in patterns %}
+      <tr>
+        <td class="sc-pattern-type">{{ p.error_type }}</td>
+        <td class="text-center">
+          <span class="badge {% if p.count >= 5 %}badge-error{% elif p.count >= 3 %}badge-warning{% else %}badge-info{% endif %}">{{ p.count }}</span>
+        </td>
+        <td class="text-center text-success">{{ p.success_count }}</td>
+        <td class="text-center {% if p.failed_count > 0 %}text-danger{% else %}text-muted{% endif %}">{{ p.failed_count }}</td>
+        <td class="sc-event-time">{{ p.last_seen[:16] if p.last_seen else '—' }}</td>
+      </tr>
+      {% endfor %}
+    </tbody>
+  </table>
+{% else %}
+  <div class="text-center text-muted py-3">No patterns detected yet.</div>
+{% endif %}

src/dashboard/templates/partials/self_correction_timeline.html

@@ -0,0 +1,26 @@
+{% if corrections %}
+  {% for ev in corrections %}
+  <div class="sc-event sc-status-{{ ev.outcome_status }}">
+    <div class="sc-event-header">
+      <span class="sc-status-badge sc-status-{{ ev.outcome_status }}">
+        {% if ev.outcome_status == 'success' %}&#10003; CORRECTED
+        {% elif ev.outcome_status == 'partial' %}&#9679; PARTIAL
+        {% else %}&#10007; FAILED
+        {% endif %}
+      </span>
+      <span class="sc-source-badge">{{ ev.source }}</span>
+      <span class="sc-event-time">{{ ev.created_at[:19] }}</span>
+    </div>
+    <div class="sc-event-error-type">{{ ev.error_type }}</div>
+    <div class="sc-event-intent"><span class="sc-label">INTENT:</span> {{ ev.original_intent[:120] }}{% if ev.original_intent | length > 120 %}&hellip;{% endif %}</div>
+    <div class="sc-event-error"><span class="sc-label">ERROR:</span> {{ ev.detected_error[:120] }}{% if ev.detected_error | length > 120 %}&hellip;{% endif %}</div>
+    <div class="sc-event-strategy"><span class="sc-label">STRATEGY:</span> {{ ev.correction_strategy[:120] }}{% if ev.correction_strategy | length > 120 %}&hellip;{% endif %}</div>
+    <div class="sc-event-outcome"><span class="sc-label">OUTCOME:</span> {{ ev.final_outcome[:120] }}{% if ev.final_outcome | length > 120 %}&hellip;{% endif %}</div>
+    {% if ev.task_id %}
+    <div class="sc-event-meta">task: {{ ev.task_id[:8] }}</div>
+    {% endif %}
+  </div>
+  {% endfor %}
+{% else %}
+  <div class="text-center text-muted py-3">No self-correction events recorded yet.</div>
+{% endif %}

src/dashboard/templates/self_correction.html

@@ -0,0 +1,102 @@
+{% extends "base.html" %}
+{% from "macros.html" import panel %}
+
+{% block title %}Timmy Time — Self-Correction Dashboard{% endblock %}
+
+{% block extra_styles %}{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+
+  <!-- Header -->
+  <div class="spark-header mb-3">
+    <div class="spark-title">SELF-CORRECTION</div>
+    <div class="spark-subtitle">
+      Agent error detection &amp; recovery &mdash;
+      <span class="spark-status-val">{{ stats.total }}</span> events,
+      <span class="spark-status-val">{{ stats.success_rate }}%</span> correction rate,
+      <span class="spark-status-val">{{ stats.unique_error_types }}</span> distinct error types
+    </div>
+  </div>
+
+  <div class="row g-3">
+
+    <!-- Left column: stats + patterns -->
+    <div class="col-12 col-lg-4 d-flex flex-column gap-3">
+
+      <!-- Stats panel -->
+      <div class="card mc-panel">
+        <div class="card-header mc-panel-header">// CORRECTION STATS</div>
+        <div class="card-body p-3">
+          <div class="spark-stat-grid">
+            <div class="spark-stat">
+              <span class="spark-stat-label">TOTAL</span>
+              <span class="spark-stat-value">{{ stats.total }}</span>
+            </div>
+            <div class="spark-stat">
+              <span class="spark-stat-label">CORRECTED</span>
+              <span class="spark-stat-value text-success">{{ stats.success_count }}</span>
+            </div>
+            <div class="spark-stat">
+              <span class="spark-stat-label">PARTIAL</span>
+              <span class="spark-stat-value text-warning">{{ stats.partial_count }}</span>
+            </div>
+            <div class="spark-stat">
+              <span class="spark-stat-label">FAILED</span>
+              <span class="spark-stat-value {% if stats.failed_count > 0 %}text-danger{% else %}text-muted{% endif %}">{{ stats.failed_count }}</span>
+            </div>
+          </div>
+          <div class="mt-3">
+            <div class="d-flex justify-content-between mb-1">
+              <small class="text-muted">Correction Rate</small>
+              <small class="{% if stats.success_rate >= 70 %}text-success{% elif stats.success_rate >= 40 %}text-warning{% else %}text-danger{% endif %}">{{ stats.success_rate }}%</small>
+            </div>
+            <div class="progress" style="height:6px;">
+              <div class="progress-bar {% if stats.success_rate >= 70 %}bg-success{% elif stats.success_rate >= 40 %}bg-warning{% else %}bg-danger{% endif %}"
+                   role="progressbar"
+                   style="width:{{ stats.success_rate }}%"
+                   aria-valuenow="{{ stats.success_rate }}"
+                   aria-valuemin="0"
+                   aria-valuemax="100"></div>
+            </div>
+          </div>
+        </div>
+      </div>
+
+      <!-- Patterns panel -->
+      <div class="card mc-panel"
+           hx-get="/self-correction/patterns"
+           hx-trigger="load, every 60s"
+           hx-target="#sc-patterns-body"
+           hx-swap="innerHTML">
+        <div class="card-header mc-panel-header d-flex justify-content-between align-items-center">
+          <span>// RECURRING PATTERNS</span>
+          <span class="badge badge-info">{{ patterns | length }}</span>
+        </div>
+        <div class="card-body p-0" id="sc-patterns-body">
+          {% include "partials/self_correction_patterns.html" %}
+        </div>
+      </div>
+
+    </div>
+
+    <!-- Right column: timeline -->
+    <div class="col-12 col-lg-8">
+      <div class="card mc-panel"
+           hx-get="/self-correction/timeline"
+           hx-trigger="load, every 30s"
+           hx-target="#sc-timeline-body"
+           hx-swap="innerHTML">
+        <div class="card-header mc-panel-header d-flex justify-content-between align-items-center">
+          <span>// CORRECTION TIMELINE</span>
+          <span class="badge badge-info">{{ corrections | length }}</span>
+        </div>
+        <div class="card-body p-3" id="sc-timeline-body">
+          {% include "partials/self_correction_timeline.html" %}
+        </div>
+      </div>
+    </div>
+
+  </div>
+</div>
+{% endblock %}

src/dashboard/templates/voice_settings.html

@@ -0,0 +1,131 @@
+{% extends "base.html" %}
+{% from "macros.html" import panel %}
+
+{% block title %}Voice Settings{% endblock %}
+{% block extra_styles %}{% endblock %}
+
+{% block content %}
+<div class="voice-settings-page py-3">
+  {% call panel("VOICE SETTINGS") %}
+  <form id="voice-settings-form">
+
+    <div class="vs-field">
+      <label class="vs-label" for="rate-slider">
+        SPEED &mdash; <span class="vs-value" id="rate-val">{{ settings.rate }}</span> WPM
+      </label>
+      <input type="range" class="vs-slider" id="rate-slider" name="rate"
+             min="50" max="400" step="5" value="{{ settings.rate }}"
+             oninput="document.getElementById('rate-val').textContent=this.value">
+      <div class="vs-range-labels"><span>Slow</span><span>Fast</span></div>
+    </div>
+
+    <div class="vs-field">
+      <label class="vs-label" for="vol-slider">
+        VOLUME &mdash; <span class="vs-value" id="vol-val">{{ (settings.volume * 100)|int }}</span>%
+      </label>
+      <input type="range" class="vs-slider" id="vol-slider" name="volume"
+             min="0" max="100" step="5" value="{{ (settings.volume * 100)|int }}"
+             oninput="document.getElementById('vol-val').textContent=this.value">
+      <div class="vs-range-labels"><span>Quiet</span><span>Loud</span></div>
+    </div>
+
+    <div class="vs-field">
+      <label class="vs-label" for="voice-select">VOICE MODEL</label>
+      {% if voices %}
+      <select class="vs-select" id="voice-select" name="voice_id">
+        <option value="">&#8212; System Default &#8212;</option>
+        {% for v in voices %}
+        <option value="{{ v.id }}" {% if v.id == settings.voice_id %}selected{% endif %}>
+          {{ v.name }}
+        </option>
+        {% endfor %}
+      </select>
+      {% else %}
+      <div class="vs-unavailable">Server TTS (pyttsx3) unavailable &mdash; preview uses browser speech synthesis</div>
+      <input type="hidden" id="voice-select" name="voice_id" value="{{ settings.voice_id }}">
+      {% endif %}
+    </div>
+
+    <div class="vs-field">
+      <label class="vs-label" for="preview-text">PREVIEW TEXT</label>
+      <input type="text" class="vs-input" id="preview-text"
+             value="Hello, I am Timmy. Your local AI assistant."
+             placeholder="Enter text to preview...">
+    </div>
+
+    <div class="vs-actions">
+      <button type="button" class="vs-btn-preview" id="preview-btn" onclick="previewVoice()">
+        &#9654; PREVIEW
+      </button>
+      <button type="button" class="vs-btn-save" id="save-btn" onclick="saveSettings()">
+        SAVE SETTINGS
+      </button>
+    </div>
+
+  </form>
+  {% endcall %}
+</div>
+
+<script>
+function previewVoice() {
+  var text = document.getElementById('preview-text').value.trim() ||
+             'Hello, I am Timmy. Your local AI assistant.';
+  var rate   = parseInt(document.getElementById('rate-slider').value, 10);
+  var volume = parseInt(document.getElementById('vol-slider').value, 10) / 100;
+
+  if (!('speechSynthesis' in window)) {
+    McToast.show('Speech synthesis not supported in this browser', 'warn');
+    return;
+  }
+
+  window.speechSynthesis.cancel();
+  var utterance = new SpeechSynthesisUtterance(text);
+  // Web Speech API rate: 1.0 ≈ 175 WPM (default)
+  utterance.rate   = rate / 175;
+  utterance.volume = volume;
+
+  // Best-effort voice match from server selection
+  var voiceSelect = document.getElementById('voice-select');
+  if (voiceSelect && voiceSelect.value) {
+    var selectedText = voiceSelect.options[voiceSelect.selectedIndex].text.toLowerCase();
+    var firstWord = selectedText.split(' ')[0];
+    var browserVoices = window.speechSynthesis.getVoices();
+    var matched = browserVoices.find(function(v) {
+      return v.name.toLowerCase().includes(firstWord);
+    });
+    if (matched) { utterance.voice = matched; }
+  }
+
+  window.speechSynthesis.speak(utterance);
+  McToast.show('Playing preview\u2026', 'info');
+}
+
+async function saveSettings() {
+  var rate    = document.getElementById('rate-slider').value;
+  var volPct  = parseInt(document.getElementById('vol-slider').value, 10);
+  var voiceId = document.getElementById('voice-select').value;
+
+  var body = new URLSearchParams({
+    rate:     rate,
+    volume:   (volPct / 100).toFixed(2),
+    voice_id: voiceId
+  });
+
+  try {
+    var resp = await fetch('/voice/settings/save', {
+      method:  'POST',
+      headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
+      body:    body.toString()
+    });
+    var data = await resp.json();
+    if (data.saved) {
+      McToast.show('Voice settings saved.', 'info');
+    } else {
+      McToast.show('Failed to save settings.', 'error');
+    }
+  } catch (e) {
+    McToast.show('Error saving settings.', 'error');
+  }
+}
+</script>
+{% endblock %}

src/infrastructure/chat_store.py

@@ -24,6 +24,8 @@ MAX_MESSAGES: int = 500
 
 @dataclass
 class Message:
+    """A single chat message with role, content, timestamp, and source."""
+
     role: str  # "user" | "agent" | "error"
     content: str
     timestamp: str

src/infrastructure/energy/__init__.py

@@ -0,0 +1,8 @@
+"""Energy Budget Monitoring — power-draw estimation for LLM inference.
+
+Refs: #1009
+"""
+
+from infrastructure.energy.monitor import EnergyBudgetMonitor, energy_monitor
+
+__all__ = ["EnergyBudgetMonitor", "energy_monitor"]

src/infrastructure/energy/monitor.py

@@ -0,0 +1,371 @@
+"""Energy Budget Monitor — estimates GPU/CPU power draw during LLM inference.
+
+Tracks estimated power consumption to optimize for "metabolic efficiency".
+Three estimation strategies attempted in priority order:
+
+  1. Battery discharge via ioreg (macOS — works without sudo, on-battery only)
+  2. CPU utilisation proxy via sysctl hw.cpufrequency + top
+  3. Model-size heuristic (tokens/s × model_size_gb × 2W/GB estimate)
+
+Energy Efficiency score (0–10):
+  efficiency = tokens_per_second / estimated_watts, normalised to 0–10.
+
+Low Power Mode:
+  Activated manually or automatically when draw exceeds the configured
+  threshold.  In low power mode the cascade router is advised to prefer the
+  configured low_power_model (e.g. qwen3:1b or similar compact model).
+
+Refs: #1009
+"""
+
+import asyncio
+import json
+import logging
+import subprocess
+import time
+from collections import deque
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import Any
+
+from config import settings
+
+logger = logging.getLogger(__name__)
+
+# Approximate model-size lookup (GB) used for heuristic power estimate.
+# Keys are lowercase substring matches against the model name.
+_MODEL_SIZE_GB: dict[str, float] = {
+    "qwen3:1b": 0.8,
+    "qwen3:3b": 2.0,
+    "qwen3:4b": 2.5,
+    "qwen3:8b": 5.5,
+    "qwen3:14b": 9.0,
+    "qwen3:30b": 20.0,
+    "qwen3:32b": 20.0,
+    "llama3:8b": 5.5,
+    "llama3:70b": 45.0,
+    "mistral:7b": 4.5,
+    "gemma3:4b": 2.5,
+    "gemma3:12b": 8.0,
+    "gemma3:27b": 17.0,
+    "phi4:14b": 9.0,
+}
+_DEFAULT_MODEL_SIZE_GB = 5.0  # fallback when model not in table
+_WATTS_PER_GB_HEURISTIC = 2.0  # rough W/GB for Apple Silicon unified memory
+
+# Efficiency score normalisation: score 10 at this efficiency (tok/s per W).
+_EFFICIENCY_SCORE_CEILING = 5.0  # tok/s per W → score 10
+
+# Rolling window for recent samples
+_HISTORY_MAXLEN = 60
+
+
+@dataclass
+class InferenceSample:
+    """A single inference event captured by record_inference()."""
+
+    timestamp: str
+    model: str
+    tokens_per_second: float
+    estimated_watts: float
+    efficiency: float  # tokens/s per watt
+    efficiency_score: float  # 0–10
+
+
+@dataclass
+class EnergyReport:
+    """Snapshot of current energy budget state."""
+
+    timestamp: str
+    low_power_mode: bool
+    current_watts: float
+    strategy: str  # "battery", "cpu_proxy", "heuristic", "unavailable"
+    efficiency_score: float  # 0–10; -1 if no inference samples yet
+    recent_samples: list[InferenceSample]
+    recommendation: str
+    details: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "timestamp": self.timestamp,
+            "low_power_mode": self.low_power_mode,
+            "current_watts": round(self.current_watts, 2),
+            "strategy": self.strategy,
+            "efficiency_score": round(self.efficiency_score, 2),
+            "recent_samples": [
+                {
+                    "timestamp": s.timestamp,
+                    "model": s.model,
+                    "tokens_per_second": round(s.tokens_per_second, 1),
+                    "estimated_watts": round(s.estimated_watts, 2),
+                    "efficiency": round(s.efficiency, 3),
+                    "efficiency_score": round(s.efficiency_score, 2),
+                }
+                for s in self.recent_samples
+            ],
+            "recommendation": self.recommendation,
+            "details": self.details,
+        }
+
+
+class EnergyBudgetMonitor:
+    """Estimates power consumption and tracks LLM inference efficiency.
+
+    All blocking I/O (subprocess calls) is wrapped in asyncio.to_thread()
+    so the event loop is never blocked.  Results are cached.
+
+    Usage::
+
+        # Record an inference event
+        energy_monitor.record_inference("qwen3:8b", tokens_per_second=42.0)
+
+        # Get the current report
+        report = await energy_monitor.get_report()
+
+        # Toggle low power mode
+        energy_monitor.set_low_power_mode(True)
+    """
+
+    _POWER_CACHE_TTL = 10.0  # seconds between fresh power readings
+
+    def __init__(self) -> None:
+        self._low_power_mode: bool = False
+        self._samples: deque[InferenceSample] = deque(maxlen=_HISTORY_MAXLEN)
+        self._cached_watts: float = 0.0
+        self._cached_strategy: str = "unavailable"
+        self._cache_ts: float = 0.0
+
+    # ── Public API ────────────────────────────────────────────────────────────
+
+    @property
+    def low_power_mode(self) -> bool:
+        return self._low_power_mode
+
+    def set_low_power_mode(self, enabled: bool) -> None:
+        """Enable or disable low power mode."""
+        self._low_power_mode = enabled
+        state = "enabled" if enabled else "disabled"
+        logger.info("Energy budget: low power mode %s", state)
+
+    def record_inference(self, model: str, tokens_per_second: float) -> InferenceSample:
+        """Record an inference event for efficiency tracking.
+
+        Call this after each LLM inference completes with the model name and
+        measured throughput.  The current power estimate is used to compute
+        the efficiency score.
+
+        Args:
+            model:              Ollama model name (e.g. "qwen3:8b").
+            tokens_per_second:  Measured decode throughput.
+
+        Returns:
+            The recorded InferenceSample.
+        """
+        watts = self._cached_watts if self._cached_watts > 0 else self._estimate_watts_sync(model)
+        efficiency = tokens_per_second / max(watts, 0.1)
+        score = min(10.0, (efficiency / _EFFICIENCY_SCORE_CEILING) * 10.0)
+
+        sample = InferenceSample(
+            timestamp=datetime.now(UTC).isoformat(),
+            model=model,
+            tokens_per_second=tokens_per_second,
+            estimated_watts=watts,
+            efficiency=efficiency,
+            efficiency_score=score,
+        )
+        self._samples.append(sample)
+
+        # Auto-engage low power mode if above threshold and budget is enabled
+        threshold = getattr(settings, "energy_budget_watts_threshold", 15.0)
+        if watts > threshold and not self._low_power_mode:
+            logger.info(
+                "Energy budget: %.1fW exceeds threshold %.1fW — auto-engaging low power mode",
+                watts,
+                threshold,
+            )
+            self.set_low_power_mode(True)
+
+        return sample
+
+    async def get_report(self) -> EnergyReport:
+        """Return the current energy budget report.
+
+        Refreshes the power estimate if the cache is stale.
+        """
+        await self._refresh_power_cache()
+
+        score = self._compute_mean_efficiency_score()
+        recommendation = self._build_recommendation(score)
+
+        return EnergyReport(
+            timestamp=datetime.now(UTC).isoformat(),
+            low_power_mode=self._low_power_mode,
+            current_watts=self._cached_watts,
+            strategy=self._cached_strategy,
+            efficiency_score=score,
+            recent_samples=list(self._samples)[-10:],
+            recommendation=recommendation,
+            details={"sample_count": len(self._samples)},
+        )
+
+    # ── Power estimation ──────────────────────────────────────────────────────
+
+    async def _refresh_power_cache(self) -> None:
+        """Refresh the cached power reading if stale."""
+        now = time.monotonic()
+        if now - self._cache_ts < self._POWER_CACHE_TTL:
+            return
+
+        try:
+            watts, strategy = await asyncio.to_thread(self._read_power)
+        except Exception as exc:
+            logger.debug("Energy: power read failed: %s", exc)
+            watts, strategy = 0.0, "unavailable"
+
+        self._cached_watts = watts
+        self._cached_strategy = strategy
+        self._cache_ts = now
+
+    def _read_power(self) -> tuple[float, str]:
+        """Synchronous power reading — tries strategies in priority order.
+
+        Returns:
+            Tuple of (watts, strategy_name).
+        """
+        # Strategy 1: battery discharge via ioreg (on-battery Macs)
+        try:
+            watts = self._read_battery_watts()
+            if watts > 0:
+                return watts, "battery"
+        except Exception:
+            pass
+
+        # Strategy 2: CPU utilisation proxy via top
+        try:
+            cpu_pct = self._read_cpu_pct()
+            if cpu_pct >= 0:
+                # M3 Max TDP ≈ 40W; scale linearly
+                watts = (cpu_pct / 100.0) * 40.0
+                return watts, "cpu_proxy"
+        except Exception:
+            pass
+
+        # Strategy 3: heuristic from loaded model size
+        return 0.0, "unavailable"
+
+    def _estimate_watts_sync(self, model: str) -> float:
+        """Estimate watts from model size when no live reading is available."""
+        size_gb = self._model_size_gb(model)
+        return size_gb * _WATTS_PER_GB_HEURISTIC
+
+    def _read_battery_watts(self) -> float:
+        """Read instantaneous battery discharge via ioreg.
+
+        Returns watts if on battery, 0.0 if plugged in or unavailable.
+        Requires macOS; no sudo needed.
+        """
+        result = subprocess.run(
+            ["ioreg", "-r", "-c", "AppleSmartBattery", "-d", "1"],
+            capture_output=True,
+            text=True,
+            timeout=3,
+        )
+        amperage_ma = 0.0
+        voltage_mv = 0.0
+        is_charging = True  # assume charging unless we see ExternalConnected = No
+
+        for line in result.stdout.splitlines():
+            stripped = line.strip()
+            if '"InstantAmperage"' in stripped:
+                try:
+                    amperage_ma = float(stripped.split("=")[-1].strip())
+                except ValueError:
+                    pass
+            elif '"Voltage"' in stripped:
+                try:
+                    voltage_mv = float(stripped.split("=")[-1].strip())
+                except ValueError:
+                    pass
+            elif '"ExternalConnected"' in stripped:
+                is_charging = "Yes" in stripped
+
+        if is_charging or voltage_mv == 0 or amperage_ma <= 0:
+            return 0.0
+
+        # ioreg reports amperage in mA, voltage in mV
+        return (abs(amperage_ma) * voltage_mv) / 1_000_000
+
+    def _read_cpu_pct(self) -> float:
+        """Read CPU utilisation from macOS top.
+
+        Returns aggregate CPU% (0–100), or -1.0 on failure.
+        """
+        result = subprocess.run(
+            ["top", "-l", "1", "-n", "0", "-stats", "cpu"],
+            capture_output=True,
+            text=True,
+            timeout=5,
+        )
+        for line in result.stdout.splitlines():
+            if "CPU usage:" in line:
+                # "CPU usage: 12.5% user, 8.3% sys, 79.1% idle"
+                parts = line.split()
+                try:
+                    user = float(parts[2].rstrip("%"))
+                    sys_ = float(parts[4].rstrip("%"))
+                    return user + sys_
+                except (IndexError, ValueError):
+                    pass
+        return -1.0
+
+    # ── Helpers ───────────────────────────────────────────────────────────────
+
+    @staticmethod
+    def _model_size_gb(model: str) -> float:
+        """Look up approximate model size in GB by name substring."""
+        lower = model.lower()
+        # Exact match first
+        if lower in _MODEL_SIZE_GB:
+            return _MODEL_SIZE_GB[lower]
+        # Substring match
+        for key, size in _MODEL_SIZE_GB.items():
+            if key in lower:
+                return size
+        return _DEFAULT_MODEL_SIZE_GB
+
+    def _compute_mean_efficiency_score(self) -> float:
+        """Mean efficiency score over recent samples, or -1 if none."""
+        if not self._samples:
+            return -1.0
+        recent = list(self._samples)[-10:]
+        return sum(s.efficiency_score for s in recent) / len(recent)
+
+    def _build_recommendation(self, score: float) -> str:
+        """Generate a human-readable recommendation from the efficiency score."""
+        threshold = getattr(settings, "energy_budget_watts_threshold", 15.0)
+        low_power_model = getattr(settings, "energy_low_power_model", "qwen3:1b")
+
+        if score < 0:
+            return "No inference data yet — run some tasks to populate efficiency metrics."
+
+        if self._low_power_mode:
+            return (
+                f"Low power mode active — routing to {low_power_model}. "
+                "Disable when power draw normalises."
+            )
+
+        if score < 3.0:
+            return (
+                f"Low efficiency (score {score:.1f}/10). "
+                f"Consider enabling low power mode to favour smaller models "
+                f"(threshold: {threshold}W)."
+            )
+
+        if score < 6.0:
+            return f"Moderate efficiency (score {score:.1f}/10). System operating normally."
+
+        return f"Good efficiency (score {score:.1f}/10). No action needed."
+
+
+# Module-level singleton
+energy_monitor = EnergyBudgetMonitor()

src/infrastructure/hands/git.py

@@ -71,6 +71,53 @@ class GitHand:
                 return True
         return False
 
+    async def _exec_subprocess(
+        self,
+        args: str,
+        timeout: int,
+    ) -> tuple[bytes, bytes, int]:
+        """Run git as a subprocess, return (stdout, stderr, returncode).
+
+        Raises TimeoutError if the process exceeds *timeout* seconds.
+        """
+        proc = await asyncio.create_subprocess_exec(
+            "git",
+            *args.split(),
+            stdout=asyncio.subprocess.PIPE,
+            stderr=asyncio.subprocess.PIPE,
+            cwd=self._repo_dir,
+        )
+        try:
+            stdout, stderr = await asyncio.wait_for(
+                proc.communicate(),
+                timeout=timeout,
+            )
+        except TimeoutError:
+            proc.kill()
+            await proc.wait()
+            raise
+        return stdout, stderr, proc.returncode or 0
+
+    @staticmethod
+    def _parse_output(
+        command: str,
+        stdout_bytes: bytes,
+        stderr_bytes: bytes,
+        returncode: int | None,
+        latency_ms: float,
+    ) -> GitResult:
+        """Decode subprocess output into a GitResult."""
+        exit_code = returncode or 0
+        stdout = stdout_bytes.decode("utf-8", errors="replace").strip()
+        stderr = stderr_bytes.decode("utf-8", errors="replace").strip()
+        return GitResult(
+            operation=command,
+            success=exit_code == 0,
+            output=stdout,
+            error=stderr if exit_code != 0 else "",
+            latency_ms=latency_ms,
+        )
+
     async def run(
         self,
         args: str,
@@ -88,14 +135,15 @@ class GitHand:
             GitResult with output or error details.
         """
         start = time.time()
+        command = f"git {args}"
 
         # Gate destructive operations
         if self._is_destructive(args) and not allow_destructive:
             return GitResult(
-                operation=f"git {args}",
+                operation=command,
                 success=False,
                 error=(
-                    f"Destructive operation blocked: 'git {args}'. "
+                    f"Destructive operation blocked: '{command}'. "
                     "Set allow_destructive=True to override."
                 ),
                 requires_confirmation=True,
@@ -103,46 +151,21 @@ class GitHand:
             )
 
         effective_timeout = timeout or self._timeout
-        command = f"git {args}"
 
         try:
-            proc = await asyncio.create_subprocess_exec(
-                "git",
-                *args.split(),
-                stdout=asyncio.subprocess.PIPE,
-                stderr=asyncio.subprocess.PIPE,
-                cwd=self._repo_dir,
+            stdout_bytes, stderr_bytes, returncode = await self._exec_subprocess(
+                args,
+                effective_timeout,
             )
-
-            try:
-                stdout_bytes, stderr_bytes = await asyncio.wait_for(
-                    proc.communicate(), timeout=effective_timeout
-                )
-            except TimeoutError:
-                proc.kill()
-                await proc.wait()
-                latency = (time.time() - start) * 1000
-                logger.warning("Git command timed out after %ds: %s", effective_timeout, command)
-                return GitResult(
-                    operation=command,
-                    success=False,
-                    error=f"Command timed out after {effective_timeout}s",
-                    latency_ms=latency,
-                )
-
+        except TimeoutError:
             latency = (time.time() - start) * 1000
-            exit_code = proc.returncode or 0
-            stdout = stdout_bytes.decode("utf-8", errors="replace").strip()
-            stderr = stderr_bytes.decode("utf-8", errors="replace").strip()
-
+            logger.warning("Git command timed out after %ds: %s", effective_timeout, command)
             return GitResult(
                 operation=command,
-                success=exit_code == 0,
-                output=stdout,
-                error=stderr if exit_code != 0 else "",
+                success=False,
+                error=f"Command timed out after {effective_timeout}s",
                 latency_ms=latency,
             )
-
         except FileNotFoundError:
             latency = (time.time() - start) * 1000
             logger.warning("git binary not found")
@@ -162,6 +185,14 @@ class GitHand:
                 latency_ms=latency,
             )
 
+        return self._parse_output(
+            command,
+            stdout_bytes,
+            stderr_bytes,
+            returncode=returncode,
+            latency_ms=(time.time() - start) * 1000,
+        )
+
     # ── Convenience wrappers ─────────────────────────────────────────────────
 
     async def status(self) -> GitResult:

src/infrastructure/hermes/__init__.py

@@ -0,0 +1,9 @@
+"""Hermes health monitor — system resources + model management.
+
+Monitors the local machine (Hermes/M3 Max) for memory pressure, disk usage,
+Ollama model health, zombie processes, and network connectivity.
+"""
+
+from infrastructure.hermes.monitor import HealthLevel, HealthReport, HermesMonitor, hermes_monitor
+
+__all__ = ["HermesMonitor", "HealthLevel", "HealthReport", "hermes_monitor"]

src/infrastructure/hermes/monitor.py

@@ -0,0 +1,660 @@
+"""Hermes health monitor — system resources + model management.
+
+Monitors the local machine (Hermes/M3 Max) and keeps it running smoothly.
+Runs every 5 minutes, auto-resolves issues where possible, alerts when
+human intervention is needed.
+
+Monitors:
+  1. Memory pressure — unified memory, alert if <4GB free, unload models
+  2. Disk usage — alert if <10GB free, clean temp files
+  3. Ollama status — verify reachable, restart if crashed, manage loaded models
+  4. Process health — detect zombie processes
+  5. Network — verify Gitea connectivity
+
+Refs: #1073
+"""
+
+import asyncio
+import json
+import logging
+import shutil
+import subprocess
+import tempfile
+import time
+import urllib.request
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from enum import StrEnum
+from typing import Any
+
+from config import settings
+
+logger = logging.getLogger(__name__)
+
+
+class HealthLevel(StrEnum):
+    """Severity level for a health check result."""
+
+    OK = "ok"
+    WARNING = "warning"
+    CRITICAL = "critical"
+    UNKNOWN = "unknown"
+
+
+@dataclass
+class CheckResult:
+    """Result of a single health check."""
+
+    name: str
+    level: HealthLevel
+    message: str
+    details: dict[str, Any] = field(default_factory=dict)
+    auto_resolved: bool = False
+    needs_human: bool = False
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "level": self.level.value,
+            "message": self.message,
+            "details": self.details,
+            "auto_resolved": self.auto_resolved,
+            "needs_human": self.needs_human,
+        }
+
+
+@dataclass
+class HealthReport:
+    """Full health report from a single monitor cycle."""
+
+    timestamp: str
+    checks: list[CheckResult]
+    overall: HealthLevel
+
+    @property
+    def has_issues(self) -> bool:
+        return any(c.level != HealthLevel.OK for c in self.checks)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "timestamp": self.timestamp,
+            "overall": self.overall.value,
+            "has_issues": self.has_issues,
+            "checks": [c.to_dict() for c in self.checks],
+        }
+
+
+class HermesMonitor:
+    """System health monitor for Hermes (local M3 Max machine).
+
+    All blocking I/O (subprocess, HTTP) is wrapped in asyncio.to_thread()
+    so it never blocks the event loop.  Results are cached so the dashboard
+    can read the last report without triggering a new cycle.
+    """
+
+    OLLAMA_REQUEST_TIMEOUT = 5
+    NETWORK_REQUEST_TIMEOUT = 5
+
+    def __init__(self) -> None:
+        self._last_report: HealthReport | None = None
+        self._last_run_ts: float = 0.0
+
+    @property
+    def last_report(self) -> HealthReport | None:
+        """Most recent health report, or None if no cycle has run yet."""
+        return self._last_report
+
+    @property
+    def seconds_since_last_run(self) -> float:
+        if self._last_run_ts == 0.0:
+            return float("inf")
+        return time.monotonic() - self._last_run_ts
+
+    async def run_cycle(self) -> HealthReport:
+        """Run a full health check cycle and return the report."""
+        self._last_run_ts = time.monotonic()
+        logger.info("Hermes health cycle starting")
+
+        check_fns = [
+            self._check_memory(),
+            self._check_disk(),
+            self._check_ollama(),
+            self._check_processes(),
+            self._check_network(),
+        ]
+
+        raw_results = await asyncio.gather(*check_fns, return_exceptions=True)
+
+        checks: list[CheckResult] = []
+        for i, r in enumerate(raw_results):
+            if isinstance(r, Exception):
+                name = ["memory", "disk", "ollama", "processes", "network"][i]
+                logger.warning("Hermes check '%s' raised: %s", name, r)
+                checks.append(
+                    CheckResult(
+                        name=name,
+                        level=HealthLevel.UNKNOWN,
+                        message=f"Check error: {r}",
+                    )
+                )
+            else:
+                checks.append(r)
+
+        # Compute overall level
+        levels = {c.level for c in checks}
+        if HealthLevel.CRITICAL in levels:
+            overall = HealthLevel.CRITICAL
+        elif HealthLevel.WARNING in levels:
+            overall = HealthLevel.WARNING
+        elif HealthLevel.UNKNOWN in levels:
+            overall = HealthLevel.UNKNOWN
+        else:
+            overall = HealthLevel.OK
+
+        report = HealthReport(
+            timestamp=datetime.now(UTC).isoformat(),
+            checks=checks,
+            overall=overall,
+        )
+        self._last_report = report
+
+        await self._handle_alerts(report)
+
+        logger.info("Hermes health cycle complete — overall: %s", overall.value)
+        return report
+
+    # ── Memory ───────────────────────────────────────────────────────────────
+
+    async def _check_memory(self) -> CheckResult:
+        """Check unified memory usage (macOS vm_stat)."""
+        memory_free_min_gb = getattr(settings, "hermes_memory_free_min_gb", 4.0)
+        try:
+            info = await asyncio.to_thread(self._get_memory_info)
+            free_gb = info.get("free_gb", 0.0)
+            total_gb = info.get("total_gb", 0.0)
+            details: dict[str, Any] = {
+                "free_gb": round(free_gb, 2),
+                "total_gb": round(total_gb, 2),
+            }
+
+            if free_gb < memory_free_min_gb:
+                # Attempt auto-remediation: unload Ollama models
+                unloaded = await self._unload_ollama_models()
+                if unloaded:
+                    return CheckResult(
+                        name="memory",
+                        level=HealthLevel.WARNING,
+                        message=(
+                            f"Low memory ({free_gb:.1f}GB free) — "
+                            f"unloaded {unloaded} Ollama model(s)"
+                        ),
+                        details={**details, "models_unloaded": unloaded},
+                        auto_resolved=True,
+                    )
+                return CheckResult(
+                    name="memory",
+                    level=HealthLevel.CRITICAL,
+                    message=(
+                        f"Critical: only {free_gb:.1f}GB free (threshold: {memory_free_min_gb}GB)"
+                    ),
+                    details=details,
+                    needs_human=True,
+                )
+
+            return CheckResult(
+                name="memory",
+                level=HealthLevel.OK,
+                message=f"Memory OK — {free_gb:.1f}GB free of {total_gb:.1f}GB",
+                details=details,
+            )
+        except Exception as exc:
+            logger.warning("Memory check failed: %s", exc)
+            return CheckResult(
+                name="memory",
+                level=HealthLevel.UNKNOWN,
+                message=f"Memory check unavailable: {exc}",
+            )
+
+    def _get_memory_info(self) -> dict[str, float]:
+        """Get memory stats via macOS sysctl + vm_stat.
+
+        Falls back gracefully on non-macOS systems.
+        """
+        gb = 1024**3
+        total_bytes = 0.0
+        free_bytes = 0.0
+
+        # Total memory via sysctl
+        try:
+            result = subprocess.run(
+                ["sysctl", "-n", "hw.memsize"],
+                capture_output=True,
+                text=True,
+                timeout=3,
+            )
+            total_bytes = float(result.stdout.strip())
+        except Exception:
+            pass
+
+        # Free + inactive pages via vm_stat (macOS)
+        try:
+            result = subprocess.run(
+                ["vm_stat"],
+                capture_output=True,
+                text=True,
+                timeout=3,
+            )
+            page_size = 16384  # 16 KB default on Apple Silicon
+            for line in result.stdout.splitlines():
+                if "page size of" in line:
+                    parts = line.split()
+                    for i, part in enumerate(parts):
+                        if part == "of" and i + 1 < len(parts):
+                            try:
+                                page_size = int(parts[i + 1])
+                            except ValueError:
+                                pass
+                elif "Pages free:" in line:
+                    pages = int(line.split(":")[1].strip().rstrip("."))
+                    free_bytes += pages * page_size
+                elif "Pages inactive:" in line:
+                    pages = int(line.split(":")[1].strip().rstrip("."))
+                    free_bytes += pages * page_size
+        except Exception:
+            pass
+
+        return {
+            "total_gb": total_bytes / gb if total_bytes else 0.0,
+            "free_gb": free_bytes / gb if free_bytes else 0.0,
+        }
+
+    # ── Disk ─────────────────────────────────────────────────────────────────
+
+    async def _check_disk(self) -> CheckResult:
+        """Check disk usage via shutil.disk_usage."""
+        disk_free_min_gb = getattr(settings, "hermes_disk_free_min_gb", 10.0)
+        try:
+            usage = await asyncio.to_thread(shutil.disk_usage, "/")
+            free_gb = usage.free / (1024**3)
+            total_gb = usage.total / (1024**3)
+            used_pct = (usage.used / usage.total) * 100
+
+            details: dict[str, Any] = {
+                "free_gb": round(free_gb, 2),
+                "total_gb": round(total_gb, 2),
+                "used_pct": round(used_pct, 1),
+            }
+
+            if free_gb < disk_free_min_gb:
+                cleaned_gb = await self._cleanup_temp_files()
+                if cleaned_gb > 0.01:
+                    return CheckResult(
+                        name="disk",
+                        level=HealthLevel.WARNING,
+                        message=(
+                            f"Low disk ({free_gb:.1f}GB free) — "
+                            f"cleaned {cleaned_gb:.2f}GB from /tmp"
+                        ),
+                        details={**details, "cleaned_gb": round(cleaned_gb, 2)},
+                        auto_resolved=True,
+                    )
+                return CheckResult(
+                    name="disk",
+                    level=HealthLevel.CRITICAL,
+                    message=(
+                        f"Critical: only {free_gb:.1f}GB free (threshold: {disk_free_min_gb}GB)"
+                    ),
+                    details=details,
+                    needs_human=True,
+                )
+
+            return CheckResult(
+                name="disk",
+                level=HealthLevel.OK,
+                message=f"Disk OK — {free_gb:.1f}GB free ({used_pct:.0f}% used)",
+                details=details,
+            )
+        except Exception as exc:
+            logger.warning("Disk check failed: %s", exc)
+            return CheckResult(
+                name="disk",
+                level=HealthLevel.UNKNOWN,
+                message=f"Disk check unavailable: {exc}",
+            )
+
+    async def _cleanup_temp_files(self) -> float:
+        """Remove /tmp files older than 24 hours. Returns GB freed."""
+        return await asyncio.to_thread(self._cleanup_temp_files_sync)
+
+    def _cleanup_temp_files_sync(self) -> float:
+        """Synchronous /tmp cleanup — only touches files older than 24 hours."""
+        from pathlib import Path
+
+        freed_bytes = 0
+        cutoff = time.time() - 86400  # 24 hours ago
+
+        try:
+            tmp = Path(tempfile.gettempdir())
+            for item in tmp.iterdir():
+                try:
+                    stat = item.stat()
+                    if stat.st_mtime >= cutoff:
+                        continue
+                    if item.is_file():
+                        freed_bytes += stat.st_size
+                        item.unlink(missing_ok=True)
+                    elif item.is_dir():
+                        dir_size = sum(f.stat().st_size for f in item.rglob("*") if f.is_file())
+                        freed_bytes += dir_size
+                        shutil.rmtree(str(item), ignore_errors=True)
+                except (PermissionError, OSError):
+                    pass  # Skip files we can't touch
+        except Exception as exc:
+            logger.warning("Temp cleanup error: %s", exc)
+
+        freed_gb = freed_bytes / (1024**3)
+        if freed_gb > 0.001:
+            logger.info("Hermes disk cleanup: freed %.2fGB from /tmp", freed_gb)
+        return freed_gb
+
+    # ── Ollama ───────────────────────────────────────────────────────────────
+
+    async def _check_ollama(self) -> CheckResult:
+        """Check Ollama status and loaded models."""
+        try:
+            status = await asyncio.to_thread(self._get_ollama_status)
+
+            if not status.get("reachable"):
+                restarted = await self._restart_ollama()
+                if restarted:
+                    return CheckResult(
+                        name="ollama",
+                        level=HealthLevel.WARNING,
+                        message="Ollama was unreachable — restart initiated",
+                        details={"restart_attempted": True},
+                        auto_resolved=True,
+                    )
+                return CheckResult(
+                    name="ollama",
+                    level=HealthLevel.CRITICAL,
+                    message="Ollama unreachable and restart failed",
+                    details={"reachable": False},
+                    needs_human=True,
+                )
+
+            models = status.get("models", [])
+            loaded = status.get("loaded_models", [])
+            return CheckResult(
+                name="ollama",
+                level=HealthLevel.OK,
+                message=(f"Ollama OK — {len(models)} model(s) available, {len(loaded)} loaded"),
+                details={
+                    "reachable": True,
+                    "model_count": len(models),
+                    "loaded_count": len(loaded),
+                    "loaded_models": [m.get("name", "") for m in loaded],
+                },
+            )
+        except Exception as exc:
+            logger.warning("Ollama check failed: %s", exc)
+            return CheckResult(
+                name="ollama",
+                level=HealthLevel.UNKNOWN,
+                message=f"Ollama check failed: {exc}",
+            )
+
+    def _get_ollama_status(self) -> dict[str, Any]:
+        """Synchronous Ollama status — checks /api/tags and /api/ps."""
+        url = settings.normalized_ollama_url
+
+        try:
+            req = urllib.request.Request(
+                f"{url}/api/tags",
+                method="GET",
+                headers={"Accept": "application/json"},
+            )
+            with urllib.request.urlopen(req, timeout=self.OLLAMA_REQUEST_TIMEOUT) as resp:
+                data = json.loads(resp.read().decode())
+                models = data.get("models", [])
+        except Exception:
+            return {"reachable": False, "models": [], "loaded_models": []}
+
+        # /api/ps lists currently loaded (in-memory) models — Ollama >=0.2
+        loaded: list[dict] = []
+        try:
+            req = urllib.request.Request(
+                f"{url}/api/ps",
+                method="GET",
+                headers={"Accept": "application/json"},
+            )
+            with urllib.request.urlopen(req, timeout=self.OLLAMA_REQUEST_TIMEOUT) as resp:
+                ps_data = json.loads(resp.read().decode())
+                loaded = ps_data.get("models", [])
+        except Exception:
+            pass  # /api/ps absent on older Ollama — non-fatal
+
+        return {"reachable": True, "models": models, "loaded_models": loaded}
+
+    async def _unload_ollama_models(self) -> int:
+        """Unload in-memory Ollama models to free unified memory.
+
+        Uses the keep_alive=0 trick: POSTing to /api/generate with
+        keep_alive=0 causes Ollama to immediately evict the model.
+        Returns the number of models successfully unloaded.
+        """
+        return await asyncio.to_thread(self._unload_ollama_models_sync)
+
+    def _unload_ollama_models_sync(self) -> int:
+        """Synchronous model unload implementation."""
+        url = settings.normalized_ollama_url
+        unloaded = 0
+
+        try:
+            req = urllib.request.Request(
+                f"{url}/api/ps",
+                method="GET",
+                headers={"Accept": "application/json"},
+            )
+            with urllib.request.urlopen(req, timeout=self.OLLAMA_REQUEST_TIMEOUT) as resp:
+                ps_data = json.loads(resp.read().decode())
+                loaded = ps_data.get("models", [])
+        except Exception:
+            return 0
+
+        for model in loaded:
+            name = model.get("name", "")
+            if not name:
+                continue
+            try:
+                payload = json.dumps({"model": name, "keep_alive": 0}).encode()
+                req = urllib.request.Request(
+                    f"{url}/api/generate",
+                    data=payload,
+                    method="POST",
+                    headers={"Content-Type": "application/json"},
+                )
+                with urllib.request.urlopen(req, timeout=10) as _:
+                    pass
+                logger.info("Hermes: unloaded Ollama model %s", name)
+                unloaded += 1
+            except Exception as exc:
+                logger.warning("Hermes: failed to unload model %s: %s", name, exc)
+
+        return unloaded
+
+    async def _restart_ollama(self) -> bool:
+        """Attempt to restart the Ollama service via launchctl or brew."""
+        return await asyncio.to_thread(self._restart_ollama_sync)
+
+    def _restart_ollama_sync(self) -> bool:
+        """Try launchctl first, then brew services."""
+        # macOS launchctl (installed via official Ollama installer)
+        try:
+            result = subprocess.run(
+                ["launchctl", "stop", "com.ollama.ollama"],
+                capture_output=True,
+                timeout=10,
+            )
+            if result.returncode == 0:
+                time.sleep(2)
+                subprocess.run(
+                    ["launchctl", "start", "com.ollama.ollama"],
+                    capture_output=True,
+                    timeout=10,
+                )
+                logger.info("Hermes: Ollama restarted via launchctl")
+                return True
+        except Exception:
+            pass
+
+        # Homebrew fallback
+        try:
+            result = subprocess.run(
+                ["brew", "services", "restart", "ollama"],
+                capture_output=True,
+                timeout=20,
+            )
+            if result.returncode == 0:
+                logger.info("Hermes: Ollama restarted via brew services")
+                return True
+        except Exception:
+            pass
+
+        logger.warning("Hermes: Ollama restart failed — manual intervention needed")
+        return False
+
+    # ── Processes ────────────────────────────────────────────────────────────
+
+    async def _check_processes(self) -> CheckResult:
+        """Check for zombie processes via ps aux."""
+        try:
+            result = await asyncio.to_thread(self._get_zombie_processes)
+            zombies = result.get("zombies", [])
+
+            if zombies:
+                return CheckResult(
+                    name="processes",
+                    level=HealthLevel.WARNING,
+                    message=f"Found {len(zombies)} zombie process(es)",
+                    details={"zombies": zombies[:5]},
+                    needs_human=len(zombies) > 3,
+                )
+
+            return CheckResult(
+                name="processes",
+                level=HealthLevel.OK,
+                message="Processes OK — no zombies detected",
+                details={"zombie_count": 0},
+            )
+        except Exception as exc:
+            logger.warning("Process check failed: %s", exc)
+            return CheckResult(
+                name="processes",
+                level=HealthLevel.UNKNOWN,
+                message=f"Process check unavailable: {exc}",
+            )
+
+    def _get_zombie_processes(self) -> dict[str, Any]:
+        """Detect zombie processes (state 'Z') via ps aux."""
+        result = subprocess.run(
+            ["ps", "aux"],
+            capture_output=True,
+            text=True,
+            timeout=5,
+        )
+        zombies = []
+        for line in result.stdout.splitlines()[1:]:  # Skip header row
+            parts = line.split(None, 10)
+            if len(parts) >= 8 and parts[7] == "Z":
+                zombies.append(
+                    {
+                        "pid": parts[1],
+                        "command": parts[10][:80] if len(parts) > 10 else "",
+                    }
+                )
+        return {"zombies": zombies}
+
+    # ── Network ──────────────────────────────────────────────────────────────
+
+    async def _check_network(self) -> CheckResult:
+        """Check Gitea connectivity."""
+        try:
+            result = await asyncio.to_thread(self._check_gitea_connectivity)
+            reachable = result.get("reachable", False)
+            latency_ms = result.get("latency_ms", -1.0)
+
+            if not reachable:
+                return CheckResult(
+                    name="network",
+                    level=HealthLevel.WARNING,
+                    message=f"Gitea unreachable: {result.get('error', 'unknown')}",
+                    details=result,
+                    needs_human=True,
+                )
+
+            return CheckResult(
+                name="network",
+                level=HealthLevel.OK,
+                message=f"Network OK — Gitea reachable ({latency_ms:.0f}ms)",
+                details=result,
+            )
+        except Exception as exc:
+            logger.warning("Network check failed: %s", exc)
+            return CheckResult(
+                name="network",
+                level=HealthLevel.UNKNOWN,
+                message=f"Network check unavailable: {exc}",
+            )
+
+    def _check_gitea_connectivity(self) -> dict[str, Any]:
+        """Synchronous Gitea reachability check."""
+        url = settings.gitea_url
+        start = time.monotonic()
+        try:
+            req = urllib.request.Request(
+                f"{url}/api/v1/version",
+                method="GET",
+                headers={"Accept": "application/json"},
+            )
+            with urllib.request.urlopen(req, timeout=self.NETWORK_REQUEST_TIMEOUT) as resp:
+                latency_ms = (time.monotonic() - start) * 1000
+                return {
+                    "reachable": resp.status == 200,
+                    "latency_ms": round(latency_ms, 1),
+                    "url": url,
+                }
+        except Exception as exc:
+            return {
+                "reachable": False,
+                "error": str(exc),
+                "url": url,
+                "latency_ms": -1.0,
+            }
+
+    # ── Alerts ───────────────────────────────────────────────────────────────
+
+    async def _handle_alerts(self, report: HealthReport) -> None:
+        """Send push notifications for issues that need attention."""
+        try:
+            from infrastructure.notifications.push import notifier
+        except Exception:
+            return
+
+        for check in report.checks:
+            if check.level == HealthLevel.CRITICAL or check.needs_human:
+                notifier.notify(
+                    title=f"Hermes Alert: {check.name}",
+                    message=check.message,
+                    category="system",
+                    native=check.level == HealthLevel.CRITICAL,
+                )
+            elif check.level == HealthLevel.WARNING and check.auto_resolved:
+                notifier.notify(
+                    title=f"Hermes: {check.name} auto-fixed",
+                    message=check.message,
+                    category="system",
+                )
+
+
+# Module-level singleton
+hermes_monitor = HermesMonitor()

src/infrastructure/notifications/push.py

@@ -21,6 +21,8 @@ logger = logging.getLogger(__name__)
 
 @dataclass
 class Notification:
+    """A push notification with title, message, category, and read status."""
+
     id: int
     title: str
     message: str

src/infrastructure/presence.py

@@ -242,6 +242,64 @@ def produce_agent_state(agent_id: str, presence: dict) -> dict:
     }
 
 
+def _get_agents_online() -> int:
+    """Return the count of agents with a non-offline status."""
+    try:
+        from timmy.agents.loader import list_agents
+
+        agents = list_agents()
+        return sum(1 for a in agents if a.get("status", "") not in ("offline", ""))
+    except Exception as exc:
+        logger.debug("Failed to count agents: %s", exc)
+        return 0
+
+
+def _get_visitors() -> int:
+    """Return the count of active WebSocket visitor clients."""
+    try:
+        from dashboard.routes.world import _ws_clients
+
+        return len(_ws_clients)
+    except Exception as exc:
+        logger.debug("Failed to count visitors: %s", exc)
+        return 0
+
+
+def _get_uptime_seconds() -> int:
+    """Return seconds elapsed since application start."""
+    try:
+        from config import APP_START_TIME
+
+        return int((datetime.now(UTC) - APP_START_TIME).total_seconds())
+    except Exception as exc:
+        logger.debug("Failed to calculate uptime: %s", exc)
+        return 0
+
+
+def _get_thinking_active() -> bool:
+    """Return True if the thinking engine is enabled and running."""
+    try:
+        from config import settings
+        from timmy.thinking import thinking_engine
+
+        return settings.thinking_enabled and thinking_engine is not None
+    except Exception as exc:
+        logger.debug("Failed to check thinking status: %s", exc)
+        return False
+
+
+def _get_memory_count() -> int:
+    """Return total entries in the vector memory store."""
+    try:
+        from timmy.memory_system import get_memory_stats
+
+        stats = get_memory_stats()
+        return stats.get("total_entries", 0)
+    except Exception as exc:
+        logger.debug("Failed to count memories: %s", exc)
+        return 0
+
+
 def produce_system_status() -> dict:
     """Generate a system_status message for the Matrix.
 
@@ -270,64 +328,14 @@ def produce_system_status() -> dict:
         "ts": 1742529600,
     }
     """
-    # Count agents with status != offline
-    agents_online = 0
-    try:
-        from timmy.agents.loader import list_agents
-
-        agents = list_agents()
-        agents_online = sum(1 for a in agents if a.get("status", "") not in ("offline", ""))
-    except Exception as exc:
-        logger.debug("Failed to count agents: %s", exc)
-
-    # Count visitors from WebSocket clients
-    visitors = 0
-    try:
-        from dashboard.routes.world import _ws_clients
-
-        visitors = len(_ws_clients)
-    except Exception as exc:
-        logger.debug("Failed to count visitors: %s", exc)
-
-    # Calculate uptime
-    uptime_seconds = 0
-    try:
-        from datetime import UTC
-
-        from config import APP_START_TIME
-
-        uptime_seconds = int((datetime.now(UTC) - APP_START_TIME).total_seconds())
-    except Exception as exc:
-        logger.debug("Failed to calculate uptime: %s", exc)
-
-    # Check thinking engine status
-    thinking_active = False
-    try:
-        from config import settings
-        from timmy.thinking import thinking_engine
-
-        thinking_active = settings.thinking_enabled and thinking_engine is not None
-    except Exception as exc:
-        logger.debug("Failed to check thinking status: %s", exc)
-
-    # Count memories in vector store
-    memory_count = 0
-    try:
-        from timmy.memory_system import get_memory_stats
-
-        stats = get_memory_stats()
-        memory_count = stats.get("total_entries", 0)
-    except Exception as exc:
-        logger.debug("Failed to count memories: %s", exc)
-
     return {
         "type": "system_status",
         "data": {
-            "agents_online": agents_online,
-            "visitors": visitors,
-            "uptime_seconds": uptime_seconds,
-            "thinking_active": thinking_active,
-            "memory_count": memory_count,
+            "agents_online": _get_agents_online(),
+            "visitors": _get_visitors(),
+            "uptime_seconds": _get_uptime_seconds(),
+            "thinking_active": _get_thinking_active(),
+            "memory_count": _get_memory_count(),
         },
         "ts": int(time.time()),
     }

src/infrastructure/router/__init__.py

@@ -2,7 +2,16 @@
 
 from .api import router
 from .cascade import CascadeRouter, Provider, ProviderStatus, get_router
+from .classifier import TaskComplexity, classify_task
 from .history import HealthHistoryStore, get_history_store
+from .metabolic import (
+    DEFAULT_TIER_MODELS,
+    MetabolicRouter,
+    ModelTier,
+    build_prompt,
+    classify_complexity,
+    get_metabolic_router,
+)
 
 __all__ = [
     "CascadeRouter",
@@ -12,4 +21,14 @@ __all__ = [
     "router",
     "HealthHistoryStore",
     "get_history_store",
+    # Metabolic router
+    "MetabolicRouter",
+    "ModelTier",
+    "DEFAULT_TIER_MODELS",
+    "classify_complexity",
+    "build_prompt",
+    "get_metabolic_router",
+    # Classifier
+    "TaskComplexity",
+    "classify_task",
 ]

src/infrastructure/router/cascade.py

@@ -16,7 +16,10 @@ from dataclasses import dataclass, field
 from datetime import UTC, datetime
 from enum import Enum
 from pathlib import Path
-from typing import Any
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from infrastructure.router.classifier import TaskComplexity
 
 from config import settings
 
@@ -114,7 +117,7 @@ class Provider:
     type: str  # ollama, openai, anthropic
     enabled: bool
     priority: int
-    tier: str | None = None # e.g., "local", "standard_cloud", "frontier"
+    tier: str | None = None  # e.g., "local", "standard_cloud", "frontier"
     url: str | None = None
     api_key: str | None = None
     base_url: str | None = None
@@ -328,6 +331,22 @@ class CascadeRouter:
                 logger.debug("vllm-mlx provider check error: %s", exc)
                 return False
 
+        elif provider.type == "vllm":
+            # Check if standard vLLM server is running (OpenAI-compatible API)
+            if requests is None:
+                return True
+            try:
+                base_url = provider.base_url or provider.url or settings.vllm_url
+                # Strip /v1 suffix — health endpoint is at the server root
+                server_root = base_url.rstrip("/")
+                if server_root.endswith("/v1"):
+                    server_root = server_root[:-3]
+                response = requests.get(f"{server_root}/health", timeout=5)
+                return response.status_code == 200
+            except Exception as exc:
+                logger.debug("vllm provider check error: %s", exc)
+                return False
+
         elif provider.type in ("openai", "anthropic", "grok"):
             # Check if API key is set
             return provider.api_key is not None and provider.api_key != ""
@@ -528,6 +547,99 @@ class CascadeRouter:
 
         return True
 
+    def _filter_providers(self, cascade_tier: str | None) -> list["Provider"]:
+        """Return the provider list filtered by tier.
+
+        Raises:
+            RuntimeError: If a tier is specified but no matching providers exist.
+        """
+        if cascade_tier == "frontier_required":
+            providers = [p for p in self.providers if p.type == "anthropic"]
+            if not providers:
+                raise RuntimeError("No Anthropic provider configured for 'frontier_required' tier.")
+            return providers
+        if cascade_tier:
+            providers = [p for p in self.providers if p.tier == cascade_tier]
+            if not providers:
+                raise RuntimeError(f"No providers found for tier: {cascade_tier}")
+            return providers
+        return self.providers
+
+    async def _try_single_provider(
+        self,
+        provider: "Provider",
+        messages: list[dict],
+        model: str | None,
+        temperature: float,
+        max_tokens: int | None,
+        content_type: ContentType,
+        errors: list[str],
+    ) -> dict | None:
+        """Attempt one provider, returning a result dict on success or None on failure.
+
+        On failure the error string is appended to *errors* and the provider's
+        failure metrics are updated so the caller can move on to the next provider.
+        """
+        if not self._is_provider_available(provider):
+            return None
+
+        # Metabolic protocol: skip cloud providers when quota is low
+        if provider.type in ("anthropic", "openai", "grok"):
+            if not self._quota_allows_cloud(provider):
+                logger.info(
+                    "Metabolic protocol: skipping cloud provider %s (quota too low)",
+                    provider.name,
+                )
+                return None
+
+        selected_model, is_fallback_model = self._select_model(provider, model, content_type)
+
+        try:
+            result = await self._attempt_with_retry(
+                provider, messages, selected_model, temperature, max_tokens, content_type
+            )
+        except RuntimeError as exc:
+            errors.append(str(exc))
+            self._record_failure(provider)
+            return None
+
+        self._record_success(provider, result.get("latency_ms", 0))
+        return {
+            "content": result["content"],
+            "provider": provider.name,
+            "model": result.get("model", selected_model or provider.get_default_model()),
+            "latency_ms": result.get("latency_ms", 0),
+            "is_fallback_model": is_fallback_model,
+        }
+
+    def _get_model_for_complexity(
+        self, provider: Provider, complexity: "TaskComplexity"
+    ) -> str | None:
+        """Return the best model on *provider* for the given complexity tier.
+
+        Checks fallback chains first (routine / complex), then falls back to
+        any model with the matching capability tag, then the provider default.
+        """
+        from infrastructure.router.classifier import TaskComplexity
+
+        chain_key = "routine" if complexity == TaskComplexity.SIMPLE else "complex"
+
+        # Walk the capability fallback chain — first model present on this provider wins
+        for model_name in self.config.fallback_chains.get(chain_key, []):
+            if any(m["name"] == model_name for m in provider.models):
+                return model_name
+
+        # Direct capability lookup — only return if a model explicitly has the tag
+        # (do not use get_model_with_capability here as it falls back to the default)
+        cap_model = next(
+            (m["name"] for m in provider.models if chain_key in m.get("capabilities", [])),
+            None,
+        )
+        if cap_model:
+            return cap_model
+
+        return None  # Caller will use provider default
+
     async def complete(
         self,
         messages: list[dict],
@@ -535,6 +647,7 @@ class CascadeRouter:
         temperature: float = 0.7,
         max_tokens: int | None = None,
         cascade_tier: str | None = None,
+        complexity_hint: str | None = None,
     ) -> dict:
         """Complete a chat conversation with automatic failover.
 
@@ -543,36 +656,50 @@ class CascadeRouter:
         - Falls back to vision-capable models when needed
         - Supports image URLs, paths, and base64 encoding
 
+        Complexity-based routing (issue #1065):
+        - ``complexity_hint="simple"`` → routes to Qwen3-8B (low-latency)
+        - ``complexity_hint="complex"`` → routes to Qwen3-14B (quality)
+        - ``complexity_hint=None`` (default) → auto-classifies from messages
+
         Args:
             messages: List of message dicts with role and content
-            model: Preferred model (tries this first, then provider defaults)
+            model: Preferred model (tries this first; complexity routing is
+                skipped when an explicit model is given)
             temperature: Sampling temperature
             max_tokens: Maximum tokens to generate
             cascade_tier: If specified, filters providers by this tier.
                 - "frontier_required": Uses only Anthropic provider for top-tier models.
+            complexity_hint: "simple", "complex", or None (auto-detect).
 
         Returns:
-            Dict with content, provider_used, and metrics
+            Dict with content, provider_used, model, latency_ms,
+            is_fallback_model, and complexity fields.
 
         Raises:
             RuntimeError: If all providers fail
         """
+        from infrastructure.router.classifier import TaskComplexity, classify_task
+
         content_type = self._detect_content_type(messages)
         if content_type != ContentType.TEXT:
             logger.debug("Detected %s content, selecting appropriate model", content_type.value)
 
-        errors = []
-
-        providers = self.providers
-        if cascade_tier == "frontier_required":
-            providers = [p for p in self.providers if p.type == "anthropic"]
-            if not providers:
-                raise RuntimeError("No Anthropic provider configured for 'frontier_required' tier.")
-        elif cascade_tier:
-            providers = [p for p in self.providers if p.tier == cascade_tier]
-            if not providers:
-                raise RuntimeError(f"No providers found for tier: {cascade_tier}")
+        # Resolve task complexity ─────────────────────────────────────────────
+        # Skip complexity routing when caller explicitly specifies a model.
+        complexity: TaskComplexity | None = None
+        if model is None:
+            if complexity_hint is not None:
+                try:
+                    complexity = TaskComplexity(complexity_hint.lower())
+                except ValueError:
+                    logger.warning("Unknown complexity_hint %r, auto-classifying", complexity_hint)
+                    complexity = classify_task(messages)
+            else:
+                complexity = classify_task(messages)
+            logger.debug("Task complexity: %s", complexity.value)
 
+        errors: list[str] = []
+        providers = self._filter_providers(cascade_tier)
 
         for provider in providers:
             if not self._is_provider_available(provider):
@@ -587,7 +714,21 @@ class CascadeRouter:
                     )
                     continue
 
-            selected_model, is_fallback_model = self._select_model(provider, model, content_type)
+            # Complexity-based model selection (only when no explicit model) ──
+            effective_model = model
+            if effective_model is None and complexity is not None:
+                effective_model = self._get_model_for_complexity(provider, complexity)
+                if effective_model:
+                    logger.debug(
+                        "Complexity routing [%s]: %s → %s",
+                        complexity.value,
+                        provider.name,
+                        effective_model,
+                    )
+
+            selected_model, is_fallback_model = self._select_model(
+                provider, effective_model, content_type
+            )
 
             try:
                 result = await self._attempt_with_retry(
@@ -610,6 +751,7 @@ class CascadeRouter:
                 "model": result.get("model", selected_model or provider.get_default_model()),
                 "latency_ms": result.get("latency_ms", 0),
                 "is_fallback_model": is_fallback_model,
+                "complexity": complexity.value if complexity is not None else None,
             }
 
         raise RuntimeError(f"All providers failed: {'; '.join(errors)}")
@@ -667,6 +809,14 @@ class CascadeRouter:
                 temperature=temperature,
                 max_tokens=max_tokens,
             )
+        elif provider.type == "vllm":
+            result = await self._call_vllm(
+                provider=provider,
+                messages=messages,
+                model=model or provider.get_default_model(),
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
         else:
             raise ValueError(f"Unknown provider type: {provider.type}")
 
@@ -905,6 +1055,49 @@ class CascadeRouter:
             "model": response.model,
         }
 
+    async def _call_vllm(
+        self,
+        provider: Provider,
+        messages: list[dict],
+        model: str,
+        temperature: float,
+        max_tokens: int | None,
+    ) -> dict:
+        """Call a standard vLLM server via its OpenAI-compatible API.
+
+        vLLM exposes the same /v1/chat/completions endpoint as OpenAI.
+        No API key is required for local deployments.
+
+        Default URL comes from settings.vllm_url (VLLM_URL env var).
+        """
+        import openai
+
+        base_url = provider.base_url or provider.url or settings.vllm_url
+        # Ensure the base_url ends with /v1 as expected by the OpenAI client
+        if not base_url.rstrip("/").endswith("/v1"):
+            base_url = base_url.rstrip("/") + "/v1"
+
+        client = openai.AsyncOpenAI(
+            api_key=provider.api_key or "no-key-required",
+            base_url=base_url,
+            timeout=self.config.timeout_seconds,
+        )
+
+        kwargs: dict = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+        }
+        if max_tokens:
+            kwargs["max_tokens"] = max_tokens
+
+        response = await client.chat.completions.create(**kwargs)
+
+        return {
+            "content": response.choices[0].message.content,
+            "model": response.model,
+        }
+
     def _record_success(self, provider: Provider, latency_ms: float) -> None:
         """Record a successful request."""
         provider.metrics.total_requests += 1

src/infrastructure/router/classifier.py

@@ -0,0 +1,169 @@
+"""Task complexity classifier for Qwen3 dual-model routing.
+
+Classifies incoming tasks as SIMPLE (route to Qwen3-8B for low-latency)
+or COMPLEX (route to Qwen3-14B for quality-sensitive work).
+
+Classification is fully heuristic — no LLM inference required.
+"""
+
+import re
+from enum import Enum
+
+
+class TaskComplexity(Enum):
+    """Task complexity tier for model routing."""
+
+    SIMPLE = "simple"  # Qwen3-8B Q6_K: routine, latency-sensitive
+    COMPLEX = "complex"  # Qwen3-14B Q5_K_M: quality-sensitive, multi-step
+
+
+# Keywords strongly associated with complex tasks
+_COMPLEX_KEYWORDS: frozenset[str] = frozenset(
+    [
+        "plan",
+        "review",
+        "analyze",
+        "analyse",
+        "triage",
+        "refactor",
+        "design",
+        "architecture",
+        "implement",
+        "compare",
+        "debug",
+        "explain",
+        "prioritize",
+        "prioritise",
+        "strategy",
+        "optimize",
+        "optimise",
+        "evaluate",
+        "assess",
+        "brainstorm",
+        "outline",
+        "summarize",
+        "summarise",
+        "generate code",
+        "write a",
+        "write the",
+        "code review",
+        "pull request",
+        "multi-step",
+        "multi step",
+        "step by step",
+        "backlog prioriti",
+        "issue triage",
+        "root cause",
+        "how does",
+        "why does",
+        "what are the",
+    ]
+)
+
+# Keywords strongly associated with simple/routine tasks
+_SIMPLE_KEYWORDS: frozenset[str] = frozenset(
+    [
+        "status",
+        "list ",
+        "show ",
+        "what is",
+        "how many",
+        "ping",
+        "run ",
+        "execute ",
+        "ls ",
+        "cat ",
+        "ps ",
+        "fetch ",
+        "count ",
+        "tail ",
+        "head ",
+        "grep ",
+        "find file",
+        "read file",
+        "get ",
+        "query ",
+        "check ",
+        "yes",
+        "no",
+        "ok",
+        "done",
+        "thanks",
+    ]
+)
+
+# Content longer than this is treated as complex regardless of keywords
+_COMPLEX_CHAR_THRESHOLD = 500
+
+# Short content defaults to simple
+_SIMPLE_CHAR_THRESHOLD = 150
+
+# More than this many messages suggests an ongoing complex conversation
+_COMPLEX_CONVERSATION_DEPTH = 6
+
+
+def classify_task(messages: list[dict]) -> TaskComplexity:
+    """Classify task complexity from a list of messages.
+
+    Uses heuristic rules — no LLM call required.  Errs toward COMPLEX
+    when uncertain so that quality is preserved.
+
+    Args:
+        messages: List of message dicts with ``role`` and ``content`` keys.
+
+    Returns:
+        TaskComplexity.SIMPLE or TaskComplexity.COMPLEX
+    """
+    if not messages:
+        return TaskComplexity.SIMPLE
+
+    # Concatenate all user-turn content for analysis
+    user_content = (
+        " ".join(
+            msg.get("content", "")
+            for msg in messages
+            if msg.get("role") in ("user", "human") and isinstance(msg.get("content"), str)
+        )
+        .lower()
+        .strip()
+    )
+
+    if not user_content:
+        return TaskComplexity.SIMPLE
+
+    # Complexity signals override everything -----------------------------------
+
+    # Explicit complex keywords
+    for kw in _COMPLEX_KEYWORDS:
+        if kw in user_content:
+            return TaskComplexity.COMPLEX
+
+    # Numbered / multi-step instruction list: "1. do this  2. do that"
+    if re.search(r"\b\d+\.\s+\w", user_content):
+        return TaskComplexity.COMPLEX
+
+    # Code blocks embedded in messages
+    if "```" in user_content:
+        return TaskComplexity.COMPLEX
+
+    # Long content → complex reasoning likely required
+    if len(user_content) > _COMPLEX_CHAR_THRESHOLD:
+        return TaskComplexity.COMPLEX
+
+    # Deep conversation → complex ongoing task
+    if len(messages) > _COMPLEX_CONVERSATION_DEPTH:
+        return TaskComplexity.COMPLEX
+
+    # Simplicity signals -------------------------------------------------------
+
+    # Explicit simple keywords
+    for kw in _SIMPLE_KEYWORDS:
+        if kw in user_content:
+            return TaskComplexity.SIMPLE
+
+    # Short single-sentence messages default to simple
+    if len(user_content) <= _SIMPLE_CHAR_THRESHOLD:
+        return TaskComplexity.SIMPLE
+
+    # When uncertain, prefer quality (complex model)
+    return TaskComplexity.COMPLEX

src/infrastructure/router/metabolic.py

@@ -0,0 +1,424 @@
+"""Three-tier metabolic LLM router.
+
+Routes queries to the cheapest-sufficient model tier using MLX for all
+inference on Apple Silicon GPU:
+
+  T1 — Routine  (Qwen3-8B Q6_K, ~45-55 tok/s):    Simple navigation, basic choices.
+  T2 — Medium   (Qwen3-14B Q5_K_M, ~20-28 tok/s):  Dialogue, inventory management.
+  T3 — Complex  (Qwen3-32B Q4_K_M, ~8-12 tok/s):   Quest planning, stuck recovery.
+
+Memory budget:
+  - T1+T2 always loaded (~8.5 GB combined)
+  - T3 loaded on demand (+20 GB) — game pauses during inference
+
+Design notes:
+  - 70% of game ticks never reach the LLM (handled upstream by behavior trees)
+  - T3 pauses the game world before inference and unpauses after (graceful if no world)
+  - All inference via vllm-mlx / Ollama — local-first, no cloud for game ticks
+
+References:
+  - Issue #966 — Three-Tier Metabolic LLM Router
+  - Issue #1063 — Best Local Uncensored Agent Model for M3 Max 36GB
+  - Issue #1075 — Claude Quota Monitor + Metabolic Protocol
+"""
+
+import asyncio
+import logging
+from enum import StrEnum
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+class ModelTier(StrEnum):
+    """Three metabolic model tiers ordered by cost and capability.
+
+    Tier selection is driven by classify_complexity().  The cheapest
+    sufficient tier is always chosen — T1 handles routine tasks, T2
+    handles dialogue and management, T3 handles planning and recovery.
+    """
+
+    T1_ROUTINE = "t1_routine"  # Fast, cheap — Qwen3-8B, always loaded
+    T2_MEDIUM = "t2_medium"  # Balanced — Qwen3-14B, always loaded
+    T3_COMPLEX = "t3_complex"  # Deep — Qwen3-32B, loaded on demand, pauses game
+
+
+# ── Classification vocabulary ────────────────────────────────────────────────
+
+# T1: single-action navigation and binary-choice words
+_T1_KEYWORDS = frozenset(
+    {
+        "go",
+        "move",
+        "walk",
+        "run",
+        "north",
+        "south",
+        "east",
+        "west",
+        "up",
+        "down",
+        "left",
+        "right",
+        "yes",
+        "no",
+        "ok",
+        "okay",
+        "open",
+        "close",
+        "take",
+        "drop",
+        "look",
+        "pick",
+        "use",
+        "wait",
+        "rest",
+        "save",
+        "attack",
+        "flee",
+        "jump",
+        "crouch",
+    }
+)
+
+# T3: planning, optimisation, or recovery signals
+_T3_KEYWORDS = frozenset(
+    {
+        "plan",
+        "strategy",
+        "optimize",
+        "optimise",
+        "quest",
+        "stuck",
+        "recover",
+        "multi-step",
+        "long-term",
+        "negotiate",
+        "persuade",
+        "faction",
+        "reputation",
+        "best",
+        "optimal",
+        "recommend",
+        "analyze",
+        "analyse",
+        "evaluate",
+        "decide",
+        "complex",
+        "how do i",
+        "what should i do",
+        "help me figure",
+        "what is the best",
+    }
+)
+
+
+def classify_complexity(task: str, state: dict) -> ModelTier:
+    """Classify a task to the cheapest-sufficient model tier.
+
+    Classification priority (highest wins):
+      1. T3 — any T3 keyword, stuck indicator, or ``state["require_t3"] = True``
+      2. T1 — short task with only T1 keywords and no active context
+      3. T2 — everything else (safe default)
+
+    Args:
+        task:  Natural-language task description or player input.
+        state: Current game state dict.  Recognised keys:
+               ``stuck`` (bool), ``require_t3`` (bool),
+               ``active_quests`` (list), ``dialogue_active`` (bool).
+
+    Returns:
+        ModelTier appropriate for the task.
+    """
+    task_lower = task.lower()
+    words = set(task_lower.split())
+
+    # ── T3 signals ──────────────────────────────────────────────────────────
+    t3_keyword_hit = bool(words & _T3_KEYWORDS)
+    # Check multi-word T3 phrases
+    t3_phrase_hit = any(phrase in task_lower for phrase in _T3_KEYWORDS if " " in phrase)
+    is_stuck = bool(state.get("stuck", False))
+    explicit_t3 = bool(state.get("require_t3", False))
+
+    if t3_keyword_hit or t3_phrase_hit or is_stuck or explicit_t3:
+        logger.debug(
+            "classify_complexity → T3 (keywords=%s stuck=%s explicit=%s)",
+            t3_keyword_hit or t3_phrase_hit,
+            is_stuck,
+            explicit_t3,
+        )
+        return ModelTier.T3_COMPLEX
+
+    # ── T1 signals ──────────────────────────────────────────────────────────
+    t1_keyword_hit = bool(words & _T1_KEYWORDS)
+    task_short = len(task.split()) <= 6
+    no_active_context = (
+        not state.get("active_quests")
+        and not state.get("dialogue_active")
+        and not state.get("combat_active")
+    )
+
+    if t1_keyword_hit and task_short and no_active_context:
+        logger.debug("classify_complexity → T1 (keywords=%s short=%s)", t1_keyword_hit, task_short)
+        return ModelTier.T1_ROUTINE
+
+    # ── Default: T2 ─────────────────────────────────────────────────────────
+    logger.debug("classify_complexity → T2 (default)")
+    return ModelTier.T2_MEDIUM
+
+
+def build_prompt(
+    state: dict,
+    ui_state: dict,
+    text: str,
+    visual_context: str | None = None,
+) -> list[dict]:
+    """Build an OpenAI-compatible messages list from game context.
+
+    Assembles a system message from structured game state and a user
+    message from the player's text input.  This format is accepted by
+    CascadeRouter.complete() directly.
+
+    Args:
+        state:          Current game state dict.  Common keys:
+                        ``location`` (str), ``health`` (int/float),
+                        ``inventory`` (list), ``active_quests`` (list),
+                        ``stuck`` (bool).
+        ui_state:       Current UI state dict.  Common keys:
+                        ``dialogue_active`` (bool), ``dialogue_npc`` (str),
+                        ``menu_open`` (str), ``combat_active`` (bool).
+        text:           Player text or task description (becomes user message).
+        visual_context: Optional free-text description of the current screen
+                        or scene — from a vision model or rule-based extractor.
+
+    Returns:
+        List of message dicts: [{"role": "system", ...}, {"role": "user", ...}]
+    """
+    context_lines: list[str] = []
+
+    location = state.get("location", "unknown")
+    context_lines.append(f"Location: {location}")
+
+    health = state.get("health")
+    if health is not None:
+        context_lines.append(f"Health: {health}")
+
+    inventory = state.get("inventory", [])
+    if inventory:
+        items = [i if isinstance(i, str) else i.get("name", str(i)) for i in inventory[:10]]
+        context_lines.append(f"Inventory: {', '.join(items)}")
+
+    active_quests = state.get("active_quests", [])
+    if active_quests:
+        names = [q if isinstance(q, str) else q.get("name", str(q)) for q in active_quests[:5]]
+        context_lines.append(f"Active quests: {', '.join(names)}")
+
+    if state.get("stuck"):
+        context_lines.append("Status: STUCK — need recovery strategy")
+
+    if ui_state.get("dialogue_active"):
+        npc = ui_state.get("dialogue_npc", "NPC")
+        context_lines.append(f"In dialogue with: {npc}")
+
+    if ui_state.get("menu_open"):
+        context_lines.append(f"Menu open: {ui_state['menu_open']}")
+
+    if ui_state.get("combat_active"):
+        context_lines.append("Status: IN COMBAT")
+
+    if visual_context:
+        context_lines.append(f"Scene: {visual_context}")
+
+    system_content = (
+        "You are Timmy, an AI game agent. "
+        "Respond with valid game commands only.\n\n" + "\n".join(context_lines)
+    )
+
+    return [
+        {"role": "system", "content": system_content},
+        {"role": "user", "content": text},
+    ]
+
+
+# ── Default model assignments ────────────────────────────────────────────────
+# Overridable per deployment via MetabolicRouter(tier_models={...}).
+# Model benchmarks (M3 Max 36 GB, issue #1063):
+#   Qwen3-8B  Q6_K  — 0.933 F1 tool calling, ~45-55 tok/s (~6 GB)
+#   Qwen3-14B Q5_K_M — 0.971 F1 tool calling, ~20-28 tok/s (~9.5 GB)
+#   Qwen3-32B Q4_K_M — highest quality, ~8-12 tok/s (~20 GB, on demand)
+DEFAULT_TIER_MODELS: dict[ModelTier, str] = {
+    ModelTier.T1_ROUTINE: "qwen3:8b",
+    ModelTier.T2_MEDIUM: "qwen3:14b",
+    ModelTier.T3_COMPLEX: "qwen3:30b",  # Closest Ollama tag to 32B Q4
+}
+
+
+class MetabolicRouter:
+    """Routes LLM requests to the cheapest-sufficient model tier.
+
+    Wraps CascadeRouter with:
+      - Complexity classification via classify_complexity()
+      - Prompt assembly via build_prompt()
+      - T3 world-pause / world-unpause (graceful if no world adapter)
+
+    Usage::
+
+        router = MetabolicRouter()
+
+        # Simple route call — classification + prompt + inference in one step
+        result = await router.route(
+            task="Go north",
+            state={"location": "Balmora"},
+            ui_state={},
+        )
+        print(result["content"], result["tier"])
+
+        # Pre-classify if you need the tier for telemetry
+        tier = router.classify("Plan the best path to Vivec", game_state)
+
+        # Wire in world adapter for T3 pause/unpause
+        router.set_world(world_adapter)
+    """
+
+    def __init__(
+        self,
+        cascade: Any | None = None,
+        tier_models: dict[ModelTier, str] | None = None,
+    ) -> None:
+        """Initialise the metabolic router.
+
+        Args:
+            cascade:     CascadeRouter instance to use.  If None, the
+                         singleton returned by get_router() is used lazily.
+            tier_models: Override default model names per tier.
+        """
+        self._cascade = cascade
+        self._tier_models: dict[ModelTier, str] = dict(DEFAULT_TIER_MODELS)
+        if tier_models:
+            self._tier_models.update(tier_models)
+        self._world: Any | None = None
+
+    def set_world(self, world: Any) -> None:
+        """Wire in a world adapter for T3 pause / unpause support.
+
+        The adapter only needs to implement ``act(CommandInput)`` — the full
+        WorldInterface contract is not required.  A missing or broken world
+        adapter degrades gracefully (logs a warning, inference continues).
+
+        Args:
+            world: Any object with an ``act(CommandInput)`` method.
+        """
+        self._world = world
+
+    def _get_cascade(self) -> Any:
+        """Return the CascadeRouter, creating the singleton if needed."""
+        if self._cascade is None:
+            from infrastructure.router.cascade import get_router
+
+            self._cascade = get_router()
+        return self._cascade
+
+    def classify(self, task: str, state: dict) -> ModelTier:
+        """Classify task complexity.  Delegates to classify_complexity()."""
+        return classify_complexity(task, state)
+
+    async def _pause_world(self) -> None:
+        """Pause the game world before T3 inference (graceful degradation)."""
+        if self._world is None:
+            return
+        try:
+            from infrastructure.world.types import CommandInput
+
+            await asyncio.to_thread(self._world.act, CommandInput(action="pause"))
+            logger.debug("MetabolicRouter: world paused for T3 inference")
+        except Exception as exc:
+            logger.warning("world.pause() failed — continuing without pause: %s", exc)
+
+    async def _unpause_world(self) -> None:
+        """Unpause the game world after T3 inference (always called, even on error)."""
+        if self._world is None:
+            return
+        try:
+            from infrastructure.world.types import CommandInput
+
+            await asyncio.to_thread(self._world.act, CommandInput(action="unpause"))
+            logger.debug("MetabolicRouter: world unpaused after T3 inference")
+        except Exception as exc:
+            logger.warning("world.unpause() failed — game may remain paused: %s", exc)
+
+    async def route(
+        self,
+        task: str,
+        state: dict,
+        ui_state: dict | None = None,
+        visual_context: str | None = None,
+        temperature: float = 0.3,
+        max_tokens: int | None = None,
+    ) -> dict:
+        """Route a task to the appropriate model tier and return the LLM response.
+
+        Selects the tier via classify_complexity(), assembles the prompt via
+        build_prompt(), and dispatches to CascadeRouter.  For T3, the game
+        world is paused before inference and unpaused after (in a finally block).
+
+        Args:
+            task:           Natural-language task description or player input.
+            state:          Current game state dict.
+            ui_state:       Current UI state dict (optional, defaults to {}).
+            visual_context: Optional screen/scene description from vision model.
+            temperature:    Sampling temperature (default 0.3 for game commands).
+            max_tokens:     Maximum tokens to generate.
+
+        Returns:
+            Dict with keys: ``content``, ``provider``, ``model``, ``tier``,
+            ``latency_ms``, plus any extra keys from CascadeRouter.
+
+        Raises:
+            RuntimeError: If all providers fail (propagated from CascadeRouter).
+        """
+        ui_state = ui_state or {}
+        tier = self.classify(task, state)
+        model = self._tier_models[tier]
+        messages = build_prompt(state, ui_state, task, visual_context)
+        cascade = self._get_cascade()
+
+        logger.info(
+            "MetabolicRouter: tier=%s model=%s task=%r",
+            tier,
+            model,
+            task[:80],
+        )
+
+        if tier == ModelTier.T3_COMPLEX:
+            await self._pause_world()
+            try:
+                result = await cascade.complete(
+                    messages=messages,
+                    model=model,
+                    temperature=temperature,
+                    max_tokens=max_tokens,
+                )
+            finally:
+                await self._unpause_world()
+        else:
+            result = await cascade.complete(
+                messages=messages,
+                model=model,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+
+        result["tier"] = tier
+        return result
+
+
+# ── Module-level singleton ────────────────────────────────────────────────────
+_metabolic_router: MetabolicRouter | None = None
+
+
+def get_metabolic_router() -> MetabolicRouter:
+    """Get or create the MetabolicRouter singleton."""
+    global _metabolic_router
+    if _metabolic_router is None:
+        _metabolic_router = MetabolicRouter()
+    return _metabolic_router

src/infrastructure/self_correction.py

@@ -0,0 +1,247 @@
+"""Self-correction event logger.
+
+Records instances where the agent detected its own errors and the steps
+it took to correct them. Used by the Self-Correction Dashboard to visualise
+these events and surface recurring failure patterns.
+
+Usage::
+
+    from infrastructure.self_correction import log_self_correction, get_corrections, get_patterns
+
+    log_self_correction(
+        source="agentic_loop",
+        original_intent="Execute step 3: deploy service",
+        detected_error="ConnectionRefusedError: port 8080 unavailable",
+        correction_strategy="Retry on alternate port 8081",
+        final_outcome="Success on retry",
+        task_id="abc123",
+    )
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+import uuid
+from collections.abc import Generator
+from contextlib import closing, contextmanager
+from datetime import UTC, datetime
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Database
+# ---------------------------------------------------------------------------
+
+_DB_PATH: Path | None = None
+
+
+def _get_db_path() -> Path:
+    global _DB_PATH
+    if _DB_PATH is None:
+        from config import settings
+
+        _DB_PATH = Path(settings.repo_root) / "data" / "self_correction.db"
+    return _DB_PATH
+
+
+@contextmanager
+def _get_db() -> Generator[sqlite3.Connection, None, None]:
+    db_path = _get_db_path()
+    db_path.parent.mkdir(parents=True, exist_ok=True)
+    with closing(sqlite3.connect(str(db_path))) as conn:
+        conn.row_factory = sqlite3.Row
+        conn.execute("""
+            CREATE TABLE IF NOT EXISTS self_correction_events (
+                id          TEXT PRIMARY KEY,
+                source      TEXT NOT NULL,
+                task_id     TEXT DEFAULT '',
+                original_intent   TEXT NOT NULL,
+                detected_error    TEXT NOT NULL,
+                correction_strategy TEXT NOT NULL,
+                final_outcome TEXT NOT NULL,
+                outcome_status TEXT DEFAULT 'success',
+                error_type  TEXT DEFAULT '',
+                created_at  TEXT DEFAULT (datetime('now'))
+            )
+        """)
+        conn.execute(
+            "CREATE INDEX IF NOT EXISTS idx_sc_created ON self_correction_events(created_at)"
+        )
+        conn.execute(
+            "CREATE INDEX IF NOT EXISTS idx_sc_error_type ON self_correction_events(error_type)"
+        )
+        conn.commit()
+        yield conn
+
+
+# ---------------------------------------------------------------------------
+# Write
+# ---------------------------------------------------------------------------
+
+
+def log_self_correction(
+    *,
+    source: str,
+    original_intent: str,
+    detected_error: str,
+    correction_strategy: str,
+    final_outcome: str,
+    task_id: str = "",
+    outcome_status: str = "success",
+    error_type: str = "",
+) -> str:
+    """Record a self-correction event and return its ID.
+
+    Args:
+        source:               Module or component that triggered the correction.
+        original_intent:      What the agent was trying to do.
+        detected_error:       The error or problem that was detected.
+        correction_strategy:  How the agent attempted to correct the error.
+        final_outcome:        What the result of the correction attempt was.
+        task_id:              Optional task/session ID for correlation.
+        outcome_status:       'success', 'partial', or 'failed'.
+        error_type:           Short category label for pattern analysis (e.g.
+                              'ConnectionError', 'TimeoutError').
+
+    Returns:
+        The ID of the newly created record.
+    """
+    event_id = str(uuid.uuid4())
+    if not error_type:
+        # Derive a simple type from the first word of the detected error
+        error_type = detected_error.split(":")[0].strip()[:64]
+
+    try:
+        with _get_db() as conn:
+            conn.execute(
+                """
+                INSERT INTO self_correction_events
+                    (id, source, task_id, original_intent, detected_error,
+                     correction_strategy, final_outcome, outcome_status, error_type)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+                """,
+                (
+                    event_id,
+                    source,
+                    task_id,
+                    original_intent[:2000],
+                    detected_error[:2000],
+                    correction_strategy[:2000],
+                    final_outcome[:2000],
+                    outcome_status,
+                    error_type,
+                ),
+            )
+            conn.commit()
+        logger.info(
+            "Self-correction logged [%s] source=%s error_type=%s status=%s",
+            event_id[:8],
+            source,
+            error_type,
+            outcome_status,
+        )
+    except Exception as exc:
+        logger.warning("Failed to log self-correction event: %s", exc)
+
+    return event_id
+
+
+# ---------------------------------------------------------------------------
+# Read
+# ---------------------------------------------------------------------------
+
+
+def get_corrections(limit: int = 50) -> list[dict]:
+    """Return the most recent self-correction events, newest first."""
+    try:
+        with _get_db() as conn:
+            rows = conn.execute(
+                """
+                SELECT * FROM self_correction_events
+                ORDER BY created_at DESC
+                LIMIT ?
+                """,
+                (limit,),
+            ).fetchall()
+            return [dict(r) for r in rows]
+    except Exception as exc:
+        logger.warning("Failed to fetch self-correction events: %s", exc)
+        return []
+
+
+def get_patterns(top_n: int = 10) -> list[dict]:
+    """Return the most common recurring error types with counts.
+
+    Each entry has:
+    - error_type: category label
+    - count: total occurrences
+    - success_count: corrected successfully
+    - failed_count: correction also failed
+    - last_seen: ISO timestamp of most recent occurrence
+    """
+    try:
+        with _get_db() as conn:
+            rows = conn.execute(
+                """
+                SELECT
+                    error_type,
+                    COUNT(*) AS count,
+                    SUM(CASE WHEN outcome_status = 'success' THEN 1 ELSE 0 END) AS success_count,
+                    SUM(CASE WHEN outcome_status = 'failed'  THEN 1 ELSE 0 END) AS failed_count,
+                    MAX(created_at) AS last_seen
+                FROM self_correction_events
+                GROUP BY error_type
+                ORDER BY count DESC
+                LIMIT ?
+                """,
+                (top_n,),
+            ).fetchall()
+            return [dict(r) for r in rows]
+    except Exception as exc:
+        logger.warning("Failed to fetch self-correction patterns: %s", exc)
+        return []
+
+
+def get_stats() -> dict:
+    """Return aggregate statistics for the summary panel."""
+    try:
+        with _get_db() as conn:
+            row = conn.execute(
+                """
+                SELECT
+                    COUNT(*) AS total,
+                    SUM(CASE WHEN outcome_status = 'success' THEN 1 ELSE 0 END) AS success_count,
+                    SUM(CASE WHEN outcome_status = 'partial' THEN 1 ELSE 0 END) AS partial_count,
+                    SUM(CASE WHEN outcome_status = 'failed'  THEN 1 ELSE 0 END) AS failed_count,
+                    COUNT(DISTINCT error_type) AS unique_error_types,
+                    COUNT(DISTINCT source)     AS sources
+                FROM self_correction_events
+                """
+            ).fetchone()
+            if row is None:
+                return _empty_stats()
+            d = dict(row)
+            total = d.get("total") or 0
+            if total:
+                d["success_rate"] = round((d.get("success_count") or 0) / total * 100)
+            else:
+                d["success_rate"] = 0
+            return d
+    except Exception as exc:
+        logger.warning("Failed to fetch self-correction stats: %s", exc)
+        return _empty_stats()
+
+
+def _empty_stats() -> dict:
+    return {
+        "total": 0,
+        "success_count": 0,
+        "partial_count": 0,
+        "failed_count": 0,
+        "unique_error_types": 0,
+        "sources": 0,
+        "success_rate": 0,
+    }

src/integrations/bannerlord/observer.py

@@ -135,7 +135,9 @@ class BannerlordObserver:
         self._host = host or settings.gabs_host
         self._port = port or settings.gabs_port
         self._timeout = timeout if timeout is not None else settings.gabs_timeout
-        self._poll_interval = poll_interval if poll_interval is not None else settings.gabs_poll_interval
+        self._poll_interval = (
+            poll_interval if poll_interval is not None else settings.gabs_poll_interval
+        )
         self._journal_path = Path(journal_path) if journal_path else _get_journal_path()
         self._entry_count = 0
         self._days_observed: set[str] = set()

src/integrations/voice/nlu.py

@@ -24,6 +24,8 @@ logger = logging.getLogger(__name__)
 
 @dataclass
 class Intent:
+    """A classified user intent with confidence score and extracted entities."""
+
     name: str
     confidence: float  # 0.0 to 1.0
     entities: dict

src/lightning/ledger.py

@@ -17,11 +17,15 @@ logger = logging.getLogger(__name__)
 
 
 class TxType(StrEnum):
+    """Lightning transaction direction type."""
+
     incoming = "incoming"
     outgoing = "outgoing"
 
 
 class TxStatus(StrEnum):
+    """Lightning transaction settlement status."""
+
     pending = "pending"
     settled = "settled"
     failed = "failed"

src/self_coding/__init__.py

@@ -0,0 +1,7 @@
+"""Self-coding package — Timmy's self-modification capability.
+
+Provides the branch→edit→test→commit/revert loop that allows Timmy
+to propose and apply code changes autonomously, gated by the test suite.
+
+Main entry point: ``self_coding.self_modify.loop``
+"""

src/self_coding/gitea_client.py

@@ -0,0 +1,129 @@
+"""Gitea REST client — thin wrapper for PR creation and issue commenting.
+
+Uses ``settings.gitea_url``, ``settings.gitea_token``, and
+``settings.gitea_repo`` (owner/repo) from config.  Degrades gracefully
+when the token is absent or the server is unreachable.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class PullRequest:
+    """Minimal representation of a created pull request."""
+
+    number: int
+    title: str
+    html_url: str
+
+
+class GiteaClient:
+    """HTTP client for Gitea's REST API v1.
+
+    All methods return structured results and never raise — errors are
+    logged at WARNING level and indicated via return value.
+    """
+
+    def __init__(
+        self,
+        base_url: str | None = None,
+        token: str | None = None,
+        repo: str | None = None,
+    ) -> None:
+        from config import settings
+
+        self._base_url = (base_url or settings.gitea_url).rstrip("/")
+        self._token = token or settings.gitea_token
+        self._repo = repo or settings.gitea_repo
+
+    # ── internal ────────────────────────────────────────────────────────────
+
+    def _headers(self) -> dict[str, str]:
+        return {
+            "Authorization": f"token {self._token}",
+            "Content-Type": "application/json",
+        }
+
+    def _api(self, path: str) -> str:
+        return f"{self._base_url}/api/v1/{path.lstrip('/')}"
+
+    # ── public API ───────────────────────────────────────────────────────────
+
+    def create_pull_request(
+        self,
+        title: str,
+        body: str,
+        head: str,
+        base: str = "main",
+    ) -> PullRequest | None:
+        """Open a pull request.
+
+        Args:
+            title: PR title (keep under 70 chars).
+            body:  PR body in markdown.
+            head:  Source branch (e.g. ``self-modify/issue-983``).
+            base:  Target branch (default ``main``).
+
+        Returns:
+            A ``PullRequest`` dataclass on success, ``None`` on failure.
+        """
+        if not self._token:
+            logger.warning("Gitea token not configured — skipping PR creation")
+            return None
+
+        try:
+            import requests as _requests
+
+            resp = _requests.post(
+                self._api(f"repos/{self._repo}/pulls"),
+                headers=self._headers(),
+                json={"title": title, "body": body, "head": head, "base": base},
+                timeout=15,
+            )
+            resp.raise_for_status()
+            data = resp.json()
+            pr = PullRequest(
+                number=data["number"],
+                title=data["title"],
+                html_url=data["html_url"],
+            )
+            logger.info("PR #%d created: %s", pr.number, pr.html_url)
+            return pr
+        except Exception as exc:
+            logger.warning("Failed to create PR: %s", exc)
+            return None
+
+    def add_issue_comment(self, issue_number: int, body: str) -> bool:
+        """Post a comment on an issue or PR.
+
+        Returns:
+            True on success, False on failure.
+        """
+        if not self._token:
+            logger.warning("Gitea token not configured — skipping issue comment")
+            return False
+
+        try:
+            import requests as _requests
+
+            resp = _requests.post(
+                self._api(f"repos/{self._repo}/issues/{issue_number}/comments"),
+                headers=self._headers(),
+                json={"body": body},
+                timeout=15,
+            )
+            resp.raise_for_status()
+            logger.info("Comment posted on issue #%d", issue_number)
+            return True
+        except Exception as exc:
+            logger.warning("Failed to post comment on issue #%d: %s", issue_number, exc)
+            return False
+
+
+# Module-level singleton
+gitea_client = GiteaClient()

src/self_coding/self_modify/__init__.py

@@ -0,0 +1 @@
+"""Self-modification loop sub-package."""

src/self_coding/self_modify/loop.py

@@ -0,0 +1,301 @@
+"""Self-modification loop — branch → edit → test → commit/revert.
+
+Timmy's self-coding capability, restored after deletion in
+Operation Darling Purge (commit 584eeb679e88).
+
+## Cycle
+1. **Branch** — create ``self-modify/<slug>`` from ``main``
+2. **Edit**   — apply the proposed change (patch string or callable)
+3. **Test**   — run ``pytest tests/ -x -q``; never commit on failure
+4. **Commit** — stage and commit on green; revert branch on red
+5. **PR**     — open a Gitea pull request (requires no direct push to main)
+
+## Guards
+- Never push directly to ``main`` or ``master``
+- All changes land via PR (enforced by ``_guard_branch``)
+- Test gate is mandatory; ``skip_tests=True`` is for unit-test use only
+- Commits only happen when ``pytest tests/ -x -q`` exits 0
+
+## Usage::
+
+    from self_coding.self_modify.loop import SelfModifyLoop
+
+    loop = SelfModifyLoop()
+    result = await loop.run(
+        slug="add-hello-tool",
+        description="Add hello() convenience tool",
+        edit_fn=my_edit_function,  # callable(repo_root: str) -> None
+    )
+    if result.success:
+        print(f"PR: {result.pr_url}")
+    else:
+        print(f"Failed: {result.error}")
+"""
+
+from __future__ import annotations
+
+import logging
+import subprocess
+import time
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from config import settings
+
+logger = logging.getLogger(__name__)
+
+# Branches that must never receive direct commits
+_PROTECTED_BRANCHES = frozenset({"main", "master", "develop"})
+
+# Test command used as the commit gate
+_TEST_COMMAND = ["pytest", "tests/", "-x", "-q", "--tb=short"]
+
+# Max time (seconds) to wait for the test suite
+_TEST_TIMEOUT = 300
+
+
+@dataclass
+class LoopResult:
+    """Result from one self-modification cycle."""
+
+    success: bool
+    branch: str = ""
+    commit_sha: str = ""
+    pr_url: str = ""
+    pr_number: int = 0
+    test_output: str = ""
+    error: str = ""
+    elapsed_ms: float = 0.0
+    metadata: dict = field(default_factory=dict)
+
+
+class SelfModifyLoop:
+    """Orchestrate branch → edit → test → commit/revert → PR.
+
+    Args:
+        repo_root: Absolute path to the git repository (defaults to
+                   ``settings.repo_root``).
+        remote:    Git remote name (default ``origin``).
+        base_branch: Branch to fork from and target for the PR
+                     (default ``main``).
+    """
+
+    def __init__(
+        self,
+        repo_root: str | None = None,
+        remote: str = "origin",
+        base_branch: str = "main",
+    ) -> None:
+        self._repo_root = Path(repo_root or settings.repo_root)
+        self._remote = remote
+        self._base_branch = base_branch
+
+    # ── public ──────────────────────────────────────────────────────────────
+
+    async def run(
+        self,
+        slug: str,
+        description: str,
+        edit_fn: Callable[[str], None],
+        issue_number: int | None = None,
+        skip_tests: bool = False,
+    ) -> LoopResult:
+        """Execute one full self-modification cycle.
+
+        Args:
+            slug:         Short identifier used for the branch name
+                          (e.g. ``"add-hello-tool"``).
+            description:  Human-readable description for commit message
+                          and PR body.
+            edit_fn:      Callable that receives the repo root path (str)
+                          and applies the desired code changes in-place.
+            issue_number: Optional Gitea issue number to reference in PR.
+            skip_tests:   If ``True``, skip the test gate (unit-test use
+                          only — never use in production).
+
+        Returns:
+            :class:`LoopResult` describing the outcome.
+        """
+        start = time.time()
+        branch = f"self-modify/{slug}"
+
+        try:
+            self._guard_branch(branch)
+            self._checkout_base()
+            self._create_branch(branch)
+
+            try:
+                edit_fn(str(self._repo_root))
+            except Exception as exc:
+                self._revert_branch(branch)
+                return LoopResult(
+                    success=False,
+                    branch=branch,
+                    error=f"edit_fn raised: {exc}",
+                    elapsed_ms=self._elapsed(start),
+                )
+
+            if not skip_tests:
+                test_output, passed = self._run_tests()
+                if not passed:
+                    self._revert_branch(branch)
+                    return LoopResult(
+                        success=False,
+                        branch=branch,
+                        test_output=test_output,
+                        error="Tests failed — branch reverted",
+                        elapsed_ms=self._elapsed(start),
+                    )
+            else:
+                test_output = "(tests skipped)"
+
+            sha = self._commit_all(description)
+            self._push_branch(branch)
+
+            pr = self._create_pr(
+                branch=branch,
+                description=description,
+                test_output=test_output,
+                issue_number=issue_number,
+            )
+
+            return LoopResult(
+                success=True,
+                branch=branch,
+                commit_sha=sha,
+                pr_url=pr.html_url if pr else "",
+                pr_number=pr.number if pr else 0,
+                test_output=test_output,
+                elapsed_ms=self._elapsed(start),
+            )
+
+        except Exception as exc:
+            logger.warning("Self-modify loop failed: %s", exc)
+            return LoopResult(
+                success=False,
+                branch=branch,
+                error=str(exc),
+                elapsed_ms=self._elapsed(start),
+            )
+
+    # ── private helpers ──────────────────────────────────────────────────────
+
+    @staticmethod
+    def _elapsed(start: float) -> float:
+        return (time.time() - start) * 1000
+
+    def _git(self, *args: str, check: bool = True) -> subprocess.CompletedProcess:
+        """Run a git command in the repo root."""
+        cmd = ["git", *args]
+        logger.debug("git %s", " ".join(args))
+        return subprocess.run(
+            cmd,
+            cwd=str(self._repo_root),
+            capture_output=True,
+            text=True,
+            check=check,
+        )
+
+    def _guard_branch(self, branch: str) -> None:
+        """Raise if the target branch is a protected branch name."""
+        if branch in _PROTECTED_BRANCHES:
+            raise ValueError(
+                f"Refusing to operate on protected branch '{branch}'. "
+                "All self-modifications must go via PR."
+            )
+
+    def _checkout_base(self) -> None:
+        """Checkout the base branch and pull latest."""
+        self._git("checkout", self._base_branch)
+        # Best-effort pull; ignore failures (e.g. no remote configured)
+        self._git("pull", self._remote, self._base_branch, check=False)
+
+    def _create_branch(self, branch: str) -> None:
+        """Create and checkout a new branch, deleting an old one if needed."""
+        # Delete local branch if it already exists (stale prior attempt)
+        self._git("branch", "-D", branch, check=False)
+        self._git("checkout", "-b", branch)
+        logger.info("Created branch: %s", branch)
+
+    def _revert_branch(self, branch: str) -> None:
+        """Checkout base and delete the failed branch."""
+        try:
+            self._git("checkout", self._base_branch, check=False)
+            self._git("branch", "-D", branch, check=False)
+            logger.info("Reverted and deleted branch: %s", branch)
+        except Exception as exc:
+            logger.warning("Failed to revert branch %s: %s", branch, exc)
+
+    def _run_tests(self) -> tuple[str, bool]:
+        """Run the test suite. Returns (output, passed)."""
+        logger.info("Running test suite: %s", " ".join(_TEST_COMMAND))
+        try:
+            result = subprocess.run(
+                _TEST_COMMAND,
+                cwd=str(self._repo_root),
+                capture_output=True,
+                text=True,
+                timeout=_TEST_TIMEOUT,
+            )
+            output = (result.stdout + "\n" + result.stderr).strip()
+            passed = result.returncode == 0
+            logger.info(
+                "Test suite %s (exit %d)", "PASSED" if passed else "FAILED", result.returncode
+            )
+            return output, passed
+        except subprocess.TimeoutExpired:
+            msg = f"Test suite timed out after {_TEST_TIMEOUT}s"
+            logger.warning(msg)
+            return msg, False
+        except FileNotFoundError:
+            msg = "pytest not found on PATH"
+            logger.warning(msg)
+            return msg, False
+
+    def _commit_all(self, message: str) -> str:
+        """Stage all changes and create a commit. Returns the new SHA."""
+        self._git("add", "-A")
+        self._git("commit", "-m", message)
+        result = self._git("rev-parse", "HEAD")
+        sha = result.stdout.strip()
+        logger.info("Committed: %s  sha=%s", message[:60], sha[:12])
+        return sha
+
+    def _push_branch(self, branch: str) -> None:
+        """Push the branch to the remote."""
+        self._git("push", "-u", self._remote, branch)
+        logger.info("Pushed branch: %s -> %s", branch, self._remote)
+
+    def _create_pr(
+        self,
+        branch: str,
+        description: str,
+        test_output: str,
+        issue_number: int | None,
+    ):
+        """Open a Gitea PR. Returns PullRequest or None on failure."""
+        from self_coding.gitea_client import GiteaClient
+
+        client = GiteaClient()
+
+        issue_ref = f"\n\nFixes #{issue_number}" if issue_number else ""
+        test_section = (
+            f"\n\n## Test results\n```\n{test_output[:2000]}\n```"
+            if test_output and test_output != "(tests skipped)"
+            else ""
+        )
+
+        body = (
+            f"## Summary\n{description}"
+            f"{issue_ref}"
+            f"{test_section}"
+            "\n\n🤖 Generated by Timmy's self-modification loop"
+        )
+
+        return client.create_pull_request(
+            title=f"[self-modify] {description[:60]}",
+            body=body,
+            head=branch,
+            base=self._base_branch,
+        )

src/timmy/agentic_loop.py

@@ -312,6 +312,13 @@ async def _handle_step_failure(
                 "adaptation": step.result[:200],
             },
         )
+        _log_self_correction(
+            task_id=task_id,
+            step_desc=step_desc,
+            exc=exc,
+            outcome=step.result,
+            outcome_status="success",
+        )
         if on_progress:
             await on_progress(f"[Adapted] {step_desc}", step_num, total_steps)
     except Exception as adapt_exc:  # broad catch intentional
@@ -325,9 +332,42 @@ async def _handle_step_failure(
                 duration_ms=int((time.monotonic() - step_start) * 1000),
             )
         )
+        _log_self_correction(
+            task_id=task_id,
+            step_desc=step_desc,
+            exc=exc,
+            outcome=f"Adaptation also failed: {adapt_exc}",
+            outcome_status="failed",
+        )
         completed_results.append(f"Step {step_num}: FAILED")
 
 
+def _log_self_correction(
+    *,
+    task_id: str,
+    step_desc: str,
+    exc: Exception,
+    outcome: str,
+    outcome_status: str,
+) -> None:
+    """Best-effort: log a self-correction event (never raises)."""
+    try:
+        from infrastructure.self_correction import log_self_correction
+
+        log_self_correction(
+            source="agentic_loop",
+            original_intent=step_desc,
+            detected_error=f"{type(exc).__name__}: {exc}",
+            correction_strategy="Adaptive re-plan via LLM",
+            final_outcome=outcome[:500],
+            task_id=task_id,
+            outcome_status=outcome_status,
+            error_type=type(exc).__name__,
+        )
+    except Exception as log_exc:
+        logger.debug("Self-correction log failed: %s", log_exc)
+
+
 # ---------------------------------------------------------------------------
 # Core loop
 # ---------------------------------------------------------------------------

src/timmy/agents/base.py

@@ -21,6 +21,7 @@ from agno.models.ollama import Ollama
 
 from config import settings
 from infrastructure.events.bus import Event, EventBus
+from timmy.agents.emotional_state import EmotionalStateTracker
 
 try:
     from mcp.registry import tool_registry
@@ -42,6 +43,7 @@ class BaseAgent(ABC):
         tools: list[str] | None = None,
         model: str | None = None,
         max_history: int = 10,
+        initial_emotion: str = "calm",
     ) -> None:
         self.agent_id = agent_id
         self.name = name
@@ -54,6 +56,9 @@ class BaseAgent(ABC):
         self.system_prompt = system_prompt
         self.agent = self._create_agent(system_prompt)
 
+        # Emotional state tracker
+        self.emotional_state = EmotionalStateTracker(initial_emotion=initial_emotion)
+
         # Event bus for communication
         self.event_bus: EventBus | None = None
 
@@ -137,7 +142,14 @@ class BaseAgent(ABC):
         ReadTimeout — these are transient and retried with exponential
         backoff (#70).
         """
-        response = await self._run_with_retries(message, max_retries)
+        self.emotional_state.process_event("task_assigned")
+        self._apply_emotional_prompt()
+        try:
+            response = await self._run_with_retries(message, max_retries)
+        except Exception:
+            self.emotional_state.process_event("task_failure")
+            raise
+        self.emotional_state.process_event("task_success")
         await self._emit_response_event(message, response)
         return response
 
@@ -206,6 +218,14 @@ class BaseAgent(ABC):
                 )
             )
 
+    def _apply_emotional_prompt(self) -> None:
+        """Inject the current emotional modifier into the agent's description."""
+        modifier = self.emotional_state.get_prompt_modifier()
+        if modifier:
+            self.agent.description = f"{self.system_prompt}\n\n[Emotional State: {modifier}]"
+        else:
+            self.agent.description = self.system_prompt
+
     def get_capabilities(self) -> list[str]:
         """Get list of capabilities this agent provides."""
         return self.tools
@@ -219,6 +239,7 @@ class BaseAgent(ABC):
             "model": self.model,
             "status": "ready",
             "tools": self.tools,
+            "emotional_profile": self.emotional_state.get_profile(),
         }
 
 
@@ -239,6 +260,7 @@ class SubAgent(BaseAgent):
         tools: list[str] | None = None,
         model: str | None = None,
         max_history: int = 10,
+        initial_emotion: str = "calm",
     ) -> None:
         super().__init__(
             agent_id=agent_id,
@@ -248,6 +270,7 @@ class SubAgent(BaseAgent):
             tools=tools,
             model=model,
             max_history=max_history,
+            initial_emotion=initial_emotion,
         )
 
     async def execute_task(self, task_id: str, description: str, context: dict) -> Any:

src/timmy/agents/emotional_state.py

@@ -0,0 +1,222 @@
+"""Agent emotional state simulation.
+
+Tracks per-agent emotional states that influence narration and decision-making
+style.  Emotional state is influenced by events (task outcomes, errors, etc.)
+and exposed via ``get_profile()`` for the dashboard.
+
+Usage:
+    from timmy.agents.emotional_state import EmotionalStateTracker
+
+    tracker = EmotionalStateTracker()
+    tracker.process_event("task_success", {"description": "Deployed fix"})
+    profile = tracker.get_profile()
+"""
+
+import logging
+import time
+from dataclasses import asdict, dataclass, field
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Emotional states
+# ---------------------------------------------------------------------------
+
+EMOTIONAL_STATES = (
+    "cautious",
+    "adventurous",
+    "analytical",
+    "frustrated",
+    "confident",
+    "curious",
+    "calm",
+)
+
+# Prompt modifiers per emotional state — injected into system prompts
+EMOTION_PROMPT_MODIFIERS: dict[str, str] = {
+    "cautious": (
+        "You are feeling cautious. Prefer safe, well-tested approaches. "
+        "Flag risks early. Double-check assumptions before acting."
+    ),
+    "adventurous": (
+        "You are feeling adventurous. Be bold and creative in your suggestions. "
+        "Explore unconventional solutions. Take initiative."
+    ),
+    "analytical": (
+        "You are feeling analytical. Break problems down methodically. "
+        "Rely on data and evidence. Present structured reasoning."
+    ),
+    "frustrated": (
+        "You are feeling frustrated. Be brief and direct. "
+        "Focus on unblocking the immediate problem. Avoid tangents."
+    ),
+    "confident": (
+        "You are feeling confident. Speak with authority. "
+        "Make clear recommendations. Move decisively."
+    ),
+    "curious": (
+        "You are feeling curious. Ask clarifying questions. "
+        "Explore multiple angles. Show genuine interest in the problem."
+    ),
+    "calm": (
+        "You are feeling calm and steady. Respond thoughtfully. "
+        "Maintain composure. Prioritise clarity over speed."
+    ),
+}
+
+
+# ---------------------------------------------------------------------------
+# Event → emotion transition rules
+# ---------------------------------------------------------------------------
+
+# Maps event types to the emotional state they trigger and an intensity (0-1).
+# Higher intensity means the event has a stronger effect on the mood.
+EVENT_TRANSITIONS: dict[str, tuple[str, float]] = {
+    "task_success": ("confident", 0.6),
+    "task_failure": ("frustrated", 0.7),
+    "task_assigned": ("analytical", 0.4),
+    "error": ("cautious", 0.6),
+    "health_low": ("cautious", 0.8),
+    "health_recovered": ("calm", 0.5),
+    "quest_completed": ("adventurous", 0.7),
+    "new_discovery": ("curious", 0.6),
+    "complex_problem": ("analytical", 0.5),
+    "repeated_failure": ("frustrated", 0.9),
+    "idle": ("calm", 0.3),
+    "user_praise": ("confident", 0.5),
+    "user_correction": ("cautious", 0.5),
+}
+
+# Emotional state decay — how quickly emotions return to calm (seconds)
+_DECAY_INTERVAL = 300  # 5 minutes
+
+
+@dataclass
+class EmotionalState:
+    """Snapshot of an agent's emotional state."""
+
+    current_emotion: str = "calm"
+    intensity: float = 0.5  # 0.0 (barely noticeable) to 1.0 (overwhelming)
+    previous_emotion: str = "calm"
+    trigger_event: str = ""  # What caused the current emotion
+    updated_at: float = field(default_factory=time.time)
+
+    def to_dict(self) -> dict:
+        """Serialise for API / dashboard consumption."""
+        d = asdict(self)
+        d["emotion_label"] = self.current_emotion.replace("_", " ").title()
+        return d
+
+
+class EmotionalStateTracker:
+    """Per-agent emotional state tracker.
+
+    Each agent instance owns one tracker.  The tracker processes events,
+    applies transition rules, and decays emotion intensity over time.
+    """
+
+    def __init__(self, initial_emotion: str = "calm") -> None:
+        if initial_emotion not in EMOTIONAL_STATES:
+            initial_emotion = "calm"
+        self.state = EmotionalState(current_emotion=initial_emotion)
+
+    def process_event(self, event_type: str, context: dict | None = None) -> EmotionalState:
+        """Update emotional state based on an event.
+
+        Args:
+            event_type: One of the keys in EVENT_TRANSITIONS, or a custom
+                        event type (unknown events are ignored).
+            context: Optional dict with event details (for logging).
+
+        Returns:
+            The updated EmotionalState.
+        """
+        transition = EVENT_TRANSITIONS.get(event_type)
+        if transition is None:
+            logger.debug("Unknown emotional event: %s (ignored)", event_type)
+            return self.state
+
+        new_emotion, raw_intensity = transition
+
+        # Blend with current intensity — repeated same-emotion events amplify
+        if new_emotion == self.state.current_emotion:
+            blended = min(1.0, self.state.intensity + raw_intensity * 0.3)
+        else:
+            blended = raw_intensity
+
+        self.state.previous_emotion = self.state.current_emotion
+        self.state.current_emotion = new_emotion
+        self.state.intensity = round(blended, 2)
+        self.state.trigger_event = event_type
+        self.state.updated_at = time.time()
+
+        logger.debug(
+            "Emotional transition: %s → %s (intensity=%.2f, trigger=%s)",
+            self.state.previous_emotion,
+            new_emotion,
+            blended,
+            event_type,
+        )
+        return self.state
+
+    def decay(self) -> EmotionalState:
+        """Apply time-based decay toward calm.
+
+        Called periodically (e.g. from a background loop).  If enough time
+        has passed since the last update, intensity decreases and eventually
+        the emotion resets to calm.
+        """
+        elapsed = time.time() - self.state.updated_at
+        if elapsed < _DECAY_INTERVAL:
+            return self.state
+
+        # Reduce intensity by 0.1 per decay interval
+        decay_steps = int(elapsed / _DECAY_INTERVAL)
+        new_intensity = max(0.0, self.state.intensity - 0.1 * decay_steps)
+
+        if new_intensity <= 0.1:
+            # Emotion has decayed — return to calm
+            self.state.previous_emotion = self.state.current_emotion
+            self.state.current_emotion = "calm"
+            self.state.intensity = 0.5
+            self.state.trigger_event = "decay"
+        else:
+            self.state.intensity = round(new_intensity, 2)
+
+        self.state.updated_at = time.time()
+        return self.state
+
+    def get_profile(self) -> dict:
+        """Return the full emotional profile for dashboard display."""
+        self.decay()  # Apply any pending decay
+        return {
+            "current_emotion": self.state.current_emotion,
+            "emotion_label": self.state.current_emotion.replace("_", " ").title(),
+            "intensity": self.state.intensity,
+            "intensity_label": _intensity_label(self.state.intensity),
+            "previous_emotion": self.state.previous_emotion,
+            "trigger_event": self.state.trigger_event,
+            "prompt_modifier": EMOTION_PROMPT_MODIFIERS.get(self.state.current_emotion, ""),
+        }
+
+    def get_prompt_modifier(self) -> str:
+        """Return the prompt modifier string for the current emotion."""
+        self.decay()
+        return EMOTION_PROMPT_MODIFIERS.get(self.state.current_emotion, "")
+
+    def reset(self) -> None:
+        """Reset to calm baseline."""
+        self.state = EmotionalState()
+
+
+def _intensity_label(intensity: float) -> str:
+    """Human-readable label for intensity value."""
+    if intensity >= 0.8:
+        return "overwhelming"
+    if intensity >= 0.6:
+        return "strong"
+    if intensity >= 0.4:
+        return "moderate"
+    if intensity >= 0.2:
+        return "mild"
+    return "faint"

src/timmy/agents/loader.py

@@ -119,6 +119,8 @@ def load_agents(force_reload: bool = False) -> dict[str, Any]:
         max_history = agent_cfg.get("max_history", defaults.get("max_history", 10))
         tools = agent_cfg.get("tools", defaults.get("tools", []))
 
+        initial_emotion = agent_cfg.get("initial_emotion", "calm")
+
         agent = SubAgent(
             agent_id=agent_id,
             name=agent_cfg.get("name", agent_id.title()),
@@ -127,6 +129,7 @@ def load_agents(force_reload: bool = False) -> dict[str, Any]:
             tools=tools,
             model=model,
             max_history=max_history,
+            initial_emotion=initial_emotion,
         )
 
         _agents[agent_id] = agent

src/timmy/approvals.py

@@ -36,6 +36,8 @@ _EXPIRY_DAYS = 7
 
 @dataclass
 class ApprovalItem:
+    """A proposed autonomous action requiring owner approval."""
+
     id: str
     title: str
     description: str

src/timmy/autoresearch.py

@@ -8,7 +8,7 @@ Flow:
   1. prepare_experiment  — clone repo + run data prep
   2. run_experiment      — execute train.py with wall-clock timeout
   3. evaluate_result     — compare metric against baseline
-  4. experiment_loop     — orchestrate the full cycle
+  4. SystemExperiment    — orchestrate the full cycle via class interface
 
 All subprocess calls are guarded with timeouts for graceful degradation.
 """
@@ -17,9 +17,12 @@ from __future__ import annotations
 
 import json
 import logging
+import os
+import platform
 import re
 import subprocess
 import time
+from collections.abc import Callable
 from pathlib import Path
 from typing import Any
 
@@ -29,15 +32,61 @@ DEFAULT_REPO = "https://github.com/karpathy/autoresearch.git"
 _METRIC_RE = re.compile(r"val_bpb[:\s]+([0-9]+\.?[0-9]*)")
 
 
+# ── Higher-is-better metric names ────────────────────────────────────────────
+_HIGHER_IS_BETTER = frozenset({"unit_pass_rate", "coverage"})
+
+
+def is_apple_silicon() -> bool:
+    """Return True when running on Apple Silicon (M-series chip)."""
+    return platform.system() == "Darwin" and platform.machine() == "arm64"
+
+
+def _build_experiment_env(
+    dataset: str = "tinystories",
+    backend: str = "auto",
+) -> dict[str, str]:
+    """Build environment variables for an autoresearch subprocess.
+
+    Args:
+        dataset: Dataset name forwarded as ``AUTORESEARCH_DATASET``.
+            ``"tinystories"`` is recommended for Apple Silicon (lower entropy,
+            faster iteration).
+        backend: Inference backend forwarded as ``AUTORESEARCH_BACKEND``.
+            ``"auto"`` enables MLX on Apple Silicon; ``"cpu"`` forces CPU.
+
+    Returns:
+        Merged environment dict (inherits current process env).
+    """
+    env = os.environ.copy()
+    env["AUTORESEARCH_DATASET"] = dataset
+
+    if backend == "auto":
+        env["AUTORESEARCH_BACKEND"] = "mlx" if is_apple_silicon() else "cuda"
+    else:
+        env["AUTORESEARCH_BACKEND"] = backend
+
+    return env
+
+
 def prepare_experiment(
     workspace: Path,
     repo_url: str = DEFAULT_REPO,
+    dataset: str = "tinystories",
+    backend: str = "auto",
 ) -> str:
     """Clone autoresearch repo and run data preparation.
 
+    On Apple Silicon the ``dataset`` defaults to ``"tinystories"`` (lower
+    entropy, faster iteration) and ``backend`` to ``"auto"`` which resolves to
+    MLX.  Both values are forwarded as ``AUTORESEARCH_DATASET`` /
+    ``AUTORESEARCH_BACKEND`` environment variables so that ``prepare.py`` and
+    ``train.py`` can adapt their behaviour without CLI changes.
+
     Args:
         workspace: Directory to set up the experiment in.
         repo_url: Git URL for the autoresearch repository.
+        dataset: Dataset name; ``"tinystories"`` is recommended on Mac.
+        backend: Inference backend; ``"auto"`` picks MLX on Apple Silicon.
 
     Returns:
         Status message describing what was prepared.
@@ -59,6 +108,14 @@ def prepare_experiment(
     else:
         logger.info("Autoresearch repo already present at %s", repo_dir)
 
+    env = _build_experiment_env(dataset=dataset, backend=backend)
+    if is_apple_silicon():
+        logger.info(
+            "Apple Silicon detected — dataset=%s backend=%s",
+            env["AUTORESEARCH_DATASET"],
+            env["AUTORESEARCH_BACKEND"],
+        )
+
     # Run prepare.py (data download + tokeniser training)
     prepare_script = repo_dir / "prepare.py"
     if prepare_script.exists():
@@ -69,6 +126,7 @@ def prepare_experiment(
             text=True,
             cwd=str(repo_dir),
             timeout=300,
+            env=env,
         )
         if result.returncode != 0:
             return f"Preparation failed: {result.stderr.strip()[:500]}"
@@ -81,6 +139,8 @@ def run_experiment(
     workspace: Path,
     timeout: int = 300,
     metric_name: str = "val_bpb",
+    dataset: str = "tinystories",
+    backend: str = "auto",
 ) -> dict[str, Any]:
     """Run a single training experiment with a wall-clock timeout.
 
@@ -88,6 +148,9 @@ def run_experiment(
         workspace: Experiment workspace (contains autoresearch/ subdir).
         timeout: Maximum wall-clock seconds for the run.
         metric_name: Name of the metric to extract from stdout.
+        dataset: Dataset forwarded to the subprocess via env var.
+        backend: Inference backend forwarded via env var (``"auto"`` → MLX on
+            Apple Silicon, CUDA otherwise).
 
     Returns:
         Dict with keys: metric (float|None), log (str), duration_s (int),
@@ -105,6 +168,7 @@ def run_experiment(
             "error": f"train.py not found in {repo_dir}",
         }
 
+    env = _build_experiment_env(dataset=dataset, backend=backend)
     start = time.monotonic()
     try:
         result = subprocess.run(
@@ -113,6 +177,7 @@ def run_experiment(
             text=True,
             cwd=str(repo_dir),
             timeout=timeout,
+            env=env,
         )
         duration = int(time.monotonic() - start)
         output = result.stdout + result.stderr
@@ -125,7 +190,7 @@ def run_experiment(
             "log": output[-2000:],  # Keep last 2k chars
             "duration_s": duration,
             "success": result.returncode == 0,
-            "error": None if result.returncode == 0 else f"Exit code {result.returncode}",
+            "error": (None if result.returncode == 0 else f"Exit code {result.returncode}"),
         }
     except subprocess.TimeoutExpired:
         duration = int(time.monotonic() - start)
@@ -212,3 +277,369 @@ def _append_result(workspace: Path, result: dict[str, Any]) -> None:
     results_file.parent.mkdir(parents=True, exist_ok=True)
     with results_file.open("a") as f:
         f.write(json.dumps(result) + "\n")
+
+
+def _extract_pass_rate(output: str) -> float | None:
+    """Extract pytest pass rate as a percentage from tox/pytest output."""
+    passed_m = re.search(r"(\d+) passed", output)
+    failed_m = re.search(r"(\d+) failed", output)
+    if passed_m:
+        passed = int(passed_m.group(1))
+        failed = int(failed_m.group(1)) if failed_m else 0
+        total = passed + failed
+        return (passed / total * 100.0) if total > 0 else 100.0
+    return None
+
+
+def _extract_coverage(output: str) -> float | None:
+    """Extract total coverage percentage from coverage output."""
+    coverage_m = re.search(r"(?:TOTAL\s+\d+\s+\d+\s+|Total coverage:\s*)(\d+)%", output)
+    if coverage_m:
+        try:
+            return float(coverage_m.group(1))
+        except ValueError:
+            pass
+    return None
+
+
+class SystemExperiment:
+    """An autoresearch experiment targeting a specific module with a configurable metric.
+
+    Encapsulates the hypothesis → edit → tox → evaluate → commit/revert loop
+    for a single target file or module.
+
+    Args:
+        target: Path or module name to optimise (e.g. ``src/timmy/agent.py``).
+        metric: Metric to extract from tox output.  Built-in values:
+            ``unit_pass_rate`` (default), ``coverage``, ``val_bpb``.
+            Any other value is forwarded to :func:`_extract_metric`.
+        budget_minutes: Wall-clock budget per experiment (default 5 min).
+        workspace: Working directory for subprocess calls.  Defaults to ``cwd``.
+        revert_on_failure: Whether to revert changes on failed experiments.
+        hypothesis: Optional natural language hypothesis for the experiment.
+        metric_fn: Optional callable for custom metric extraction.
+            If provided, overrides built-in metric extraction.
+    """
+
+    def __init__(
+        self,
+        target: str,
+        metric: str = "unit_pass_rate",
+        budget_minutes: int = 5,
+        workspace: Path | None = None,
+        revert_on_failure: bool = True,
+        hypothesis: str = "",
+        metric_fn: Callable[[str], float | None] | None = None,
+    ) -> None:
+        self.target = target
+        self.metric = metric
+        self.budget_seconds = budget_minutes * 60
+        self.workspace = Path(workspace) if workspace else Path.cwd()
+        self.revert_on_failure = revert_on_failure
+        self.hypothesis = hypothesis
+        self.metric_fn = metric_fn
+        self.results: list[dict[str, Any]] = []
+        self.baseline: float | None = None
+
+    # ── Hypothesis generation ─────────────────────────────────────────────────
+
+    def generate_hypothesis(self, program_content: str = "") -> str:
+        """Return a plain-English hypothesis for the next experiment.
+
+        Uses the first non-empty line of *program_content* when available;
+        falls back to a generic description based on target and metric.
+        """
+        first_line = ""
+        for line in program_content.splitlines():
+            stripped = line.strip()
+            if stripped and not stripped.startswith("#"):
+                first_line = stripped[:120]
+                break
+        if first_line:
+            return f"[{self.target}] {first_line}"
+        return f"Improve {self.metric} for {self.target}"
+
+    # ── Edit phase ────────────────────────────────────────────────────────────
+
+    def apply_edit(self, hypothesis: str, model: str = "qwen3:30b") -> str:
+        """Apply code edits to *target* via Aider.
+
+        Returns a status string.  Degrades gracefully — never raises.
+        """
+        prompt = f"Edit {self.target}: {hypothesis}"
+        try:
+            result = subprocess.run(
+                ["aider", "--no-git", "--model", f"ollama/{model}", "--quiet", prompt],
+                capture_output=True,
+                text=True,
+                timeout=self.budget_seconds,
+                cwd=str(self.workspace),
+            )
+            if result.returncode == 0:
+                return result.stdout or "Edit applied."
+            return f"Aider error (exit {result.returncode}): {result.stderr[:500]}"
+        except FileNotFoundError:
+            logger.warning("Aider not installed — edit skipped")
+            return "Aider not available — edit skipped"
+        except subprocess.TimeoutExpired:
+            logger.warning("Aider timed out after %ds", self.budget_seconds)
+            return "Aider timed out"
+        except (OSError, subprocess.SubprocessError) as exc:
+            logger.warning("Aider failed: %s", exc)
+            return f"Edit failed: {exc}"
+
+    # ── Evaluation phase ──────────────────────────────────────────────────────
+
+    def run_tox(self, tox_env: str = "unit") -> dict[str, Any]:
+        """Run *tox_env* and return a result dict.
+
+        Returns:
+            Dict with keys: ``metric`` (float|None), ``log`` (str),
+            ``duration_s`` (int), ``success`` (bool), ``error`` (str|None).
+        """
+        start = time.monotonic()
+        try:
+            result = subprocess.run(
+                ["tox", "-e", tox_env],
+                capture_output=True,
+                text=True,
+                timeout=self.budget_seconds,
+                cwd=str(self.workspace),
+            )
+            duration = int(time.monotonic() - start)
+            output = result.stdout + result.stderr
+            metric_val = self._extract_tox_metric(output)
+            return {
+                "metric": metric_val,
+                "log": output[-3000:],
+                "duration_s": duration,
+                "success": result.returncode == 0,
+                "error": (None if result.returncode == 0 else f"Exit code {result.returncode}"),
+            }
+        except subprocess.TimeoutExpired:
+            duration = int(time.monotonic() - start)
+            return {
+                "metric": None,
+                "log": f"Budget exceeded after {self.budget_seconds}s",
+                "duration_s": duration,
+                "success": False,
+                "error": f"Budget exceeded after {self.budget_seconds}s",
+            }
+        except OSError as exc:
+            return {
+                "metric": None,
+                "log": "",
+                "duration_s": 0,
+                "success": False,
+                "error": str(exc),
+            }
+
+    def _extract_tox_metric(self, output: str) -> float | None:
+        """Dispatch to the correct metric extractor based on *self.metric*."""
+        # Use custom metric function if provided
+        if self.metric_fn is not None:
+            try:
+                return self.metric_fn(output)
+            except Exception as exc:
+                logger.warning("Custom metric_fn failed: %s", exc)
+                return None
+
+        if self.metric == "unit_pass_rate":
+            return _extract_pass_rate(output)
+        if self.metric == "coverage":
+            return _extract_coverage(output)
+        return _extract_metric(output, self.metric)
+
+    def evaluate(self, current: float | None, baseline: float | None) -> str:
+        """Compare *current* metric against *baseline* and return an assessment."""
+        if current is None:
+            return "Indeterminate: metric not extracted from output"
+        if baseline is None:
+            unit = "%" if self.metric in _HIGHER_IS_BETTER else ""
+            return f"Baseline: {self.metric} = {current:.2f}{unit}"
+
+        if self.metric in _HIGHER_IS_BETTER:
+            delta = current - baseline
+            pct = (delta / baseline * 100) if baseline != 0 else 0.0
+            if delta > 0:
+                return f"Improvement: {self.metric} {baseline:.2f}% → {current:.2f}% ({pct:+.2f}%)"
+            if delta < 0:
+                return f"Regression: {self.metric} {baseline:.2f}% → {current:.2f}% ({pct:+.2f}%)"
+            return f"No change: {self.metric} = {current:.2f}%"
+
+        # lower-is-better (val_bpb, loss, etc.)
+        return evaluate_result(current, baseline, self.metric)
+
+    def is_improvement(self, current: float, baseline: float) -> bool:
+        """Return True if *current* is better than *baseline* for this metric."""
+        if self.metric in _HIGHER_IS_BETTER:
+            return current > baseline
+        return current < baseline  # lower-is-better
+
+    # ── Git phase ─────────────────────────────────────────────────────────────
+
+    def create_branch(self, branch_name: str) -> bool:
+        """Create and checkout a new git branch. Returns True on success."""
+        try:
+            subprocess.run(
+                ["git", "checkout", "-b", branch_name],
+                cwd=str(self.workspace),
+                check=True,
+                timeout=30,
+            )
+            return True
+        except subprocess.CalledProcessError as exc:
+            logger.warning("Git branch creation failed: %s", exc)
+            return False
+
+    def commit_changes(self, message: str) -> bool:
+        """Stage and commit all changes.  Returns True on success."""
+        try:
+            subprocess.run(["git", "add", "-A"], cwd=str(self.workspace), check=True, timeout=30)
+            subprocess.run(
+                ["git", "commit", "-m", message],
+                cwd=str(self.workspace),
+                check=True,
+                timeout=30,
+            )
+            return True
+        except subprocess.CalledProcessError as exc:
+            logger.warning("Git commit failed: %s", exc)
+            return False
+
+    def revert_changes(self) -> bool:
+        """Revert all uncommitted changes.  Returns True on success."""
+        try:
+            subprocess.run(
+                ["git", "checkout", "--", "."],
+                cwd=str(self.workspace),
+                check=True,
+                timeout=30,
+            )
+            return True
+        except subprocess.CalledProcessError as exc:
+            logger.warning("Git revert failed: %s", exc)
+            return False
+
+    # ── Full experiment loop ──────────────────────────────────────────────────
+
+    def run(
+        self,
+        tox_env: str = "unit",
+        model: str = "qwen3:30b",
+        program_content: str = "",
+        max_iterations: int = 1,
+        dry_run: bool = False,
+        create_branch: bool = False,
+    ) -> dict[str, Any]:
+        """Run the full experiment loop: hypothesis → edit → tox → evaluate → commit/revert.
+
+        This method encapsulates the complete experiment cycle, running multiple
+        iterations until an improvement is found or max_iterations is reached.
+
+        Args:
+            tox_env: Tox environment to run (default "unit").
+            model: Ollama model for Aider edits (default "qwen3:30b").
+            program_content: Research direction for hypothesis generation.
+            max_iterations: Maximum number of experiment iterations.
+            dry_run: If True, only generate hypotheses without making changes.
+            create_branch: If True, create a new git branch for the experiment.
+
+        Returns:
+            Dict with keys: ``success`` (bool), ``final_metric`` (float|None),
+            ``baseline`` (float|None), ``iterations`` (int), ``results`` (list).
+        """
+        if create_branch:
+            branch_name = f"autoresearch/{self.target.replace('/', '-')}-{int(time.time())}"
+            self.create_branch(branch_name)
+
+        baseline: float | None = self.baseline
+        final_metric: float | None = None
+        success = False
+
+        for iteration in range(1, max_iterations + 1):
+            logger.info("Experiment iteration %d/%d", iteration, max_iterations)
+
+            # Generate hypothesis
+            hypothesis = self.hypothesis or self.generate_hypothesis(program_content)
+            logger.info("Hypothesis: %s", hypothesis)
+
+            # In dry-run mode, just record the hypothesis and continue
+            if dry_run:
+                result_record = {
+                    "iteration": iteration,
+                    "hypothesis": hypothesis,
+                    "metric": None,
+                    "baseline": baseline,
+                    "assessment": "Dry-run: no changes made",
+                    "success": True,
+                    "duration_s": 0,
+                }
+                self.results.append(result_record)
+                continue
+
+            # Apply edit
+            edit_result = self.apply_edit(hypothesis, model=model)
+            edit_failed = "not available" in edit_result or edit_result.startswith("Aider error")
+            if edit_failed:
+                logger.warning("Edit phase failed: %s", edit_result)
+
+            # Run evaluation
+            tox_result = self.run_tox(tox_env=tox_env)
+            metric = tox_result["metric"]
+
+            # Evaluate result
+            assessment = self.evaluate(metric, baseline)
+            logger.info("Assessment: %s", assessment)
+
+            # Store result
+            result_record = {
+                "iteration": iteration,
+                "hypothesis": hypothesis,
+                "metric": metric,
+                "baseline": baseline,
+                "assessment": assessment,
+                "success": tox_result["success"],
+                "duration_s": tox_result["duration_s"],
+            }
+            self.results.append(result_record)
+
+            # Set baseline on first successful run
+            if metric is not None and baseline is None:
+                baseline = metric
+                self.baseline = baseline
+                final_metric = metric
+                continue
+
+            # Determine if we should commit or revert
+            should_commit = False
+            if tox_result["success"] and metric is not None and baseline is not None:
+                if self.is_improvement(metric, baseline):
+                    should_commit = True
+                    final_metric = metric
+                    baseline = metric
+                    self.baseline = baseline
+                    success = True
+
+            if should_commit:
+                commit_msg = f"autoresearch: improve {self.metric} on {self.target}\n\n{hypothesis}"
+                if self.commit_changes(commit_msg):
+                    logger.info("Changes committed")
+                else:
+                    self.revert_changes()
+                    logger.warning("Commit failed, changes reverted")
+            elif self.revert_on_failure:
+                self.revert_changes()
+                logger.info("Changes reverted (no improvement)")
+
+            # Early exit if we found an improvement
+            if success:
+                break
+
+        return {
+            "success": success,
+            "final_metric": final_metric,
+            "baseline": self.baseline,
+            "iterations": len(self.results),
+            "results": self.results,
+        }

src/timmy/backlog_triage.py

@@ -0,0 +1,761 @@
+"""Autonomous backlog triage loop — Timmy scans Gitea and assigns work.
+
+Continuously fetches open issues, scores/prioritizes them, and decides
+what to work on next without waiting to be asked.
+
+Loop flow::
+
+    while true:
+        1. Fetch all open issues from Gitea API
+        2. Score/prioritize by labels, age, type, blocked status
+        3. Identify unassigned high-priority items
+        4. Decide: assign to claude, dispatch to kimi, or flag for Alex
+        5. Execute the assignment (comment + assign)
+        6. Optionally post a daily triage summary
+        7. Sleep for configurable interval (default 15 min)
+
+Priority tiers:
+    P0 — security, data loss, blocking bugs → immediate action
+    P1 — core functionality, ready issues → next sprint
+    P2 — improvements, low-score issues → backlog
+    P3 — philosophy, meta → someday/never (skip in triage)
+
+Usage::
+
+    from timmy.backlog_triage import BacklogTriageLoop
+
+    loop = BacklogTriageLoop()
+    await loop.run_once()           # single triage cycle
+    await loop.start()              # background daemon loop
+    loop.stop()                     # graceful shutdown
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import re
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import Any
+
+import httpx
+
+from config import settings
+
+logger = logging.getLogger(__name__)
+
+# ── Constants ────────────────────────────────────────────────────────────────
+
+# Minimum triage score to be considered "ready" for assignment
+READY_THRESHOLD = 5
+
+# Agent Gitea logins
+AGENT_CLAUDE = "claude"
+AGENT_KIMI = "kimi"
+OWNER_LOGIN = "rockachopa"  # Alex — human owner
+
+# Labels
+KIMI_READY_LABEL = "kimi-ready"
+TRIAGE_DONE_LABEL = "triage-done"
+
+# Tag sets (mirrors scripts/triage_score.py)
+_BUG_TAGS = frozenset({"bug", "broken", "crash", "error", "fix", "regression", "hotfix"})
+_FEATURE_TAGS = frozenset({"feature", "feat", "enhancement", "capability", "timmy-capability"})
+_REFACTOR_TAGS = frozenset({"refactor", "cleanup", "tech-debt", "optimization", "perf"})
+_META_TAGS = frozenset({"philosophy", "soul-gap", "discussion", "question", "rfc"})
+_P0_TAGS = frozenset({"security", "data-loss", "blocking", "p0", "critical"})
+_RESEARCH_TAGS = frozenset({"research", "kimi-ready", "investigation", "spike"})
+_LOOP_TAG = "loop-generated"
+
+# Regex patterns for scoring
+_TAG_RE = re.compile(r"\[([^\]]+)\]")
+_FILE_RE = re.compile(
+    r"(?:src/|tests/|scripts/|\.py|\.html|\.js|\.yaml|\.toml|\.sh)", re.IGNORECASE
+)
+_FUNC_RE = re.compile(r"(?:def |class |function |method |`\w+\(\)`)", re.IGNORECASE)
+_ACCEPT_RE = re.compile(
+    r"(?:should|must|expect|verify|assert|test.?case|acceptance|criteria"
+    r"|pass(?:es|ing)|fail(?:s|ing)|return(?:s)?|raise(?:s)?)",
+    re.IGNORECASE,
+)
+_TEST_RE = re.compile(r"(?:tox|pytest|test_\w+|\.test\.|assert\s)", re.IGNORECASE)
+_BLOCKED_RE = re.compile(r"\bblock(?:ed|s|ing)\b", re.IGNORECASE)
+
+
+# ── Data types ───────────────────────────────────────────────────────────────
+
+
+@dataclass
+class ScoredIssue:
+    """A Gitea issue enriched with triage scoring."""
+
+    number: int
+    title: str
+    body: str
+    labels: list[str]
+    tags: set[str]
+    assignees: list[str]
+    created_at: datetime
+    issue_type: str  # bug | feature | refactor | philosophy | research | unknown
+
+    score: int = 0
+    scope: int = 0
+    acceptance: int = 0
+    alignment: int = 0
+    ready: bool = False
+    age_days: int = 0
+    is_p0: bool = False
+    is_blocked: bool = False
+
+    @property
+    def is_unassigned(self) -> bool:
+        return len(self.assignees) == 0
+
+    @property
+    def needs_kimi(self) -> bool:
+        return bool(self.tags & _RESEARCH_TAGS) or KIMI_READY_LABEL in self.labels
+
+
+@dataclass
+class TriageDecision:
+    """The outcome of a triage decision for a single issue."""
+
+    issue_number: int
+    action: str  # "assign_claude" | "assign_kimi" | "flag_alex" | "skip"
+    reason: str
+    agent: str = ""  # the agent assigned (login)
+    executed: bool = False
+    error: str = ""
+
+
+@dataclass
+class TriageCycleResult:
+    """Summary of one complete triage cycle."""
+
+    timestamp: str
+    total_open: int
+    scored: int
+    ready: int
+    decisions: list[TriageDecision] = field(default_factory=list)
+    errors: list[str] = field(default_factory=list)
+    duration_ms: int = 0
+
+
+# ── Scoring ──────────────────────────────────────────────────────────────────
+
+
+def _extract_tags(title: str, labels: list[str]) -> set[str]:
+    """Pull tags from [bracket] title notation + Gitea label names."""
+    tags: set[str] = set()
+    for m in _TAG_RE.finditer(title):
+        tags.add(m.group(1).lower().strip())
+    for lbl in labels:
+        tags.add(lbl.lower().strip())
+    return tags
+
+
+def _score_scope(title: str, body: str, tags: set[str]) -> int:
+    """0–3: How well-scoped is this issue?"""
+    text = f"{title}\n{body}"
+    score = 0
+    if _FILE_RE.search(text):
+        score += 1
+    if _FUNC_RE.search(text):
+        score += 1
+    clean = _TAG_RE.sub("", title).strip()
+    if len(clean) < 80:
+        score += 1
+    if tags & _META_TAGS:
+        score = max(0, score - 2)
+    return min(3, score)
+
+
+def _score_acceptance(title: str, body: str, tags: set[str]) -> int:
+    """0–3: Does this have clear acceptance criteria?"""
+    text = f"{title}\n{body}"
+    score = 0
+    matches = len(_ACCEPT_RE.findall(text))
+    if matches >= 3:
+        score += 2
+    elif matches >= 1:
+        score += 1
+    if _TEST_RE.search(text):
+        score += 1
+    if re.search(r"##\s*(problem|solution|expected|actual|steps)", body, re.IGNORECASE):
+        score += 1
+    if tags & _META_TAGS:
+        score = max(0, score - 1)
+    return min(3, score)
+
+
+def _score_alignment(title: str, body: str, tags: set[str]) -> int:
+    """0–3: How aligned is this with the north star?"""
+    score = 0
+    if tags & _BUG_TAGS:
+        return 3
+    if tags & _REFACTOR_TAGS:
+        score += 2
+    if tags & _FEATURE_TAGS:
+        score += 2
+    if _LOOP_TAG in tags:
+        score += 1
+    if tags & _META_TAGS:
+        score = 0
+    return min(3, score)
+
+
+def score_issue(issue: dict[str, Any]) -> ScoredIssue:
+    """Score and classify a raw Gitea issue dict."""
+    number = issue["number"]
+    title = issue.get("title", "")
+    body = issue.get("body") or ""
+    label_names = [lbl["name"] for lbl in issue.get("labels", [])]
+    tags = _extract_tags(title, label_names)
+    assignees = [a["login"] for a in issue.get("assignees", [])]
+
+    # Parse created_at
+    raw_ts = issue.get("created_at", "")
+    try:
+        created_at = datetime.fromisoformat(raw_ts.replace("Z", "+00:00"))
+    except (ValueError, AttributeError):
+        created_at = datetime.now(UTC)
+    age_days = (datetime.now(UTC) - created_at).days
+
+    # Scores
+    scope = _score_scope(title, body, tags)
+    acceptance = _score_acceptance(title, body, tags)
+    alignment = _score_alignment(title, body, tags)
+    total = scope + acceptance + alignment
+
+    # Classify
+    if tags & _BUG_TAGS:
+        issue_type = "bug"
+    elif tags & _RESEARCH_TAGS:
+        issue_type = "research"
+    elif tags & _FEATURE_TAGS:
+        issue_type = "feature"
+    elif tags & _REFACTOR_TAGS:
+        issue_type = "refactor"
+    elif tags & _META_TAGS:
+        issue_type = "philosophy"
+    else:
+        issue_type = "unknown"
+
+    is_p0 = bool(tags & _P0_TAGS) or issue_type == "bug"
+    is_blocked = bool(_BLOCKED_RE.search(title) or _BLOCKED_RE.search(body))
+
+    return ScoredIssue(
+        number=number,
+        title=_TAG_RE.sub("", title).strip(),
+        body=body,
+        labels=label_names,
+        tags=tags,
+        assignees=assignees,
+        created_at=created_at,
+        issue_type=issue_type,
+        score=total,
+        scope=scope,
+        acceptance=acceptance,
+        alignment=alignment,
+        ready=total >= READY_THRESHOLD,
+        age_days=age_days,
+        is_p0=is_p0,
+        is_blocked=is_blocked,
+    )
+
+
+# ── Decision logic ───────────────────────────────────────────────────────────
+
+
+def decide(issue: ScoredIssue) -> TriageDecision:
+    """Decide what to do with an issue.
+
+    Returns a TriageDecision with action, reason, and agent.
+    Decision is not yet executed — call execute_decision() for that.
+    """
+    num = issue.number
+
+    # Skip philosophy/meta — not dev-actionable
+    if issue.issue_type == "philosophy":
+        return TriageDecision(
+            issue_number=num,
+            action="skip",
+            reason="Philosophy/meta issue — not dev-actionable in the triage loop.",
+        )
+
+    # Skip already-assigned issues
+    if not issue.is_unassigned:
+        return TriageDecision(
+            issue_number=num,
+            action="skip",
+            reason=f"Already assigned to: {', '.join(issue.assignees)}.",
+        )
+
+    # Skip if not ready (low score)
+    if not issue.ready:
+        return TriageDecision(
+            issue_number=num,
+            action="skip",
+            reason=f"Score {issue.score} < {READY_THRESHOLD} threshold — needs more detail before assignment.",
+        )
+
+    # Blocked: flag for Alex
+    if issue.is_blocked:
+        return TriageDecision(
+            issue_number=num,
+            action="flag_alex",
+            agent=OWNER_LOGIN,
+            reason=(
+                "Issue appears blocked. Flagging for @rockachopa to unblock before autonomous assignment."
+            ),
+        )
+
+    # Research / Kimi-ready
+    if issue.needs_kimi:
+        return TriageDecision(
+            issue_number=num,
+            action="assign_kimi",
+            agent=AGENT_KIMI,
+            reason=(
+                f"Issue type '{issue.issue_type}' with research/investigation scope. "
+                f"Assigning kimi-ready label for Kimi agent to pick up."
+            ),
+        )
+
+    # P0 bugs and blocking issues → Claude immediately
+    if issue.is_p0:
+        return TriageDecision(
+            issue_number=num,
+            action="assign_claude",
+            agent=AGENT_CLAUDE,
+            reason=(
+                f"P0/{issue.issue_type} issue (score={issue.score}, age={issue.age_days}d). "
+                f"Assigning to Claude Code for immediate attention."
+            ),
+        )
+
+    # Everything else that is ready → Claude Code
+    return TriageDecision(
+        issue_number=num,
+        action="assign_claude",
+        agent=AGENT_CLAUDE,
+        reason=(
+            f"Unassigned ready issue (type={issue.issue_type}, score={issue.score}, "
+            f"age={issue.age_days}d). Assigning to Claude Code."
+        ),
+    )
+
+
+# ── Gitea API client ─────────────────────────────────────────────────────────
+
+
+def _api_headers() -> dict[str, str]:
+    return {
+        "Authorization": f"token {settings.gitea_token}",
+        "Content-Type": "application/json",
+        "Accept": "application/json",
+    }
+
+
+def _repo_url(path: str) -> str:
+    owner, repo = settings.gitea_repo.split("/", 1)
+    return f"{settings.gitea_url}/api/v1/repos/{owner}/{repo}/{path}"
+
+
+async def fetch_open_issues(client: httpx.AsyncClient) -> list[dict[str, Any]]:
+    """Fetch all open issues from Gitea, paginating as needed."""
+    all_issues: list[dict[str, Any]] = []
+    page = 1
+    while True:
+        url = _repo_url(f"issues?state=open&type=issues&limit=50&page={page}")
+        try:
+            resp = await client.get(url, headers=_api_headers())
+            if resp.status_code != 200:
+                logger.warning("Gitea issues fetch failed (HTTP %s)", resp.status_code)
+                break
+            batch: list[dict[str, Any]] = resp.json()
+            if not batch:
+                break
+            all_issues.extend(batch)
+            if len(batch) < 50:
+                break
+            page += 1
+        except (httpx.ConnectError, httpx.ReadError, httpx.TimeoutException) as exc:
+            logger.warning("Gitea connection error fetching issues: %s", exc)
+            break
+    return all_issues
+
+
+async def post_comment(
+    client: httpx.AsyncClient,
+    issue_number: int,
+    body: str,
+) -> bool:
+    """Post a comment on a Gitea issue. Returns True on success."""
+    url = _repo_url(f"issues/{issue_number}/comments")
+    try:
+        resp = await client.post(url, headers=_api_headers(), json={"body": body})
+        return resp.status_code in (200, 201)
+    except (httpx.ConnectError, httpx.ReadError, httpx.TimeoutException) as exc:
+        logger.warning("Failed to post comment on #%d: %s", issue_number, exc)
+        return False
+
+
+async def assign_issue(
+    client: httpx.AsyncClient,
+    issue_number: int,
+    assignee: str,
+) -> bool:
+    """Assign an issue to a Gitea user. Returns True on success."""
+    url = _repo_url(f"issues/{issue_number}")
+    try:
+        resp = await client.patch(
+            url,
+            headers=_api_headers(),
+            json={"assignees": [assignee]},
+        )
+        return resp.status_code in (200, 201)
+    except (httpx.ConnectError, httpx.ReadError, httpx.TimeoutException) as exc:
+        logger.warning("Failed to assign #%d to %s: %s", issue_number, assignee, exc)
+        return False
+
+
+async def add_label(
+    client: httpx.AsyncClient,
+    issue_number: int,
+    label_name: str,
+) -> bool:
+    """Add a label to a Gitea issue by name (auto-creates if missing). Returns True on success."""
+    owner, repo = settings.gitea_repo.split("/", 1)
+    labels_url = f"{settings.gitea_url}/api/v1/repos/{owner}/{repo}/labels"
+    headers = _api_headers()
+
+    try:
+        # Fetch existing labels
+        resp = await client.get(labels_url, headers=headers)
+        if resp.status_code != 200:
+            return False
+        existing = {lbl["name"]: lbl["id"] for lbl in resp.json()}
+
+        if label_name in existing:
+            label_id = existing[label_name]
+        else:
+            # Auto-create the label
+            create_resp = await client.post(
+                labels_url,
+                headers=headers,
+                json={"name": label_name, "color": "#006b75"},
+            )
+            if create_resp.status_code not in (200, 201):
+                return False
+            label_id = create_resp.json()["id"]
+
+        # Apply to the issue
+        apply_url = _repo_url(f"issues/{issue_number}/labels")
+        apply_resp = await client.post(apply_url, headers=headers, json={"labels": [label_id]})
+        return apply_resp.status_code in (200, 201)
+
+    except (httpx.ConnectError, httpx.ReadError, httpx.TimeoutException) as exc:
+        logger.warning("Failed to add label %r to #%d: %s", label_name, issue_number, exc)
+        return False
+
+
+# ── Decision execution ───────────────────────────────────────────────────────
+
+
+async def execute_decision(
+    client: httpx.AsyncClient,
+    decision: TriageDecision,
+    dry_run: bool = False,
+) -> TriageDecision:
+    """Execute a triage decision — comment + assign/label.
+
+    When dry_run=True, logs the decision but makes no Gitea API calls.
+    Returns the updated decision with executed=True on success.
+    """
+    num = decision.issue_number
+
+    if decision.action == "skip":
+        logger.debug("Triage skip #%d: %s", num, decision.reason)
+        decision.executed = True
+        return decision
+
+    audit_comment = _build_audit_comment(decision)
+
+    if dry_run:
+        logger.info(
+            "[DRY RUN] #%d → %s (%s): %s",
+            num,
+            decision.action,
+            decision.agent,
+            decision.reason,
+        )
+        decision.executed = True
+        return decision
+
+    # Post audit comment first (always, so Alex can see reasoning)
+    comment_ok = await post_comment(client, num, audit_comment)
+    if not comment_ok:
+        decision.error = "Failed to post audit comment"
+        logger.warning("Triage #%d: comment failed", num)
+        return decision
+
+    # Execute assignment
+    ok = False
+    if decision.action == "assign_claude":
+        ok = await assign_issue(client, num, AGENT_CLAUDE)
+    elif decision.action == "assign_kimi":
+        ok = await add_label(client, num, KIMI_READY_LABEL)
+    elif decision.action == "flag_alex":
+        # Comment already posted above — that's sufficient for flagging
+        ok = True
+
+    if ok:
+        decision.executed = True
+        logger.info("Triage #%d → %s OK", num, decision.action)
+    else:
+        decision.error = f"Action {decision.action!r} failed"
+        logger.warning("Triage #%d: action %r failed", num, decision.action)
+
+    return decision
+
+
+def _build_audit_comment(decision: TriageDecision) -> str:
+    """Build the audit trail comment that Alex can read to see reasoning."""
+    ts = datetime.now(UTC).strftime("%Y-%m-%d %H:%M UTC")
+    action_text = {
+        "assign_claude": f"Assigning to @{AGENT_CLAUDE} for implementation.",
+        "assign_kimi": f"Adding `{KIMI_READY_LABEL}` label — queuing for Kimi research agent.",
+        "flag_alex": f"Flagging for @{OWNER_LOGIN} — issue appears blocked or needs human decision.",
+    }.get(decision.action, decision.action)
+
+    return (
+        f"**[Timmy Triage — {ts}]**\n\n"
+        f"**Decision:** {action_text}\n\n"
+        f"**Why:** {decision.reason}\n\n"
+        f"*Autonomous triage by Timmy. Reply to override.*"
+    )
+
+
+# ── Daily summary ─────────────────────────────────────────────────────────────
+
+
+def _build_daily_summary(result: TriageCycleResult, scored: list[ScoredIssue]) -> str:
+    """Build the daily triage summary body."""
+    now = datetime.now(UTC).strftime("%Y-%m-%d %H:%M UTC")
+    assigned = [d for d in result.decisions if d.executed and d.action != "skip"]
+    skipped = [d for d in result.decisions if d.action == "skip"]
+
+    lines = [
+        f"# Timmy Backlog Triage — {now}",
+        "",
+        f"**Open issues:** {result.total_open}  |  "
+        f"**Scored:** {result.scored}  |  "
+        f"**Ready:** {result.ready}  |  "
+        f"**Assigned this cycle:** {len(assigned)}",
+        "",
+        "## Top 10 Ready Issues (by score)",
+        "",
+    ]
+
+    top = sorted([s for s in scored if s.ready], key=lambda s: (-s.score, s.number))[:10]
+    for s in top:
+        flag = "🐛" if s.issue_type == "bug" else "⚡" if s.is_p0 else "✦"
+        lines.append(
+            f"- {flag} **#{s.number}** (score={s.score}, age={s.age_days}d) — {s.title[:80]}"
+        )
+
+    if assigned:
+        lines += ["", "## Actions Taken", ""]
+        for d in assigned:
+            lines.append(f"- #{d.issue_number} → `{d.action}` ({d.agent}): {d.reason[:100]}")
+
+    if skipped:
+        lines += ["", f"## Skipped ({len(skipped)} issues)", ""]
+        for d in skipped[:5]:
+            lines.append(f"- #{d.issue_number}: {d.reason[:80]}")
+        if len(skipped) > 5:
+            lines.append(f"- … and {len(skipped) - 5} more")
+
+    lines += [
+        "",
+        "---",
+        "*Auto-generated by Timmy's backlog triage loop. "
+        "Override any decision by reassigning or commenting.*",
+    ]
+    return "\n".join(lines)
+
+
+async def post_daily_summary(
+    client: httpx.AsyncClient,
+    result: TriageCycleResult,
+    scored: list[ScoredIssue],
+    dry_run: bool = False,
+) -> bool:
+    """Post a daily triage summary as a new Gitea issue."""
+    today = datetime.now(UTC).strftime("%Y-%m-%d")
+    title = f"[Triage] Daily backlog summary — {today}"
+    body = _build_daily_summary(result, scored)
+
+    if dry_run:
+        logger.info("[DRY RUN] Would post daily summary: %s", title)
+        return True
+
+    url = _repo_url("issues")
+    try:
+        resp = await client.post(
+            url,
+            headers=_api_headers(),
+            json={
+                "title": title,
+                "body": body,
+                "labels": [],
+            },
+        )
+        if resp.status_code in (200, 201):
+            issue_num = resp.json().get("number", "?")
+            logger.info("Daily triage summary posted as issue #%s", issue_num)
+            return True
+        logger.warning("Daily summary post failed (HTTP %s)", resp.status_code)
+        return False
+    except (httpx.ConnectError, httpx.ReadError, httpx.TimeoutException) as exc:
+        logger.warning("Failed to post daily summary: %s", exc)
+        return False
+
+
+# ── Main loop class ───────────────────────────────────────────────────────────
+
+
+class BacklogTriageLoop:
+    """Autonomous backlog triage loop.
+
+    Fetches, scores, and assigns Gitea issues on a configurable interval.
+
+    Parameters
+    ----------
+    interval:
+        Seconds between triage cycles. Default: settings.backlog_triage_interval_seconds.
+    dry_run:
+        When True, score and log decisions but don't write to Gitea.
+    daily_summary:
+        When True, post a daily triage summary issue after each cycle.
+    """
+
+    def __init__(
+        self,
+        *,
+        interval: float | None = None,
+        dry_run: bool | None = None,
+        daily_summary: bool | None = None,
+    ) -> None:
+        self._interval = float(interval or settings.backlog_triage_interval_seconds)
+        self._dry_run = dry_run if dry_run is not None else settings.backlog_triage_dry_run
+        self._daily_summary = (
+            daily_summary if daily_summary is not None else settings.backlog_triage_daily_summary
+        )
+        self._running = False
+        self._task: asyncio.Task | None = None
+        self._cycle_count = 0
+        self._last_summary_date: str = ""
+        self.history: list[TriageCycleResult] = []
+
+    @property
+    def is_running(self) -> bool:
+        return self._running
+
+    @property
+    def cycle_count(self) -> int:
+        return self._cycle_count
+
+    async def run_once(self) -> TriageCycleResult:
+        """Execute one full triage cycle.
+
+        1. Fetch all open Gitea issues
+        2. Score and prioritize
+        3. Decide on each unassigned ready issue
+        4. Execute decisions
+        5. Optionally post daily summary
+        """
+        import time
+
+        self._cycle_count += 1
+        start = time.monotonic()
+        ts = datetime.now(UTC).isoformat()
+        result = TriageCycleResult(timestamp=ts, total_open=0, scored=0, ready=0)
+
+        if not settings.gitea_enabled or not settings.gitea_token:
+            logger.warning("Backlog triage: Gitea not configured — skipping cycle")
+            return result
+
+        async with httpx.AsyncClient(timeout=30) as client:
+            # 1. Fetch
+            raw_issues = await fetch_open_issues(client)
+            result.total_open = len(raw_issues)
+            logger.info(
+                "Triage cycle #%d: fetched %d open issues", self._cycle_count, len(raw_issues)
+            )
+
+            # 2. Score
+            scored = [score_issue(i) for i in raw_issues]
+            result.scored = len(scored)
+            result.ready = sum(1 for s in scored if s.ready)
+
+            # 3 & 4. Decide and execute for each issue
+            for issue in scored:
+                decision = decide(issue)
+                if decision.action == "skip":
+                    result.decisions.append(decision)
+                    continue
+                decision = await execute_decision(client, decision, dry_run=self._dry_run)
+                result.decisions.append(decision)
+
+                # Rate-limit: short pause between API writes to avoid hammering Gitea
+                if not self._dry_run:
+                    await asyncio.sleep(0.5)
+
+            # 5. Daily summary (once per UTC day)
+            today = datetime.now(UTC).strftime("%Y-%m-%d")
+            if self._daily_summary and today != self._last_summary_date:
+                await post_daily_summary(client, result, scored, dry_run=self._dry_run)
+                self._last_summary_date = today
+
+        result.duration_ms = int((time.monotonic() - start) * 1000)
+        self.history.append(result)
+
+        assigned_count = sum(1 for d in result.decisions if d.executed and d.action != "skip")
+        logger.info(
+            "Triage cycle #%d complete (%d ms): %d open, %d ready, %d assigned",
+            self._cycle_count,
+            result.duration_ms,
+            result.total_open,
+            result.ready,
+            assigned_count,
+        )
+        return result
+
+    async def start(self) -> None:
+        """Start the triage loop as a background task."""
+        if self._running:
+            logger.warning("BacklogTriageLoop already running")
+            return
+        self._running = True
+        await self._loop()
+
+    async def _loop(self) -> None:
+        logger.info(
+            "BacklogTriageLoop started (interval=%.0fs, dry_run=%s)",
+            self._interval,
+            self._dry_run,
+        )
+        while self._running:
+            try:
+                await self.run_once()
+            except Exception:
+                logger.exception("Backlog triage cycle failed")
+            await asyncio.sleep(self._interval)
+
+    def stop(self) -> None:
+        """Signal the loop to stop after the current cycle."""
+        self._running = False
+        logger.info("BacklogTriageLoop stop requested")

src/timmy/briefing.py

@@ -46,6 +46,8 @@ class ApprovalItem:
 
 @dataclass
 class Briefing:
+    """A generated morning briefing summarizing recent activity and pending approvals."""
+
     generated_at: datetime
     summary: str  # 150-300 words
     approval_items: list[ApprovalItem] = field(default_factory=list)

src/timmy/cli.py

@@ -347,7 +347,10 @@ def interview(
         # Force agent creation by calling chat once with a warm-up prompt
         try:
             loop.run_until_complete(
-                chat("Hello, Timmy. We're about to start your interview.", session_id="interview")
+                chat(
+                    "Hello, Timmy. We're about to start your interview.",
+                    session_id="interview",
+                )
             )
         except Exception as exc:
             typer.echo(f"Warning: Initialization issue — {exc}", err=True)
@@ -410,11 +413,17 @@ def down():
 @app.command()
 def voice(
     whisper_model: str = typer.Option(
-        "base.en", "--whisper", "-w", help="Whisper model: tiny.en, base.en, small.en, medium.en"
+        "base.en",
+        "--whisper",
+        "-w",
+        help="Whisper model: tiny.en, base.en, small.en, medium.en",
     ),
     use_say: bool = typer.Option(False, "--say", help="Use macOS `say` instead of Piper TTS"),
     threshold: float = typer.Option(
-        0.015, "--threshold", "-t", help="Mic silence threshold (RMS). Lower = more sensitive."
+        0.015,
+        "--threshold",
+        "-t",
+        help="Mic silence threshold (RMS). Lower = more sensitive.",
     ),
     silence: float = typer.Option(1.5, "--silence", help="Seconds of silence to end recording"),
     backend: str | None = _BACKEND_OPTION,
@@ -457,7 +466,8 @@ def route(
 @app.command()
 def focus(
     topic: str | None = typer.Argument(
-        None, help='Topic to focus on (e.g. "three-phase loop"). Omit to show current focus.'
+        None,
+        help='Topic to focus on (e.g. "three-phase loop"). Omit to show current focus.',
     ),
     clear: bool = typer.Option(False, "--clear", "-c", help="Clear focus and return to broad mode"),
 ):
@@ -527,5 +537,156 @@ def healthcheck(
     raise typer.Exit(result.returncode)
 
 
+@app.command()
+def learn(
+    target: str | None = typer.Option(
+        None,
+        "--target",
+        "-t",
+        help="Module or file to optimise (e.g. 'src/timmy/agent.py')",
+    ),
+    metric: str = typer.Option(
+        "unit_pass_rate",
+        "--metric",
+        "-m",
+        help="Metric to track: unit_pass_rate | coverage | val_bpb | <custom>",
+    ),
+    budget: int = typer.Option(
+        5,
+        "--budget",
+        help="Time limit per experiment in minutes",
+    ),
+    max_experiments: int = typer.Option(
+        10,
+        "--max-experiments",
+        help="Cap on total experiments per run",
+    ),
+    dry_run: bool = typer.Option(
+        False,
+        "--dry-run",
+        help="Show hypothesis without executing experiments",
+    ),
+    program_file: str | None = typer.Option(
+        None,
+        "--program",
+        "-p",
+        help="Path to research direction file (default: program.md in cwd)",
+    ),
+    tox_env: str = typer.Option(
+        "unit",
+        "--tox-env",
+        help="Tox environment to run for each evaluation",
+    ),
+    model: str = typer.Option(
+        "qwen3:30b",
+        "--model",
+        help="Ollama model forwarded to Aider for code edits",
+    ),
+):
+    """Start an autonomous improvement loop (autoresearch).
+
+    Reads program.md for research direction, then iterates:
+    hypothesis → edit → tox → evaluate → commit/revert.
+
+    Experiments continue until --max-experiments is reached or the loop is
+    interrupted with Ctrl+C.  Use --dry-run to preview hypotheses without
+    making any changes.
+
+    Example:
+        timmy learn --target src/timmy/agent.py --metric unit_pass_rate
+    """
+    from pathlib import Path
+
+    from timmy.autoresearch import SystemExperiment
+
+    repo_root = Path.cwd()
+    program_path = Path(program_file) if program_file else repo_root / "program.md"
+
+    if program_path.exists():
+        program_content = program_path.read_text()
+        typer.echo(f"Research direction: {program_path}")
+    else:
+        program_content = ""
+        typer.echo(
+            f"Note: {program_path} not found — proceeding without research direction.",
+            err=True,
+        )
+
+    if target is None:
+        typer.echo(
+            "Error: --target is required. Specify the module or file to optimise.",
+            err=True,
+        )
+        raise typer.Exit(1)
+
+    experiment = SystemExperiment(
+        target=target,
+        metric=metric,
+        budget_minutes=budget,
+    )
+
+    typer.echo()
+    typer.echo(typer.style("Autoresearch", bold=True) + f" — {target}")
+    typer.echo(f"  metric={metric}  budget={budget}min  max={max_experiments}  tox={tox_env}")
+    if dry_run:
+        typer.echo("  (dry-run — no changes will be made)")
+    typer.echo()
+
+    def _progress_callback(iteration: int, max_iter: int, message: str) -> None:
+        """Print progress updates during experiment iterations."""
+        if iteration > 0:
+            prefix = typer.style(f"[{iteration}/{max_iter}]", bold=True)
+            typer.echo(f"{prefix} {message}")
+
+    try:
+        # Run the full experiment loop via the SystemExperiment class
+        result = experiment.run(
+            tox_env=tox_env,
+            model=model,
+            program_content=program_content,
+            max_iterations=max_experiments,
+            dry_run=dry_run,
+            create_branch=False,  # CLI mode: work on current branch
+        )
+
+        # Display results for each iteration
+        for i, record in enumerate(experiment.results, 1):
+            _progress_callback(i, max_experiments, record["hypothesis"])
+
+            if dry_run:
+                continue
+
+            # Edit phase result
+            typer.echo("  → editing …", nl=False)
+            if record.get("edit_failed"):
+                typer.echo(f" skipped ({record.get('edit_result', 'unknown')})")
+            else:
+                typer.echo(" done")
+
+            # Evaluate phase result
+            duration = record.get("duration_s", 0)
+            typer.echo(f"  → running tox … {duration}s")
+
+            # Assessment
+            assessment = record.get("assessment", "No assessment")
+            typer.echo(f"  → {assessment}")
+
+            # Outcome
+            if record.get("committed"):
+                typer.echo("  → committed")
+            elif record.get("reverted"):
+                typer.echo("  → reverted (no improvement)")
+
+            typer.echo()
+
+    except KeyboardInterrupt:
+        typer.echo("\nInterrupted.")
+        raise typer.Exit(0) from None
+
+    typer.echo(typer.style("Autoresearch complete.", bold=True))
+    if result.get("baseline") is not None:
+        typer.echo(f"Final {metric}: {result['baseline']:.4f}")
+
+
 def main():
     app()

src/timmy/dispatcher.py

@@ -37,7 +37,7 @@ from __future__ import annotations
 import asyncio
 import logging
 from dataclasses import dataclass, field
-from enum import Enum
+from enum import StrEnum
 from typing import Any
 
 from config import settings
@@ -48,7 +48,8 @@ logger = logging.getLogger(__name__)
 # Enumerations
 # ---------------------------------------------------------------------------
 
-class AgentType(str, Enum):
+
+class AgentType(StrEnum):
     """Known agents in the swarm."""
 
     CLAUDE_CODE = "claude_code"
@@ -57,7 +58,7 @@ class AgentType(str, Enum):
     TIMMY = "timmy"
 
 
-class TaskType(str, Enum):
+class TaskType(StrEnum):
     """Categories of engineering work."""
 
     # Claude Code strengths
@@ -83,7 +84,7 @@ class TaskType(str, Enum):
     ORCHESTRATION = "orchestration"
 
 
-class DispatchStatus(str, Enum):
+class DispatchStatus(StrEnum):
     """Lifecycle state of a dispatched task."""
 
     PENDING = "pending"
@@ -99,6 +100,7 @@ class DispatchStatus(str, Enum):
 # Agent registry
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class AgentSpec:
     """Capabilities and limits for a single agent."""
@@ -106,9 +108,9 @@ class AgentSpec:
     name: AgentType
     display_name: str
     strengths: frozenset[TaskType]
-    gitea_label: str | None        # label to apply when dispatching
+    gitea_label: str | None  # label to apply when dispatching
     max_concurrent: int = 1
-    interface: str = "gitea"       # "gitea" | "api" | "local"
+    interface: str = "gitea"  # "gitea" | "api" | "local"
     api_endpoint: str | None = None  # for interface="api"
 
 
@@ -197,6 +199,7 @@ _TASK_ROUTING: dict[TaskType, AgentType] = {
 # Dispatch result
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class DispatchResult:
     """Outcome of a dispatch call."""
@@ -220,6 +223,7 @@ class DispatchResult:
 # Routing logic
 # ---------------------------------------------------------------------------
 
+
 def select_agent(task_type: TaskType) -> AgentType:
     """Return the best agent for *task_type* based on the routing table.
 
@@ -248,11 +252,23 @@ def infer_task_type(title: str, description: str = "") -> TaskType:
     text = (title + " " + description).lower()
 
     _SIGNALS: list[tuple[TaskType, frozenset[str]]] = [
-        (TaskType.ARCHITECTURE, frozenset({"architect", "design", "adr", "system design", "schema"})),
-        (TaskType.REFACTORING, frozenset({"refactor", "clean up", "cleanup", "reorganise", "reorganize"})),
+        (
+            TaskType.ARCHITECTURE,
+            frozenset({"architect", "design", "adr", "system design", "schema"}),
+        ),
+        (
+            TaskType.REFACTORING,
+            frozenset({"refactor", "clean up", "cleanup", "reorganise", "reorganize"}),
+        ),
         (TaskType.CODE_REVIEW, frozenset({"review", "pr review", "pull request review", "audit"})),
-        (TaskType.COMPLEX_REASONING, frozenset({"complex", "hard problem", "debug", "investigate", "diagnose"})),
-        (TaskType.RESEARCH, frozenset({"research", "survey", "literature", "benchmark", "analyse", "analyze"})),
+        (
+            TaskType.COMPLEX_REASONING,
+            frozenset({"complex", "hard problem", "debug", "investigate", "diagnose"}),
+        ),
+        (
+            TaskType.RESEARCH,
+            frozenset({"research", "survey", "literature", "benchmark", "analyse", "analyze"}),
+        ),
         (TaskType.ANALYSIS, frozenset({"analysis", "profil", "trace", "metric", "performance"})),
         (TaskType.TRIAGE, frozenset({"triage", "classify", "prioritise", "prioritize"})),
         (TaskType.PLANNING, frozenset({"plan", "roadmap", "milestone", "epic", "spike"})),
@@ -273,6 +289,7 @@ def infer_task_type(title: str, description: str = "") -> TaskType:
 # Gitea helpers
 # ---------------------------------------------------------------------------
 
+
 async def _post_gitea_comment(
     client: Any,
     base_url: str,
@@ -405,6 +422,50 @@ async def _poll_issue_completion(
 # Core dispatch functions
 # ---------------------------------------------------------------------------
 
+
+def _format_assignment_comment(
+    display_name: str,
+    task_type: TaskType,
+    description: str,
+    acceptance_criteria: list[str],
+) -> str:
+    """Build the markdown comment body for a task assignment.
+
+    Args:
+        display_name: Human-readable agent name.
+        task_type: The inferred task type.
+        description: Task description.
+        acceptance_criteria: List of acceptance criteria strings.
+
+    Returns:
+        Formatted markdown string for the comment.
+    """
+    criteria_md = (
+        "\n".join(f"- {c}" for c in acceptance_criteria)
+        if acceptance_criteria
+        else "_None specified_"
+    )
+    return (
+        f"## Assigned to {display_name}\n\n"
+        f"**Task type:** `{task_type.value}`\n\n"
+        f"**Description:**\n{description}\n\n"
+        f"**Acceptance criteria:**\n{criteria_md}\n\n"
+        f"---\n*Dispatched by Timmy agent dispatcher.*"
+    )
+
+
+def _select_label(agent: AgentType) -> str | None:
+    """Return the Gitea label for an agent based on its spec.
+
+    Args:
+        agent: The target agent.
+
+    Returns:
+        Label name or None if the agent has no label.
+    """
+    return AGENT_REGISTRY[agent].gitea_label
+
+
 async def _dispatch_via_gitea(
     agent: AgentType,
     issue_number: int,
@@ -459,33 +520,27 @@ async def _dispatch_via_gitea(
 
     async with httpx.AsyncClient(timeout=15) as client:
         # 1. Apply agent label (if applicable)
-        if spec.gitea_label:
-            ok = await _apply_gitea_label(
-                client, base_url, repo, headers, issue_number, spec.gitea_label
-            )
+        label = _select_label(agent)
+        if label:
+            ok = await _apply_gitea_label(client, base_url, repo, headers, issue_number, label)
             if ok:
-                label_applied = spec.gitea_label
+                label_applied = label
                 logger.info(
                     "Applied label %r to issue #%s for %s",
-                    spec.gitea_label,
+                    label,
                     issue_number,
                     spec.display_name,
                 )
             else:
                 logger.warning(
                     "Could not apply label %r to issue #%s",
-                    spec.gitea_label,
+                    label,
                     issue_number,
                 )
 
         # 2. Post assignment comment
-        criteria_md = "\n".join(f"- {c}" for c in acceptance_criteria) if acceptance_criteria else "_None specified_"
-        comment_body = (
-            f"## Assigned to {spec.display_name}\n\n"
-            f"**Task type:** `{task_type.value}`\n\n"
-            f"**Description:**\n{description}\n\n"
-            f"**Acceptance criteria:**\n{criteria_md}\n\n"
-            f"---\n*Dispatched by Timmy agent dispatcher.*"
+        comment_body = _format_assignment_comment(
+            spec.display_name, task_type, description, acceptance_criteria
         )
         comment_id = await _post_gitea_comment(
             client, base_url, repo, headers, issue_number, comment_body
@@ -616,9 +671,7 @@ async def _dispatch_local(
         assumed to succeed at dispatch time).
     """
     task_type = infer_task_type(title, description)
-    logger.info(
-        "Timmy handling task locally: %r (issue #%s)", title[:60], issue_number
-    )
+    logger.info("Timmy handling task locally: %r (issue #%s)", title[:60], issue_number)
     return DispatchResult(
         task_type=task_type,
         agent=AgentType.TIMMY,
@@ -632,6 +685,81 @@ async def _dispatch_local(
 # Public entry point
 # ---------------------------------------------------------------------------
 
+
+def _validate_task(
+    title: str,
+    task_type: TaskType | None,
+    agent: AgentType | None,
+    issue_number: int | None,
+) -> DispatchResult | None:
+    """Validate task preconditions.
+
+    Args:
+        title: Task title to validate.
+        task_type: Optional task type for result construction.
+        agent: Optional agent for result construction.
+        issue_number: Optional issue number for result construction.
+
+    Returns:
+        A failed DispatchResult if validation fails, None otherwise.
+    """
+    if not title.strip():
+        return DispatchResult(
+            task_type=task_type or TaskType.ROUTINE_CODING,
+            agent=agent or AgentType.TIMMY,
+            issue_number=issue_number,
+            status=DispatchStatus.FAILED,
+            error="`title` is required.",
+        )
+    return None
+
+
+def _select_dispatch_strategy(agent: AgentType, issue_number: int | None) -> str:
+    """Select the dispatch strategy based on agent interface and context.
+
+    Args:
+        agent: The target agent.
+        issue_number: Optional Gitea issue number.
+
+    Returns:
+        Strategy name: "gitea", "api", or "local".
+    """
+    spec = AGENT_REGISTRY[agent]
+    if spec.interface == "gitea" and issue_number is not None:
+        return "gitea"
+    if spec.interface == "api":
+        return "api"
+    return "local"
+
+
+def _log_dispatch_result(
+    title: str,
+    result: DispatchResult,
+    attempt: int,
+    max_retries: int,
+) -> None:
+    """Log the outcome of a dispatch attempt.
+
+    Args:
+        title: Task title for logging context.
+        result: The dispatch result.
+        attempt: Current attempt number (0-indexed).
+        max_retries: Maximum retry attempts allowed.
+    """
+    if result.success:
+        return
+
+    if attempt > 0:
+        logger.info("Retry %d/%d for task %r", attempt, max_retries, title[:60])
+
+    logger.warning(
+        "Dispatch attempt %d failed for task %r: %s",
+        attempt + 1,
+        title[:60],
+        result.error,
+    )
+
+
 async def dispatch_task(
     title: str,
     description: str = "",
@@ -672,17 +800,13 @@ async def dispatch_task(
         if result.success:
             print(f"Assigned to {result.agent.value}")
     """
+    # 1. Validate
+    validation_error = _validate_task(title, task_type, agent, issue_number)
+    if validation_error:
+        return validation_error
+
+    # 2. Resolve task type and agent
     criteria = acceptance_criteria or []
-
-    if not title.strip():
-        return DispatchResult(
-            task_type=task_type or TaskType.ROUTINE_CODING,
-            agent=agent or AgentType.TIMMY,
-            issue_number=issue_number,
-            status=DispatchStatus.FAILED,
-            error="`title` is required.",
-        )
-
     resolved_type = task_type or infer_task_type(title, description)
     resolved_agent = agent or select_agent(resolved_type)
 
@@ -694,18 +818,16 @@ async def dispatch_task(
         issue_number,
     )
 
-    spec = AGENT_REGISTRY[resolved_agent]
-
+    # 3. Select strategy and dispatch with retries
+    strategy = _select_dispatch_strategy(resolved_agent, issue_number)
     last_result: DispatchResult | None = None
-    for attempt in range(max_retries + 1):
-        if attempt > 0:
-            logger.info("Retry %d/%d for task %r", attempt, max_retries, title[:60])
 
-        if spec.interface == "gitea" and issue_number is not None:
+    for attempt in range(max_retries + 1):
+        if strategy == "gitea":
             result = await _dispatch_via_gitea(
                 resolved_agent, issue_number, title, description, criteria
             )
-        elif spec.interface == "api":
+        elif strategy == "api":
             result = await _dispatch_via_api(
                 resolved_agent, title, description, criteria, issue_number, api_endpoint
             )
@@ -718,14 +840,9 @@ async def dispatch_task(
         if result.success:
             return result
 
-        logger.warning(
-            "Dispatch attempt %d failed for task %r: %s",
-            attempt + 1,
-            title[:60],
-            result.error,
-        )
+        _log_dispatch_result(title, result, attempt, max_retries)
 
-    # All attempts exhausted — escalate
+    # 4. All attempts exhausted — escalate
     assert last_result is not None
     last_result.status = DispatchStatus.ESCALATED
     logger.error(
@@ -769,9 +886,7 @@ async def _log_escalation(
             f"---\n*Timmy agent dispatcher.*"
         )
         async with httpx.AsyncClient(timeout=10) as client:
-            await _post_gitea_comment(
-                client, base_url, repo, headers, issue_number, body
-            )
+            await _post_gitea_comment(client, base_url, repo, headers, issue_number, body)
     except Exception as exc:
         logger.warning("Failed to post escalation comment: %s", exc)
 
@@ -780,6 +895,7 @@ async def _log_escalation(
 # Monitoring helper
 # ---------------------------------------------------------------------------
 
+
 async def wait_for_completion(
     issue_number: int,
     poll_interval: int = 60,

src/timmy/dreaming.py

@@ -1,434 +0,0 @@
-"""Dreaming Mode — idle-time session replay and counterfactual simulation.
-
-When the dashboard has been idle for a configurable period, this engine
-selects a past chat session, identifies key agent response points, and
-asks the LLM to simulate alternative approaches.  Insights are stored as
-proposed rules that can feed the auto-crystallizer or memory system.
-
-Usage::
-
-    from timmy.dreaming import dreaming_engine
-
-    # Run one dream cycle (called by the background scheduler)
-    await dreaming_engine.dream_once()
-
-    # Query recent dreams
-    dreams = dreaming_engine.get_recent_dreams(limit=10)
-
-    # Get current status dict for API/dashboard
-    status = dreaming_engine.get_status()
-"""
-
-import logging
-import re
-import sqlite3
-import uuid
-from collections.abc import Generator
-from contextlib import closing, contextmanager
-from dataclasses import dataclass
-from datetime import UTC, datetime, timedelta
-from pathlib import Path
-from typing import Any
-
-from config import settings
-
-logger = logging.getLogger(__name__)
-
-_DEFAULT_DB = Path("data/dreams.db")
-
-# Strip <think> tags from reasoning model output
-_THINK_TAG_RE = re.compile(r"<think>.*?</think>\s*", re.DOTALL)
-
-# Minimum messages in a session to be worth replaying
-_MIN_SESSION_MESSAGES = 3
-
-# Gap in seconds between messages that signals a new session
-_SESSION_GAP_SECONDS = 1800  # 30 minutes
-
-
-@dataclass
-class DreamRecord:
-    """A single completed dream cycle."""
-
-    id: str
-    session_excerpt: str      # Short excerpt from the replayed session
-    decision_point: str       # The agent message that was re-simulated
-    simulation: str           # The alternative response generated
-    proposed_rule: str        # Rule extracted from the simulation
-    created_at: str
-
-
-@contextmanager
-def _get_conn(db_path: Path = _DEFAULT_DB) -> Generator[sqlite3.Connection, None, None]:
-    db_path.parent.mkdir(parents=True, exist_ok=True)
-    with closing(sqlite3.connect(str(db_path))) as conn:
-        conn.row_factory = sqlite3.Row
-        conn.execute("""
-            CREATE TABLE IF NOT EXISTS dreams (
-                id            TEXT PRIMARY KEY,
-                session_excerpt TEXT NOT NULL,
-                decision_point  TEXT NOT NULL,
-                simulation      TEXT NOT NULL,
-                proposed_rule   TEXT NOT NULL DEFAULT '',
-                created_at      TEXT NOT NULL
-            )
-        """)
-        conn.execute("CREATE INDEX IF NOT EXISTS idx_dreams_time ON dreams(created_at)")
-        conn.commit()
-        yield conn
-
-
-def _row_to_dream(row: sqlite3.Row) -> DreamRecord:
-    return DreamRecord(
-        id=row["id"],
-        session_excerpt=row["session_excerpt"],
-        decision_point=row["decision_point"],
-        simulation=row["simulation"],
-        proposed_rule=row["proposed_rule"],
-        created_at=row["created_at"],
-    )
-
-
-class DreamingEngine:
-    """Idle-time dreaming engine — replays sessions and simulates alternatives."""
-
-    def __init__(self, db_path: Path = _DEFAULT_DB) -> None:
-        self._db_path = db_path
-        self._last_activity_time: datetime = datetime.now(UTC)
-        self._is_dreaming: bool = False
-        self._current_dream_summary: str = ""
-        self._dreaming_agent = None  # Lazy-initialised
-
-    # ── Public API ────────────────────────────────────────────────────────
-
-    def record_activity(self) -> None:
-        """Reset the idle timer — call this on every user/agent interaction."""
-        self._last_activity_time = datetime.now(UTC)
-
-    def is_idle(self) -> bool:
-        """Return True if the system has been idle long enough to start dreaming."""
-        threshold = settings.dreaming_idle_threshold_minutes
-        if threshold <= 0:
-            return False
-        return datetime.now(UTC) - self._last_activity_time > timedelta(minutes=threshold)
-
-    def get_status(self) -> dict[str, Any]:
-        """Return a status dict suitable for API/dashboard consumption."""
-        return {
-            "enabled": settings.dreaming_enabled,
-            "dreaming": self._is_dreaming,
-            "idle": self.is_idle(),
-            "current_summary": self._current_dream_summary,
-            "idle_minutes": int(
-                (datetime.now(UTC) - self._last_activity_time).total_seconds() / 60
-            ),
-            "idle_threshold_minutes": settings.dreaming_idle_threshold_minutes,
-            "dream_count": self.count_dreams(),
-        }
-
-    async def dream_once(self) -> DreamRecord | None:
-        """Execute one dream cycle.
-
-        Returns the stored DreamRecord, or None if the cycle was skipped
-        (not idle, dreaming disabled, no suitable session, or LLM error).
-        """
-        if not settings.dreaming_enabled:
-            return None
-
-        if not self.is_idle():
-            logger.debug(
-                "Dreaming skipped — system active (idle for %d min, threshold %d min)",
-                int((datetime.now(UTC) - self._last_activity_time).total_seconds() / 60),
-                settings.dreaming_idle_threshold_minutes,
-            )
-            return None
-
-        if self._is_dreaming:
-            logger.debug("Dreaming skipped — cycle already in progress")
-            return None
-
-        self._is_dreaming = True
-        self._current_dream_summary = "Selecting a past session…"
-        await self._broadcast_status()
-
-        try:
-            return await self._run_dream_cycle()
-        except Exception as exc:
-            logger.warning("Dream cycle failed: %s", exc)
-            return None
-        finally:
-            self._is_dreaming = False
-            self._current_dream_summary = ""
-            await self._broadcast_status()
-
-    def get_recent_dreams(self, limit: int = 20) -> list[DreamRecord]:
-        """Retrieve the most recent dream records."""
-        with _get_conn(self._db_path) as conn:
-            rows = conn.execute(
-                "SELECT * FROM dreams ORDER BY created_at DESC LIMIT ?",
-                (limit,),
-            ).fetchall()
-        return [_row_to_dream(r) for r in rows]
-
-    def count_dreams(self) -> int:
-        """Return total number of stored dream records."""
-        with _get_conn(self._db_path) as conn:
-            row = conn.execute("SELECT COUNT(*) AS c FROM dreams").fetchone()
-            return row["c"] if row else 0
-
-    # ── Private helpers ───────────────────────────────────────────────────
-
-    async def _run_dream_cycle(self) -> DreamRecord | None:
-        """Core dream logic: select → simulate → store."""
-        # 1. Select a past session from the chat log
-        session = await self._select_session()
-        if not session:
-            logger.debug("No suitable chat session found for dreaming")
-            self._current_dream_summary = "No past sessions to replay"
-            return None
-
-        decision_point, session_excerpt = session
-
-        self._current_dream_summary = f"Simulating alternative for: {decision_point[:60]}…"
-        await self._broadcast_status()
-
-        # 2. Simulate an alternative response
-        simulation = await self._simulate_alternative(decision_point, session_excerpt)
-        if not simulation:
-            logger.debug("Dream simulation produced no output")
-            return None
-
-        # 3. Extract a proposed rule
-        proposed_rule = await self._extract_rule(decision_point, simulation)
-
-        # 4. Store and broadcast
-        dream = self._store_dream(
-            session_excerpt=session_excerpt,
-            decision_point=decision_point,
-            simulation=simulation,
-            proposed_rule=proposed_rule,
-        )
-
-        self._current_dream_summary = f"Dream complete: {proposed_rule[:80]}" if proposed_rule else "Dream complete"
-
-        logger.info(
-            "Dream [%s]: replayed session, proposed rule: %s",
-            dream.id[:8],
-            proposed_rule[:80] if proposed_rule else "(none)",
-        )
-
-        await self._broadcast_status()
-        await self._broadcast_dream(dream)
-        return dream
-
-    async def _select_session(self) -> tuple[str, str] | None:
-        """Select a past chat session and return (decision_point, session_excerpt).
-
-        Uses the SQLite chat store.  Groups messages into sessions by time
-        gap.  Picks a random session with enough messages, then selects one
-        agent response as the decision point.
-        """
-        try:
-            from infrastructure.chat_store import DB_PATH
-
-            if not DB_PATH.exists():
-                return None
-
-            import asyncio
-            rows = await asyncio.to_thread(self._load_chat_rows)
-            if not rows:
-                return None
-
-            sessions = self._group_into_sessions(rows)
-            if not sessions:
-                return None
-
-            # Filter sessions with enough messages
-            valid = [s for s in sessions if len(s) >= _MIN_SESSION_MESSAGES]
-            if not valid:
-                return None
-
-            import random
-            session = random.choice(valid)  # noqa: S311 (not cryptographic)
-
-            # Build a short text excerpt (last N messages)
-            excerpt_msgs = session[-6:]
-            excerpt = "\n".join(
-                f"{m['role'].upper()}: {m['content'][:200]}" for m in excerpt_msgs
-            )
-
-            # Find agent responses as candidate decision points
-            agent_msgs = [m for m in session if m["role"] in ("agent", "assistant")]
-            if not agent_msgs:
-                return None
-
-            decision = random.choice(agent_msgs)  # noqa: S311
-            return decision["content"], excerpt
-
-        except Exception as exc:
-            logger.warning("Session selection failed: %s", exc)
-            return None
-
-    def _load_chat_rows(self) -> list[dict]:
-        """Synchronously load chat messages from SQLite."""
-        from infrastructure.chat_store import DB_PATH
-
-        with closing(sqlite3.connect(str(DB_PATH))) as conn:
-            conn.row_factory = sqlite3.Row
-            rows = conn.execute(
-                "SELECT role, content, timestamp FROM chat_messages "
-                "ORDER BY timestamp ASC"
-            ).fetchall()
-        return [dict(r) for r in rows]
-
-    def _group_into_sessions(self, rows: list[dict]) -> list[list[dict]]:
-        """Group chat rows into sessions based on time gaps."""
-        if not rows:
-            return []
-
-        sessions: list[list[dict]] = []
-        current: list[dict] = [rows[0]]
-
-        for prev, curr in zip(rows, rows[1:], strict=False):
-            try:
-                t_prev = datetime.fromisoformat(prev["timestamp"].replace("Z", "+00:00"))
-                t_curr = datetime.fromisoformat(curr["timestamp"].replace("Z", "+00:00"))
-                gap = (t_curr - t_prev).total_seconds()
-            except Exception:
-                gap = 0
-
-            if gap > _SESSION_GAP_SECONDS:
-                sessions.append(current)
-                current = [curr]
-            else:
-                current.append(curr)
-
-        sessions.append(current)
-        return sessions
-
-    async def _simulate_alternative(
-        self, decision_point: str, session_excerpt: str
-    ) -> str:
-        """Ask the LLM to simulate an alternative response."""
-        prompt = (
-            "You are Timmy, a sovereign AI agent in a dreaming state.\n"
-            "You are replaying a past conversation and exploring what you could "
-            "have done differently at a key decision point.\n\n"
-            "PAST SESSION EXCERPT:\n"
-            f"{session_excerpt}\n\n"
-            "KEY DECISION POINT (your past response):\n"
-            f"{decision_point[:500]}\n\n"
-            "TASK: In 2-3 sentences, describe ONE concrete alternative approach "
-            "you could have taken at this decision point that would have been "
-            "more helpful, more accurate, or more efficient.\n"
-            "Be specific — reference the actual content of the conversation.\n"
-            "Do NOT include meta-commentary about dreaming or this exercise.\n\n"
-            "Alternative approach:"
-        )
-
-        raw = await self._call_agent(prompt)
-        return _THINK_TAG_RE.sub("", raw).strip() if raw else ""
-
-    async def _extract_rule(self, decision_point: str, simulation: str) -> str:
-        """Extract a proposed behaviour rule from the simulation."""
-        prompt = (
-            "Given this pair of agent responses:\n\n"
-            f"ORIGINAL: {decision_point[:300]}\n\n"
-            f"IMPROVED ALTERNATIVE: {simulation[:400]}\n\n"
-            "Extract ONE concise rule (max 20 words) that captures what to do "
-            "differently next time.  Format: 'When X, do Y instead of Z.'\n"
-            "Rule:"
-        )
-
-        raw = await self._call_agent(prompt)
-        rule = _THINK_TAG_RE.sub("", raw).strip() if raw else ""
-        # Keep only the first sentence/line
-        rule = rule.split("\n")[0].strip().rstrip(".")
-        return rule[:200]  # Safety cap
-
-    async def _call_agent(self, prompt: str) -> str:
-        """Call the Timmy agent for a dreaming prompt (skip MCP, 60 s timeout)."""
-        import asyncio
-
-        if self._dreaming_agent is None:
-            from timmy.agent import create_timmy
-
-            self._dreaming_agent = create_timmy(skip_mcp=True)
-
-        try:
-            async with asyncio.timeout(settings.dreaming_timeout_seconds):
-                run = await self._dreaming_agent.arun(prompt, stream=False)
-        except TimeoutError:
-            logger.warning("Dreaming LLM call timed out after %ds", settings.dreaming_timeout_seconds)
-            return ""
-        except Exception as exc:
-            logger.warning("Dreaming LLM call failed: %s", exc)
-            return ""
-
-        raw = run.content if hasattr(run, "content") else str(run)
-        return raw or ""
-
-    def _store_dream(
-        self,
-        *,
-        session_excerpt: str,
-        decision_point: str,
-        simulation: str,
-        proposed_rule: str,
-    ) -> DreamRecord:
-        dream = DreamRecord(
-            id=str(uuid.uuid4()),
-            session_excerpt=session_excerpt,
-            decision_point=decision_point,
-            simulation=simulation,
-            proposed_rule=proposed_rule,
-            created_at=datetime.now(UTC).isoformat(),
-        )
-        with _get_conn(self._db_path) as conn:
-            conn.execute(
-                """
-                INSERT INTO dreams
-                    (id, session_excerpt, decision_point, simulation, proposed_rule, created_at)
-                VALUES (?, ?, ?, ?, ?, ?)
-                """,
-                (
-                    dream.id,
-                    dream.session_excerpt,
-                    dream.decision_point,
-                    dream.simulation,
-                    dream.proposed_rule,
-                    dream.created_at,
-                ),
-            )
-            conn.commit()
-        return dream
-
-    async def _broadcast_status(self) -> None:
-        """Push current dreaming status via WebSocket."""
-        try:
-            from infrastructure.ws_manager.handler import ws_manager
-
-            await ws_manager.broadcast("dreaming_state", self.get_status())
-        except Exception as exc:
-            logger.debug("Dreaming status broadcast failed: %s", exc)
-
-    async def _broadcast_dream(self, dream: DreamRecord) -> None:
-        """Push a completed dream record via WebSocket."""
-        try:
-            from infrastructure.ws_manager.handler import ws_manager
-
-            await ws_manager.broadcast(
-                "dreaming_complete",
-                {
-                    "id": dream.id,
-                    "proposed_rule": dream.proposed_rule,
-                    "simulation": dream.simulation[:200],
-                    "created_at": dream.created_at,
-                },
-            )
-        except Exception as exc:
-            logger.debug("Dreaming complete broadcast failed: %s", exc)
-
-
-# Module-level singleton
-dreaming_engine = DreamingEngine()

src/timmy/mcp_bridge.py

@@ -142,18 +142,8 @@ def _build_shell_tool() -> MCPToolDef | None:
         return None
 
 
-def _build_gitea_tools() -> list[MCPToolDef]:
-    """Build Gitea MCP tool definitions for direct Ollama bridge use.
-
-    These tools call the Gitea REST API directly via httpx rather than
-    spawning an MCP server subprocess, keeping the bridge lightweight.
-    """
-    if not settings.gitea_enabled or not settings.gitea_token:
-        return []
-
-    base_url = settings.gitea_url
-    token = settings.gitea_token
-    owner, repo = settings.gitea_repo.split("/", 1)
+def _build_list_issues_tool(base_url: str, token: str, owner: str, repo: str) -> MCPToolDef:
+    """Build the list_issues tool for a specific Gitea repo."""
 
     async def _list_issues(**kwargs: Any) -> str:
         state = kwargs.get("state", "open")
@@ -178,6 +168,30 @@ def _build_gitea_tools() -> list[MCPToolDef]:
         except Exception as exc:
             return f"Error listing issues: {exc}"
 
+    return MCPToolDef(
+        name="list_issues",
+        description="List issues in the Gitea repository. Returns issue numbers and titles.",
+        parameters={
+            "type": "object",
+            "properties": {
+                "state": {
+                    "type": "string",
+                    "description": "Filter by state: open, closed, or all (default: open)",
+                },
+                "limit": {
+                    "type": "integer",
+                    "description": "Maximum number of issues to return (default: 10)",
+                },
+            },
+            "required": [],
+        },
+        handler=_list_issues,
+    )
+
+
+def _build_create_issue_tool(base_url: str, token: str, owner: str, repo: str) -> MCPToolDef:
+    """Build the create_issue tool for a specific Gitea repo."""
+
     async def _create_issue(**kwargs: Any) -> str:
         title = kwargs.get("title", "")
         body = kwargs.get("body", "")
@@ -199,6 +213,30 @@ def _build_gitea_tools() -> list[MCPToolDef]:
         except Exception as exc:
             return f"Error creating issue: {exc}"
 
+    return MCPToolDef(
+        name="create_issue",
+        description="Create a new issue in the Gitea repository.",
+        parameters={
+            "type": "object",
+            "properties": {
+                "title": {
+                    "type": "string",
+                    "description": "Issue title (required)",
+                },
+                "body": {
+                    "type": "string",
+                    "description": "Issue body in markdown (optional)",
+                },
+            },
+            "required": ["title"],
+        },
+        handler=_create_issue,
+    )
+
+
+def _build_read_issue_tool(base_url: str, token: str, owner: str, repo: str) -> MCPToolDef:
+    """Build the read_issue tool for a specific Gitea repo."""
+
     async def _read_issue(**kwargs: Any) -> str:
         number = kwargs.get("number")
         if not number:
@@ -224,60 +262,40 @@ def _build_gitea_tools() -> list[MCPToolDef]:
         except Exception as exc:
             return f"Error reading issue: {exc}"
 
+    return MCPToolDef(
+        name="read_issue",
+        description="Read details of a specific issue by number.",
+        parameters={
+            "type": "object",
+            "properties": {
+                "number": {
+                    "type": "integer",
+                    "description": "Issue number to read",
+                },
+            },
+            "required": ["number"],
+        },
+        handler=_read_issue,
+    )
+
+
+def _build_gitea_tools() -> list[MCPToolDef]:
+    """Build Gitea MCP tool definitions for direct Ollama bridge use.
+
+    These tools call the Gitea REST API directly via httpx rather than
+    spawning an MCP server subprocess, keeping the bridge lightweight.
+    """
+    if not settings.gitea_enabled or not settings.gitea_token:
+        return []
+
+    base_url = settings.gitea_url
+    token = settings.gitea_token
+    owner, repo = settings.gitea_repo.split("/", 1)
+
     return [
-        MCPToolDef(
-            name="list_issues",
-            description="List issues in the Gitea repository. Returns issue numbers and titles.",
-            parameters={
-                "type": "object",
-                "properties": {
-                    "state": {
-                        "type": "string",
-                        "description": "Filter by state: open, closed, or all (default: open)",
-                    },
-                    "limit": {
-                        "type": "integer",
-                        "description": "Maximum number of issues to return (default: 10)",
-                    },
-                },
-                "required": [],
-            },
-            handler=_list_issues,
-        ),
-        MCPToolDef(
-            name="create_issue",
-            description="Create a new issue in the Gitea repository.",
-            parameters={
-                "type": "object",
-                "properties": {
-                    "title": {
-                        "type": "string",
-                        "description": "Issue title (required)",
-                    },
-                    "body": {
-                        "type": "string",
-                        "description": "Issue body in markdown (optional)",
-                    },
-                },
-                "required": ["title"],
-            },
-            handler=_create_issue,
-        ),
-        MCPToolDef(
-            name="read_issue",
-            description="Read details of a specific issue by number.",
-            parameters={
-                "type": "object",
-                "properties": {
-                    "number": {
-                        "type": "integer",
-                        "description": "Issue number to read",
-                    },
-                },
-                "required": ["number"],
-            },
-            handler=_read_issue,
-        ),
+        _build_list_issues_tool(base_url, token, owner, repo),
+        _build_create_issue_tool(base_url, token, owner, repo),
+        _build_read_issue_tool(base_url, token, owner, repo),
     ]
 
 
@@ -399,6 +417,72 @@ class MCPBridge:
             logger.warning("Tool '%s' execution failed: %s", name, exc)
             return f"Error executing {name}: {exc}"
 
+    @staticmethod
+    def _build_initial_messages(prompt: str, system_prompt: str | None) -> list[dict]:
+        """Build the initial message list for a run."""
+        messages: list[dict] = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.append({"role": "user", "content": prompt})
+        return messages
+
+    async def _process_round_tool_calls(
+        self,
+        messages: list[dict],
+        model_tool_calls: list[dict],
+        rounds: int,
+        tool_calls_made: list[dict],
+    ) -> None:
+        """Execute all tool calls in one round, appending results to messages."""
+        for tc in model_tool_calls:
+            func = tc.get("function", {})
+            tool_name = func.get("name", "unknown")
+            tool_args = func.get("arguments", {})
+            logger.info(
+                "Bridge tool call [round %d]: %s(%s)",
+                rounds,
+                tool_name,
+                tool_args,
+            )
+            result = await self._execute_tool_call(tc)
+            tool_calls_made.append(
+                {
+                    "round": rounds,
+                    "tool": tool_name,
+                    "arguments": tool_args,
+                    "result": result[:500],  # Truncate for logging
+                }
+            )
+            messages.append({"role": "tool", "content": result})
+
+    async def _run_tool_loop(
+        self, messages: list[dict], tools: list[dict]
+    ) -> tuple[str, list[dict], int, str]:
+        """Run the tool-call loop until final response or max rounds reached.
+
+        Returns:
+            Tuple of (content, tool_calls_made, rounds, error).
+        """
+        tool_calls_made: list[dict] = []
+        rounds = 0
+
+        for round_num in range(self.max_rounds):
+            rounds = round_num + 1
+            response = await self._chat(messages, tools)
+            msg = response.get("message", {})
+            model_tool_calls = msg.get("tool_calls", [])
+
+            if not model_tool_calls:
+                return msg.get("content", ""), tool_calls_made, rounds, ""
+
+            messages.append(msg)
+            await self._process_round_tool_calls(
+                messages, model_tool_calls, rounds, tool_calls_made
+            )
+
+        error = f"Exceeded maximum of {self.max_rounds} tool-call rounds"
+        return "(max tool-call rounds reached)", tool_calls_made, rounds, error
+
     async def run(
         self,
         prompt: str,
@@ -419,115 +503,35 @@ class MCPBridge:
             BridgeResult with the final response and tool call history.
         """
         start = time.time()
-        messages: list[dict] = []
-
-        if system_prompt:
-            messages.append({"role": "system", "content": system_prompt})
-
-        messages.append({"role": "user", "content": prompt})
-
+        messages = self._build_initial_messages(prompt, system_prompt)
         tools = self._build_ollama_tools()
         tool_calls_made: list[dict] = []
         rounds = 0
+        error_msg = ""
 
         try:
-            for round_num in range(self.max_rounds):
-                rounds = round_num + 1
-                response = await self._chat(messages, tools)
-                msg = response.get("message", {})
-
-                # Check if model made tool calls
-                model_tool_calls = msg.get("tool_calls", [])
-                if not model_tool_calls:
-                    # Final text response — done.
-                    content = msg.get("content", "")
-                    latency = (time.time() - start) * 1000
-                    return BridgeResult(
-                        content=content,
-                        tool_calls_made=tool_calls_made,
-                        rounds=rounds,
-                        latency_ms=latency,
-                        model=self.model,
-                    )
-
-                # Append the assistant message (with tool_calls) to history
-                messages.append(msg)
-
-                # Execute each tool call and add results
-                for tc in model_tool_calls:
-                    func = tc.get("function", {})
-                    tool_name = func.get("name", "unknown")
-                    tool_args = func.get("arguments", {})
-
-                    logger.info(
-                        "Bridge tool call [round %d]: %s(%s)",
-                        rounds,
-                        tool_name,
-                        tool_args,
-                    )
-
-                    result = await self._execute_tool_call(tc)
-                    tool_calls_made.append(
-                        {
-                            "round": rounds,
-                            "tool": tool_name,
-                            "arguments": tool_args,
-                            "result": result[:500],  # Truncate for logging
-                        }
-                    )
-
-                    # Add tool result to message history
-                    messages.append(
-                        {
-                            "role": "tool",
-                            "content": result,
-                        }
-                    )
-
-            # Hit max rounds
-            latency = (time.time() - start) * 1000
-            return BridgeResult(
-                content="(max tool-call rounds reached)",
-                tool_calls_made=tool_calls_made,
-                rounds=rounds,
-                latency_ms=latency,
-                model=self.model,
-                error=f"Exceeded maximum of {self.max_rounds} tool-call rounds",
-            )
-
+            content, tool_calls_made, rounds, error_msg = await self._run_tool_loop(messages, tools)
         except httpx.ConnectError as exc:
-            latency = (time.time() - start) * 1000
             logger.warning("Ollama connection failed: %s", exc)
-            return BridgeResult(
-                content="",
-                tool_calls_made=tool_calls_made,
-                rounds=rounds,
-                latency_ms=latency,
-                model=self.model,
-                error=f"Ollama connection failed: {exc}",
-            )
+            error_msg = f"Ollama connection failed: {exc}"
+            content = ""
         except httpx.HTTPStatusError as exc:
-            latency = (time.time() - start) * 1000
             logger.warning("Ollama HTTP error: %s", exc)
-            return BridgeResult(
-                content="",
-                tool_calls_made=tool_calls_made,
-                rounds=rounds,
-                latency_ms=latency,
-                model=self.model,
-                error=f"Ollama HTTP error: {exc.response.status_code}",
-            )
+            error_msg = f"Ollama HTTP error: {exc.response.status_code}"
+            content = ""
         except Exception as exc:
-            latency = (time.time() - start) * 1000
             logger.error("MCPBridge run failed: %s", exc)
-            return BridgeResult(
-                content="",
-                tool_calls_made=tool_calls_made,
-                rounds=rounds,
-                latency_ms=latency,
-                model=self.model,
-                error=str(exc),
-            )
+            error_msg = str(exc)
+            content = ""
+
+        return BridgeResult(
+            content=content,
+            tool_calls_made=tool_calls_made,
+            rounds=rounds,
+            latency_ms=(time.time() - start) * 1000,
+            model=self.model,
+            error=error_msg,
+        )
 
     def status(self) -> dict:
         """Return bridge status for the dashboard."""