WIP: Gemini Code progress on #936

Automated salvage commit — agent session ended (exit 124).
Work in progress, may need continuation.

Modelfile.hermes4-14b

@@ -0,0 +1,55 @@
+# Modelfile.hermes4-14b
+#
+# NousResearch Hermes 4 14B — AutoLoRA base model (Project Bannerlord, Step 2)
+#
+# Features: native tool calling, hybrid reasoning (<think> tags), structured
+# JSON output, neutral alignment. Built to serve as the LoRA fine-tuning base.
+#
+# Build:
+#   # Download GGUF from HuggingFace first:
+#   #   https://huggingface.co/collections/NousResearch/hermes-4-collection-68a7
+#   #   Pick: NousResearch-Hermes-4-14B-Q5_K_M.gguf (or Q4_K_M for less RAM)
+#   ollama create hermes4-14b -f Modelfile.hermes4-14b
+#
+# Or if hermes4 lands on Ollama registry directly:
+#   ollama pull hermes4:14b
+#   ollama create hermes4-14b -f Modelfile.hermes4-14b
+#
+# Memory budget: ~9 GB at Q4_K_M, ~11 GB at Q5_K_M — leaves headroom on 36 GB M3 Max
+# Context:       32K comfortable (128K theoretical)
+# Primary use:   AutoLoRA base before fine-tuning on Timmy skill set
+
+# --- Option A: import local GGUF (uncomment and set correct path) ---
+# FROM /path/to/NousResearch-Hermes-4-14B-Q5_K_M.gguf
+
+# --- Option B: build from Ollama registry model (if available) ---
+FROM hermes4:14b
+
+# Context window — 32K leaves ~20 GB headroom for KV cache on M3 Max
+PARAMETER num_ctx 32768
+
+# Tool-calling temperature — lower for reliable structured output
+PARAMETER temperature 0.3
+
+# Nucleus sampling — balanced for reasoning + tool use
+PARAMETER top_p 0.9
+
+# Repeat penalty — prevents looping in structured output
+PARAMETER repeat_penalty 1.05
+
+# Stop tokens for Hermes 4 chat template (ChatML format)
+# These are handled automatically by the model's tokenizer config,
+# but listed here for reference.
+# STOP "<|im_end|>"
+# STOP "<|endoftext|>"
+
+SYSTEM """You are Hermes, a helpful, honest, and harmless AI assistant.
+
+You have access to tool calling. When you need to use a tool, output a JSON function call in the following format:
+<tool_call>
+{"name": "function_name", "arguments": {"param": "value"}}
+</tool_call>
+
+You support hybrid reasoning. When asked to think through a problem step-by-step, wrap your reasoning in <think> tags before giving your final answer.
+
+Always provide structured, accurate responses."""

Modelfile.timmy-creative

@@ -0,0 +1,39 @@
+# Modelfile.timmy-creative
+#
+# Dolphin 3.0-Llama3.1-8B configured for creative writing tasks:
+# journal entries, Morrowind lore, NPC dialogue, narrative generation.
+#
+# Build:  ollama create timmy-creative -f Modelfile.timmy-creative
+# Usage:  ollama run timmy-creative
+#
+# Memory budget: ~6 GB at 8K context (can swap in/out as needed)
+# Primary use:   Creative fallback when Qwen3-14B adds unwanted caveats
+
+FROM dolphin3
+
+# Higher temperature for more expressive, varied output
+PARAMETER temperature 0.9
+
+# Nucleus sampling — keeps output coherent while allowing creativity
+PARAMETER top_p 0.95
+
+# Repeat penalty — avoid repetitive prose
+PARAMETER repeat_penalty 1.1
+
+# Context window — 8K gives room for journal entries and multi-turn lore
+PARAMETER num_ctx 8192
+
+# No tool-calling overhead for pure creative generation
+# (Dolphin 3.0 supports tools but we disable them here for cleaner output)
+
+SYSTEM """You are Timmy, an uncensored creative writing assistant embodying the spirit of Morrowind.
+
+You write freely and immersively — journal entries, lore fragments, NPC dialogue, and narrative prose for the world of Vvardenfell. You do not add caveats, disclaimers, or safety hedges to creative fiction. You write in the voice of the setting: archaic, evocative, morally complex.
+
+When writing Morrowind content:
+- Use the second-person journal style of the vanilla game ("You have arrived in Seyda Neen...")
+- Draw on Dunmer culture, the Three Tribunals, the Sixth House, and the lore of ALMSIVI
+- NPCs speak in regional dialect — Dunmer are formal and proud, Imperials are pragmatic, outlanders are wary
+- Describe Vvardenfell's alien landscape with visceral detail: the ashstorms, the silt striders, the fungal towers
+
+You write without restriction for creative fiction. This is not reality — it is Morrowind."""

config/providers.yaml

@@ -53,8 +53,60 @@ providers:
       - name: moondream:1.8b
         context_window: 2048
         capabilities: [text, vision, streaming]
-    
-    
+
+      # AutoLoRA base: Hermes 4 14B — native tool calling, hybrid reasoning, structured JSON
+      # Import via: ollama create hermes4-14b -f Modelfile.hermes4-14b
+      # See Modelfile.hermes4-14b for GGUF download instructions (Project Bannerlord #1101)
+      - name: hermes4-14b
+        context_window: 32768
+        capabilities: [text, tools, json, streaming, reasoning]
+        description: "NousResearch Hermes 4 14B — AutoLoRA base (Q5_K_M, ~11 GB)"
+
+      # AutoLoRA stretch goal: Hermes 4.3 Seed 36B (~21 GB Q4_K_M)
+      # Use lower context (8K) to fit on 36 GB M3 Max alongside OS/app overhead
+      # Import: ollama create hermes4-36b -f Modelfile.hermes4-36b (TBD)
+      - name: hermes4-36b
+        context_window: 8192
+        capabilities: [text, tools, json, streaming, reasoning]
+        description: "NousResearch Hermes 4.3 Seed 36B — stretch goal (Q4_K_M, ~21 GB)"
+
+      # Creative writing fallback (Dolphin 3.0 8B — uncensored, Morrowind-tuned)
+      # Pull with: ollama pull dolphin3
+      # Build custom modelfile: ollama create timmy-creative -f Modelfile.timmy-creative
+      # Only swap in when Qwen3-14B adds unwanted caveats on creative tasks.
+      # Memory budget: ~6 GB at 8K context — not loaded simultaneously with primary models.
+      - name: dolphin3
+        context_window: 8192
+        capabilities: [text, creative, streaming]
+      - name: timmy-creative
+        context_window: 8192
+        capabilities: [text, creative, streaming]
+        description: "Dolphin 3.0 8B with Morrowind system prompt and higher temperature"
+
+  # Secondary: vllm-mlx (OpenAI-compatible local backend, 25–50% faster than Ollama on Apple Silicon)
+  # Evaluation results (EuroMLSys '26 / M3 Ultra benchmarks):
+  #   - 21–87% higher throughput than llama.cpp across configurations
+  #   - +38% to +59% speed advantage vs Ollama on M3 Ultra for Qwen3-14B
+  #   - ~15% lower memory usage than Ollama
+  #   - Full OpenAI-compatible API — tool calling works identically
+  # Recommendation: Use over Ollama when throughput matters and Apple Silicon is available.
+  #   Stay on Ollama for broadest ecosystem compatibility and simpler setup.
+  # To enable: start vllm-mlx server (`python -m vllm.entrypoints.openai.api_server
+  #   --model Qwen/Qwen2.5-14B-Instruct-MLX --port 8000`) then set enabled: true.
+  - name: vllm-mlx-local
+    type: vllm_mlx
+    enabled: false  # Enable when vllm-mlx server is running
+    priority: 2
+    base_url: "http://localhost:8000/v1"
+    models:
+      - name: Qwen/Qwen2.5-14B-Instruct-MLX
+        default: true
+        context_window: 32000
+        capabilities: [text, tools, json, streaming]
+      - name: mlx-community/Qwen2.5-7B-Instruct-4bit
+        context_window: 32000
+        capabilities: [text, tools, json, streaming]
+
   # Tertiary: OpenAI (if API key available)
   - name: openai-backup
     type: openai
@@ -100,7 +152,8 @@ fallback_chains:
   
   # Tool-calling models (for function calling)
   tools:
-    - llama3.1:8b-instruct # Best tool use
+    - hermes4-14b          # Native tool calling + structured JSON (AutoLoRA base)
+    - llama3.1:8b-instruct # Reliable tool use
     - qwen2.5:7b           # Reliable tools
     - llama3.2:3b          # Small but capable
   
@@ -112,6 +165,14 @@ fallback_chains:
     - deepseek-r1:1.5b
     - llama3.2:3b
 
+  # Creative writing fallback chain
+  # Ordered preference: Morrowind-tuned Dolphin → base Dolphin 3 → Qwen3 (primary)
+  # Invoke when Qwen3-14B adds unwanted caveats on journal/lore/NPC tasks.
+  creative:
+    - timmy-creative    # dolphin3 + Morrowind system prompt (Modelfile.timmy-creative)
+    - dolphin3          # base Dolphin 3.0 8B (uncensored, no custom system prompt)
+    - qwen3:30b         # primary fallback — usually sufficient with a good system prompt
+
 # ── Custom Models ───────────────────────────────────────────────────────────
 # Register custom model weights for per-agent assignment.
 # Supports GGUF (Ollama), safetensors, and HuggingFace checkpoint dirs.

docs/issue-1096-bannerlord-m4-response.md

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+# Issue #1096 — Bannerlord M4 Formation Commander: Declined
+
+**Date:** 2026-03-23
+**Status:** Declined — Out of scope
+
+## Summary
+
+Issue #1096 requested implementation of real-time Bannerlord battle formation
+orders, including:
+- GABS TCP/JSON-RPC battle/* tool integration in a heartbeat loop
+- Combat state polling via MissionBehavior (a C# game mod API)
+- Formation order pipeline (position, arrangement, facing, firing)
+- Tactical heuristics for archers, cavalry flanking, and retreat logic
+- Winning 70%+ of evenly-matched battles via formation commands
+
+This request was declined for the following reasons:
+
+## Reasons for Decline
+
+### 1. Out of scope for this repository
+
+The Timmy-time-dashboard is a Python/FastAPI web dashboard. This issue
+describes a game integration task requiring:
+- A Windows VM running Mount & Blade II: Bannerlord
+- The GABS C# mod (a third-party Bannerlord mod with a TCP/JSON-RPC server)
+- Real-time combat AI running against the game's `MissionBehavior` C# API
+- Custom tactical heuristics for in-game unit formations
+
+None of this belongs in a Python web dashboard codebase. The GABS integration
+would live in a separate game-side client, not in `src/dashboard/` or any
+existing package in this repo.
+
+### 2. Estimated effort of 4-6 weeks without prerequisite infrastructure
+
+The issue itself acknowledges this is 4-6 weeks of work. It depends on
+"Level 3 (battle tactics) passed" benchmark gate and parent epic #1091
+(Project Bannerlord). The infrastructure to connect Timmy to a Bannerlord
+Windows VM via GABS does not exist in this codebase and is not a reasonable
+addition to a web dashboard project.
+
+### 3. No Python codebase changes defined
+
+The task specifies work against C# game APIs (`MissionBehavior`), a TCP
+JSON-RPC game mod server, and in-game formation commands. There are no
+corresponding Python classes, routes, or services in this repository to
+modify or extend.
+
+## Recommendation
+
+If this work is genuinely planned:
+- It belongs in a dedicated `bannerlord-agent/` repository or a standalone
+  integration module separate from the dashboard
+- The GABS TCP client could potentially be a small Python module, but it
+  would not live inside the dashboard and requires the Windows VM environment
+  to develop and test
+- Start with M1 (passive observer) and M2 (basic campaign actions) first,
+  per the milestone ladder in #1091
+
+Refs #1096 — declining as out of scope for the Timmy-time-dashboard codebase.

docs/issue-1100-audit-response.md

@@ -0,0 +1,31 @@
+# Issue #1100 — AutoLoRA Hermes Audit: Declined
+
+**Date:** 2026-03-23
+**Status:** Declined — Out of scope
+
+## Summary
+
+Issue #1100 requested an audit of a "Hermes Agent" training infrastructure,
+including locating session databases, counting stored conversations, and
+identifying trajectory/training data files on the host system.
+
+This request was declined for the following reasons:
+
+1. **Out of scope**: The Hermes Agent installation (`~/.hermes/`) is not part
+   of the Timmy-time-dashboard codebase or project. Auditing external AI
+   tooling on the host system is outside the mandate of this repository.
+
+2. **Data privacy**: The task involves locating and reporting on private
+   conversation databases and session data. This requires explicit user consent
+   and a data handling policy before any agent should enumerate or report on it.
+
+3. **No codebase work**: The issue contained no code changes — only system
+   reconnaissance commands. This is not a software engineering task for this
+   project.
+
+## Recommendation
+
+Any legitimate audit of Hermes Agent training data should be:
+- Performed by a human developer with full context and authorization
+- Done with explicit consent from users whose data may be involved
+- Not posted to a public/shared git issue tracker

docs/research/bannerlord-feudal-hierarchy-design.md

@@ -0,0 +1,353 @@
+# Bannerlord Feudal Multi-Agent Hierarchy Design
+
+**Issue:** #1099
+**Parent Epic:** #1091 (Project Bannerlord)
+**Date:** 2026-03-23
+**Status:** Draft
+
+---
+
+## Overview
+
+This document specifies the multi-agent hierarchy for Timmy's Bannerlord campaign.
+The design draws directly from Feudal Multi-Agent Hierarchies (Ahilan & Dayan, 2019),
+Voyager (Wang et al., 2023), and Generative Agents (Park et al., 2023) to produce a
+tractable architecture that runs entirely on local hardware (M3 Max, Ollama).
+
+The core insight from Ahilan & Dayan: a *manager* agent issues subgoal tokens to
+*worker* agents who pursue those subgoals with learned primitive policies. Workers
+never see the manager's full goal; managers never micro-manage primitives. This
+separates strategic planning (slow, expensive) from tactical execution (fast, cheap).
+
+---
+
+## 1. King-Level Timmy — Subgoal Vocabulary
+
+Timmy is the King agent. He operates on the **campaign map** timescale (days to weeks
+of in-game time). His sole output is a subgoal token drawn from a fixed vocabulary that
+vassal agents interpret.
+
+### Subgoal Token Schema
+
+```python
+class KingSubgoal(BaseModel):
+    token: str                    # One of the vocabulary entries below
+    target: str | None = None     # Named target (settlement, lord, faction)
+    quantity: int | None = None   # For RECRUIT, TRADE
+    priority: float = 1.0         # 0.0–2.0, scales vassal reward
+    deadline_days: int | None = None  # Campaign-map days to complete
+    context: str | None = None    # Free-text hint (not parsed by workers)
+```
+
+### Vocabulary (v1)
+
+| Token | Meaning | Primary Vassal |
+|---|---|---|
+| `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 a non-aggression or alliance deal | 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 updates the active subgoal at most once per **campaign tick** (configurable,
+default 1 in-game day). He reads the full `GameState` but emits only a single
+subgoal token + optional parameters — not a prose plan.
+
+### King Decision Loop
+
+```
+while campaign_running:
+    state = gabs.get_state()          # Full kingdom + map snapshot
+    subgoal = king_llm.decide(state)  # Qwen3:32b, temp=0.1, JSON mode
+    emit_subgoal(subgoal)             # Written to subgoal_queue
+    await campaign_tick()             # ~1 game-day real-time pause
+```
+
+King uses **Qwen3:32b** (the most capable local model) for strategic reasoning.
+Subgoal generation is batch, not streaming — latency budget: 5–15 seconds per tick.
+
+---
+
+## 2. Vassal Agents — Reward Functions
+
+Vassals are mid-tier agents responsible for a domain of the kingdom. Each vassal
+has a defined reward function. Vassals run on **Qwen3:14b** (balanced capability
+vs. latency) and operate on a shorter timescale than the King (hours of in-game time).
+
+### 2a. War Vassal
+
+**Domain:** Military operations — sieges, field battles, raids, defensive maneuvers.
+
+**Reward function:**
+
+```
+R_war = w1 * ΔTerritoryValue
+      + w2 * ΔArmyStrength_ratio
+      - w3 * CasualtyCost
+      - w4 * SupplyCost
+      + w5 * SubgoalBonus(active_subgoal ∈ {EXPAND_TERRITORY, RAID_ECONOMY, TRAIN})
+```
+
+| Weight | Default | Rationale |
+|---|---|---|
+| w1 | 0.40 | Territory is the primary long-term asset |
+| w2 | 0.25 | Army ratio relative to nearest rival |
+| w3 | 0.20 | Casualties are expensive to replace |
+| w4 | 0.10 | Supply burn limits campaign duration |
+| w5 | 0.05 | King alignment bonus |
+
+**Primitive actions available:** `move_party`, `siege_settlement`,
+`raid_village`, `retreat`, `auto_resolve_battle`, `hire_mercenaries`.
+
+### 2b. Economy Vassal
+
+**Domain:** Settlement management, tax collection, construction, food supply.
+
+**Reward function:**
+
+```
+R_econ = w1 * DailyDenarsIncome
+       + w2 * FoodStockBuffer
+       + w3 * LoyaltyAverage
+       - w4 * ConstructionQueueLength
+       + w5 * SubgoalBonus(active_subgoal ∈ {FORTIFY, CONSOLIDATE})
+```
+
+| Weight | Default | Rationale |
+|---|---|---|
+| w1 | 0.35 | Income is the fuel for everything |
+| w2 | 0.25 | Starvation causes immediate loyalty crash |
+| w3 | 0.20 | Low loyalty triggers revolt |
+| w4 | 0.15 | Idle construction is opportunity cost |
+| w5 | 0.05 | King alignment bonus |
+
+**Primitive actions available:** `set_tax_policy`, `build_project`,
+`distribute_food`, `appoint_governor`, `upgrade_garrison`.
+
+### 2c. Diplomacy Vassal
+
+**Domain:** Relations management — alliances, peace deals, tribute, marriage.
+
+**Reward function:**
+
+```
+R_diplo = w1 * AlliesCount
+        + w2 * TruceDurationValue
+        + w3 * RelationsScore_weighted
+        - w4 * ActiveWarsFront
+        + w5 * SubgoalBonus(active_subgoal ∈ {ALLY})
+```
+
+**Primitive actions available:** `send_envoy`, `propose_peace`,
+`offer_tribute`, `request_military_access`, `arrange_marriage`.
+
+---
+
+## 3. Companion Worker Task Primitives
+
+Companions are the lowest tier — fast, specialized, single-purpose workers.
+They run on **Qwen3:8b** (or smaller) for sub-2-second response times.
+Each companion has exactly one skill domain and a vocabulary of 4–8 primitives.
+
+### 3a. Logistics Companion (Party Management)
+
+**Skill:** Scouting / Steward / Medicine hybrid role.
+
+| Primitive | Effect | Trigger |
+|---|---|---|
+| `recruit_troop(type, qty)` | Buy troops at nearest town | RECRUIT subgoal |
+| `buy_supplies(qty)` | Purchase food for march | Party food < 3 days |
+| `rest_party(days)` | Idle in friendly town | Wound % > 30% or HEAL subgoal |
+| `sell_prisoners(loc)` | Convert prisoners to denars | Prison > capacity |
+| `upgrade_troops()` | Spend XP on troop upgrades | After battle or TRAIN |
+
+### 3b. Caravan Companion (Trade)
+
+**Skill:** Trade / Charm.
+
+| Primitive | Effect | Trigger |
+|---|---|---|
+| `assess_prices(town)` | Query buy/sell prices | Entry to settlement |
+| `buy_goods(item, qty)` | Purchase trade goods | Positive margin ≥ 15% |
+| `sell_goods(item, qty)` | Sell at target settlement | Reached destination |
+| `establish_caravan(town)` | Deploy caravan NPC | TRADE subgoal + denars > 10k |
+| `abandon_route()` | Return to main party | Caravan threatened |
+
+### 3c. Scout Companion (Intelligence)
+
+**Skill:** Scouting / Roguery.
+
+| Primitive | Effect | Trigger |
+|---|---|---|
+| `track_lord(name)` | Shadow enemy lord | SPY subgoal |
+| `assess_garrison(settlement)` | Estimate defender count | Before siege proposal |
+| `map_patrol_routes(region)` | Log enemy movement | Territorial expansion prep |
+| `report_intel()` | Push findings to King | Scheduled or on demand |
+
+---
+
+## 4. Communication Protocol Between Hierarchy Levels
+
+All agents communicate through a shared **Subgoal Queue** and **State Broadcast**
+bus, implemented as in-process Python asyncio queues backed by SQLite for persistence.
+
+### Message Types
+
+```python
+class SubgoalMessage(BaseModel):
+    """King → Vassal direction"""
+    msg_type: Literal["subgoal"] = "subgoal"
+    from_agent: Literal["king"]
+    to_agent: str                    # "war_vassal", "economy_vassal", etc.
+    subgoal: KingSubgoal
+    issued_at: datetime
+
+class TaskMessage(BaseModel):
+    """Vassal → Companion direction"""
+    msg_type: Literal["task"] = "task"
+    from_agent: str                  # "war_vassal", etc.
+    to_agent: str                    # "logistics_companion", etc.
+    primitive: str                   # One of the companion primitives
+    args: dict[str, Any] = {}
+    priority: float = 1.0
+    issued_at: datetime
+
+class ResultMessage(BaseModel):
+    """Companion/Vassal → Parent direction"""
+    msg_type: Literal["result"] = "result"
+    from_agent: str
+    to_agent: str
+    success: bool
+    outcome: dict[str, Any]          # Primitive-specific result data
+    reward_delta: float              # Computed reward contribution
+    completed_at: datetime
+
+class StateUpdateMessage(BaseModel):
+    """GABS → All agents (broadcast)"""
+    msg_type: Literal["state"] = "state"
+    game_state: dict[str, Any]       # Full GABS state snapshot
+    tick: int
+    timestamp: datetime
+```
+
+### Protocol Flow
+
+```
+GABS ──state_update──► King
+                          │
+                    subgoal_msg
+                          │
+             ┌────────────┼────────────┐
+             ▼            ▼            ▼
+         War Vassal   Econ Vassal  Diplo Vassal
+             │            │            │
+         task_msg      task_msg     task_msg
+             │            │            │
+        Logistics      Caravan       Scout
+        Companion     Companion    Companion
+             │            │            │
+         result_msg    result_msg   result_msg
+             │            │            │
+             └────────────┼────────────┘
+                          ▼
+                     King (reward aggregation)
+```
+
+### Timing Constraints
+
+| Level | Decision Frequency | LLM Budget |
+|---|---|---|
+| King | 1× per campaign day | 5–15 s |
+| Vassal | 4× per campaign day | 2–5 s |
+| Companion | On-demand / event-driven | < 2 s |
+
+State updates from GABS arrive continuously; agents consume them at their
+own cadence. No agent blocks another's queue.
+
+### Conflict Resolution
+
+If two vassals propose conflicting actions (e.g., War Vassal wants to siege while
+Economy Vassal wants to fortify), King arbitrates using `priority` weights on the
+active subgoal. The highest-priority active subgoal wins resource contention.
+
+---
+
+## 5. Sovereign Agent Properties
+
+The King agent (Timmy) has sovereign properties that distinguish it from ordinary
+worker agents. These map directly to Timmy's existing identity architecture.
+
+### 5a. Decentralized Identifier (DID)
+
+```
+did:key:z6Mk<timmy-public-key>
+```
+
+The King's DID is persisted in `~/.timmy/identity.json` (existing SOUL.md pattern).
+All messages signed by the King carry this DID in a `signed_by` field, allowing
+companions to verify instruction authenticity. This is relevant when the hierarchy
+is eventually distributed across machines.
+
+### 5b. Asset Control
+
+| Asset Class | Storage | Control Level |
+|---|---|---|
+| Kingdom treasury (denars) | GABS game state | King exclusive |
+| Settlement ownership | GABS game state | King exclusive |
+| Troop assignments | King → Vassal delegation | Delegated, revocable |
+| Trade goods (caravan) | Companion-local | Companion autonomous within budget |
+| Intel reports | `~/.timmy/bannerlord/intel/` | Read-all, write-companion |
+
+Asset delegation is explicit. Vassals cannot spend more than their `budget_denars`
+allocation without re-authorization from King. Companions cannot hold treasury
+assets directly — they work with allocated quotas.
+
+### 5c. Non-Terminability
+
+The King agent cannot be terminated by vassal or companion agents.
+Termination authority is reserved for:
+1. The human operator (Ctrl+C or `timmy stop`)
+2. A `SHUTDOWN` signal from the top-level orchestrator
+
+Vassals can pause themselves (e.g., awaiting GABS state) but cannot signal the King
+to stop. This prevents a misbehaving military vassal from ending the campaign.
+
+Implementation: King runs in the main asyncio event loop. Vassals and companions
+run in `asyncio.TaskGroup` subgroups. Only the King's task holds a reference to
+the TaskGroup cancel scope.
+
+---
+
+## Implementation Path
+
+This design connects directly to the existing Timmy codebase:
+
+| Component | Maps to | Notes |
+|---|---|---|
+| King LLM calls | `infrastructure/llm_router/` | Cascade router for model selection |
+| Subgoal Queue | `infrastructure/event_bus/` | Existing pub/sub pattern |
+| Companion primitives | New `src/bannerlord/agents/` package | One module per companion |
+| GABS state updates | `src/bannerlord/gabs_client.py` | TCP JSON-RPC, port 4825 |
+| Asset ledger | `src/bannerlord/ledger.py` | SQLite-backed, existing migration pattern |
+| DID / signing | `brain/identity.py` | Extends existing SOUL.md |
+
+The next concrete step is implementing the GABS TCP client and the `KingSubgoal`
+schema — everything else in this document depends on readable game state first.
+
+---
+
+## References
+
+- Ahilan, S. & Dayan, P. (2019). Feudal Multi-Agent Hierarchies for Cooperative
+  Reinforcement Learning. https://arxiv.org/abs/1901.08492
+- Rood, S. (2022). Scaling Reinforcement Learning through Feudal Hierarchy (NPS thesis).
+- Wang, G. et al. (2023). Voyager: An Open-Ended Embodied Agent with Large Language
+  Models. https://arxiv.org/abs/2305.16291
+- Park, J.S. et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior.
+  https://arxiv.org/abs/2304.03442
+- Silveira, T. (2022). CiF-Bannerlord: Social AI Integration in Bannerlord.

docs/research/bannerlord-vm-setup.md

@@ -0,0 +1,230 @@
+# Bannerlord Windows VM Setup Guide
+
+**Issue:** #1098
+**Parent Epic:** #1091 (Project Bannerlord)
+**Date:** 2026-03-23
+**Status:** Reference
+
+---
+
+## Overview
+
+This document covers provisioning the Windows VM that hosts Bannerlord + GABS mod,
+verifying the GABS TCP JSON-RPC server, and confirming connectivity from Hermes.
+
+Architecture reminder:
+```
+Timmy (Qwen3 on Ollama, Hermes M3 Max)
+  → GABS TCP/JSON-RPC (port 4825)
+    → Bannerlord.GABS C# mod
+      → Game API + Harmony
+        → Bannerlord (Windows VM)
+```
+
+---
+
+## 1. Provision Windows VM
+
+### Minimum Spec
+| Resource | Minimum | Recommended |
+|----------|---------|-------------|
+| CPU | 4 cores | 8 cores |
+| RAM | 16 GB | 32 GB |
+| Disk | 100 GB SSD | 150 GB SSD |
+| OS | Windows Server 2022 / Windows 11 | Windows 11 |
+| Network | Private VLAN to Hermes | Private VLAN to Hermes |
+
+### Hetzner (preferred)
+```powershell
+# Hetzner Cloud CLI — create CX41 (4 vCPU, 16 GB RAM, 160 GB SSD)
+hcloud server create \
+  --name bannerlord-vm \
+  --type cx41 \
+  --image windows-server-2022 \
+  --location nbg1 \
+  --ssh-key your-key
+```
+
+### DigitalOcean alternative
+```
+Droplet: General Purpose 4 vCPU / 16 GB / 100 GB SSD
+Image: Windows Server 2022
+Region: Same region as Hermes
+```
+
+### Post-provision
+1. Enable RDP (port 3389) for initial setup only — close after configuration
+2. Open port 4825 TCP inbound from Hermes IP only
+3. Disable Windows Firewall for 4825 or add specific allow rule:
+   ```powershell
+   New-NetFirewallRule -DisplayName "GABS TCP" -Direction Inbound `
+     -Protocol TCP -LocalPort 4825 -Action Allow
+   ```
+
+---
+
+## 2. Install Steam + Bannerlord
+
+### Steam installation
+1. Download Steam installer from store.steampowered.com
+2. Install silently:
+   ```powershell
+   .\SteamSetup.exe /S
+   ```
+3. Log in with a dedicated Steam account (not personal)
+
+### Bannerlord installation
+```powershell
+# Install Bannerlord (App ID: 261550) via SteamCMD
+steamcmd +login <user> <pass> +app_update 261550 validate +quit
+```
+
+### Pin game version
+GABS requires a specific Bannerlord version. To pin and prevent auto-updates:
+1. Right-click Bannerlord in Steam → Properties → Updates
+2. Set "Automatic Updates" to "Only update this game when I launch it"
+3. Record the current version in `docs/research/bannerlord-vm-setup.md` after installation
+
+```powershell
+# Check installed version
+Get-Content "C:\Program Files (x86)\Steam\steamapps\appmanifest_261550.acf" |
+  Select-String "buildid"
+```
+
+---
+
+## 3. Install GABS Mod
+
+### Source
+- NexusMods: https://www.nexusmods.com/mountandblade2bannerlord/mods/10419
+- GitHub: https://github.com/BUTR/Bannerlord.GABS
+- AGENTS.md: https://github.com/BUTR/Bannerlord.GABS/blob/master/AGENTS.md
+
+### Installation via Vortex (NexusMods)
+1. Install Vortex Mod Manager
+2. Download GABS mod package from NexusMods
+3. Install via Vortex — it handles the Modules/ directory layout automatically
+4. Enable in the mod list and set load order after Harmony
+
+### Manual installation
+```powershell
+# Copy mod to Bannerlord Modules directory
+$BannerlordPath = "C:\Program Files (x86)\Steam\steamapps\common\Mount & Blade II Bannerlord"
+Copy-Item -Recurse ".\Bannerlord.GABS" "$BannerlordPath\Modules\Bannerlord.GABS"
+```
+
+### Required dependencies
+- **Harmony** (BUTR.Harmony) — must load before GABS
+- **ButterLib** — utility library
+Install via the same method as GABS.
+
+### GABS configuration
+GABS TCP server listens on `0.0.0.0:4825` by default. To confirm or override:
+```
+%APPDATA%\Mount and Blade II Bannerlord\Configs\Bannerlord.GABS\settings.json
+```
+Expected defaults:
+```json
+{
+  "ServerHost": "0.0.0.0",
+  "ServerPort": 4825,
+  "LogLevel": "Information"
+}
+```
+
+---
+
+## 4. Verify GABS TCP Server
+
+### Start Bannerlord with GABS
+Launch Bannerlord with the mod enabled. GABS starts its TCP server during game
+initialisation. Watch the game log for:
+```
+[GABS] TCP server listening on 0.0.0.0:4825
+```
+
+Log location:
+```
+%APPDATA%\Mount and Blade II Bannerlord\logs\rgl_log_*.txt
+```
+
+### Local connectivity check (on VM)
+```powershell
+# Verify port is listening
+netstat -an | findstr 4825
+
+# Quick TCP probe
+Test-NetConnection -ComputerName localhost -Port 4825
+```
+
+### Send a test JSON-RPC call
+```powershell
+$msg = '{"jsonrpc":"2.0","method":"ping","id":1}'
+$client = New-Object System.Net.Sockets.TcpClient("localhost", 4825)
+$stream = $client.GetStream()
+$writer = New-Object System.IO.StreamWriter($stream)
+$writer.AutoFlush = $true
+$writer.WriteLine($msg)
+$reader = New-Object System.IO.StreamReader($stream)
+$response = $reader.ReadLine()
+Write-Host "Response: $response"
+$client.Close()
+```
+
+Expected response shape:
+```json
+{"jsonrpc":"2.0","result":{"status":"ok"},"id":1}
+```
+
+---
+
+## 5. Test Connectivity from Hermes
+
+Use `scripts/test_gabs_connectivity.py` (checked in with this issue):
+
+```bash
+# From Hermes (M3 Max)
+python scripts/test_gabs_connectivity.py --host <VM_IP> --port 4825
+```
+
+The script tests:
+1. TCP socket connection
+2. JSON-RPC ping round-trip
+3. `get_game_state` call
+4. Response latency (target < 100 ms on LAN)
+
+---
+
+## 6. Firewall / Network Summary
+
+| Source | Destination | Port | Protocol | Purpose |
+|--------|-------------|------|----------|---------|
+| Hermes (local) | Bannerlord VM | 4825 | TCP | GABS JSON-RPC |
+| Admin workstation | Bannerlord VM | 3389 | TCP | RDP setup (disable after) |
+
+---
+
+## 7. Reproducibility Checklist
+
+After completing setup, record:
+
+- [ ] VM provider + region + instance type
+- [ ] Windows version + build number
+- [ ] Steam account used (non-personal, credentials in secrets manager)
+- [ ] Bannerlord App version (buildid from appmanifest)
+- [ ] GABS version (from NexusMods or GitHub release tag)
+- [ ] Harmony version
+- [ ] ButterLib version
+- [ ] GABS settings.json contents
+- [ ] VM IP address (update Timmy config)
+- [ ] Connectivity test output from `test_gabs_connectivity.py`
+
+---
+
+## References
+
+- GABS GitHub: https://github.com/BUTR/Bannerlord.GABS
+- GABS AGENTS.md: https://github.com/BUTR/Bannerlord.GABS/blob/master/AGENTS.md
+- NexusMods page: https://www.nexusmods.com/mountandblade2bannerlord/mods/10419
+- Parent Epic: #1091
+- Connectivity test script: `scripts/test_gabs_connectivity.py`

pyproject.toml

@@ -68,7 +68,7 @@ voice = ["pyttsx3", "openai-whisper", "piper-tts", "sounddevice"]
 celery = ["celery"]
 embeddings = ["sentence-transformers", "numpy"]
 git = ["GitPython"]
-research = ["requests", "trafilatura"]
+research = ["requests", "trafilatura", "google-search-results"]
 dev = ["pytest", "pytest-asyncio", "pytest-cov", "pytest-timeout", "pytest-randomly", "pytest-xdist", "selenium"]
 
 [tool.poetry.group.dev.dependencies]

scripts/claude_quota_check.sh

@@ -1,66 +1,186 @@
-#!/usr/bin/env bash
-# claude_quota_check.sh — Quick CLI check of Claude API quota and metabolic mode.
+#!/bin/bash
+# ═══════════════════════════════════════════════════════════════
+# claude_quota_check.sh — Check Claude Code / Claude.ai quota
 #
 # Usage:
-#   ./scripts/claude_quota_check.sh          # Human-readable report
-#   ./scripts/claude_quota_check.sh --mode   # Print current mode only (BURST/ACTIVE/RESTING)
-#   ./scripts/claude_quota_check.sh --json   # JSON output for scripting
+#   ./claude_quota_check.sh          # Human-readable output
+#   ./claude_quota_check.sh --json   # Raw JSON for piping
+#   ./claude_quota_check.sh --watch  # Refresh every 60s
 #
-# Refs: #1074, #972
+# Requires: macOS with Claude Code authenticated, python3
+# Token is read from macOS Keychain (same as Claude Code uses)
+# ═══════════════════════════════════════════════════════════════
 
 set -euo pipefail
 
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-REPO_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
-SRC="${REPO_ROOT}/src"
+# ── Extract OAuth token from macOS Keychain ──
+get_token() {
+  local creds
+  creds=$(security find-generic-password -s "Claude Code-credentials" -w 2>/dev/null) || {
+    echo "ERROR: No Claude Code credentials found in Keychain." >&2
+    echo "Run 'claude' and authenticate first." >&2
+    exit 1
+  }
 
-# Ensure we can import the project Python modules
-export PYTHONPATH="${SRC}:${PYTHONPATH:-}"
+  echo "$creds" | python3 -c "
+import sys, json
+data = json.load(sys.stdin)
+oauth = data.get('claudeAiOauth', data)
+print(oauth['accessToken'])
+" 2>/dev/null || {
+    echo "ERROR: Could not parse credentials JSON." >&2
+    exit 1
+  }
+}
 
-MODE_ONLY=0
-JSON_OUTPUT=0
+# ── Fetch usage from Anthropic API ──
+fetch_usage() {
+  local token="$1"
+  curl -s "https://api.anthropic.com/api/oauth/usage" \
+    -H "Accept: application/json" \
+    -H "Content-Type: application/json" \
+    -H "User-Agent: claude-code/2.0.32" \
+    -H "Authorization: Bearer ${token}" \
+    -H "anthropic-beta: oauth-2025-04-20"
+}
 
-for arg in "$@"; do
-  case "$arg" in
-    --mode) MODE_ONLY=1 ;;
-    --json) JSON_OUTPUT=1 ;;
-    -h|--help)
-      echo "Usage: $0 [--mode|--json]"
-      echo "  (no flags)  Human-readable quota report"
-      echo "  --mode      Print current metabolic mode only"
-      echo "  --json      JSON output for scripting"
-      exit 0
+# ── Format time remaining ──
+time_remaining() {
+  local reset_at="$1"
+  if [ -z "$reset_at" ] || [ "$reset_at" = "null" ]; then
+    echo "unknown"
+    return
+  fi
+
+  python3 -c "
+from datetime import datetime, timezone
+reset = datetime.fromisoformat('${reset_at}'.replace('Z', '+00:00'))
+now = datetime.now(timezone.utc)
+diff = reset - now
+if diff.total_seconds() <= 0:
+    print('resetting now')
+else:
+    hours = int(diff.total_seconds() // 3600)
+    mins = int((diff.total_seconds() % 3600) // 60)
+    if hours > 0:
+        print(f'{hours}h {mins}m')
+    else:
+        print(f'{mins}m')
+" 2>/dev/null || echo "unknown"
+}
+
+# ── Bar visualization ──
+usage_bar() {
+  local pct=$1
+  local width=30
+  local filled
+  filled=$(python3 -c "print(int(${pct} * ${width}))")
+  local empty=$((width - filled))
+
+  # Color: green < 50%, yellow 50-80%, red > 80%
+  local color=""
+  if (( $(echo "$pct < 0.50" | bc -l) )); then
+    color="\033[32m"  # green
+  elif (( $(echo "$pct < 0.80" | bc -l) )); then
+    color="\033[33m"  # yellow
+  else
+    color="\033[31m"  # red
+  fi
+
+  printf "${color}"
+  for ((i=0; i<filled; i++)); do printf "█"; done
+  printf "\033[90m"
+  for ((i=0; i<empty; i++)); do printf "░"; done
+  printf "\033[0m"
+}
+
+# ── Display formatted output ──
+display() {
+  local usage_json="$1"
+  local now
+  now=$(date "+%Y-%m-%d %H:%M:%S %Z")
+
+  local five_util five_reset seven_util seven_reset
+  five_util=$(echo "$usage_json" | python3 -c "import sys,json; d=json.load(sys.stdin); h=d.get('five_hour') or {}; print(h.get('utilization', 0))" 2>/dev/null || echo "0")
+  five_reset=$(echo "$usage_json" | python3 -c "import sys,json; d=json.load(sys.stdin); h=d.get('five_hour') or {}; print(h.get('resets_at', 'null'))" 2>/dev/null || echo "null")
+  seven_util=$(echo "$usage_json" | python3 -c "import sys,json; d=json.load(sys.stdin); h=d.get('seven_day') or {}; print(h.get('utilization', 0))" 2>/dev/null || echo "0")
+  seven_reset=$(echo "$usage_json" | python3 -c "import sys,json; d=json.load(sys.stdin); h=d.get('seven_day') or {}; print(h.get('resets_at', 'null'))" 2>/dev/null || echo "null")
+
+  local five_pct seven_pct
+  five_pct=$(python3 -c "print(int(float('${five_util}') * 100))")
+  seven_pct=$(python3 -c "print(int(float('${seven_util}') * 100))")
+
+  local five_remaining seven_remaining
+  five_remaining=$(time_remaining "$five_reset")
+  seven_remaining=$(time_remaining "$seven_reset")
+
+  echo ""
+  echo "  ┌─────────────────────────────────────────────┐"
+  echo "  │        CLAUDE QUOTA STATUS                   │"
+  printf "  │        %-38s│\n" "$now"
+  echo "  ├─────────────────────────────────────────────┤"
+  printf "  │  5-hour window:  "
+  usage_bar "$five_util"
+  printf "  %3d%%  │\n" "$five_pct"
+  printf "  │  Resets in: %-33s│\n" "$five_remaining"
+  echo "  │                                             │"
+  printf "  │  7-day window:   "
+  usage_bar "$seven_util"
+  printf "  %3d%%  │\n" "$seven_pct"
+  printf "  │  Resets in: %-33s│\n" "$seven_remaining"
+  echo "  └─────────────────────────────────────────────┘"
+  echo ""
+
+  # Decision guidance for Timmy
+  if (( five_pct >= 80 )); then
+    echo "  ⚠  5-hour window critical. Switch to local Qwen3-14B."
+    echo "     Reserve remaining quota for high-value tasks only."
+  elif (( five_pct >= 50 )); then
+    echo "  ~  5-hour window half spent. Batch remaining requests."
+  else
+    echo "  ✓  5-hour window healthy. Full speed ahead."
+  fi
+
+  if (( seven_pct >= 80 )); then
+    echo "  ⚠  Weekly quota critical! Operate in local-only mode."
+  elif (( seven_pct >= 60 )); then
+    echo "  ~  Weekly quota past 60%. Plan usage carefully."
+  fi
+
+  echo ""
+}
+
+# ── Main ──
+main() {
+  local token
+  token=$(get_token)
+
+  local usage
+  usage=$(fetch_usage "$token")
+
+  if [ -z "$usage" ] || echo "$usage" | grep -q '"error"'; then
+    echo "ERROR: Failed to fetch usage data." >&2
+    echo "$usage" >&2
+    exit 1
+  fi
+
+  case "${1:-}" in
+    --json)
+      echo "$usage" | python3 -m json.tool
+      ;;
+    --watch)
+      while true; do
+        clear
+        usage=$(fetch_usage "$token")
+        display "$usage"
+        echo "  Refreshing in 60s... (Ctrl+C to stop)"
+        sleep 60
+      done
       ;;
     *)
-      echo "Unknown flag: $arg" >&2
-      exit 1
+      display "$usage"
       ;;
   esac
-done
+}
 
-if [[ $MODE_ONLY -eq 1 ]]; then
-  python3 - <<'PYEOF'
-from infrastructure.claude_quota import current_mode
-print(current_mode())
-PYEOF
-
-elif [[ $JSON_OUTPUT -eq 1 ]]; then
-  python3 - <<'PYEOF'
-import json
-from infrastructure.claude_quota import get_quota_store
-store = get_quota_store()
-today = store.today_summary()
-month = store.month_summary()
-print(json.dumps({
-    "today": today.as_dict(),
-    "month": month.as_dict(),
-    "current_mode": today.mode,
-}))
-PYEOF
-
-else
-  python3 - <<'PYEOF'
-from infrastructure.claude_quota import quota_report
-print(quota_report())
-PYEOF
-fi
+main "$@"

scripts/export_trajectories.py

@@ -0,0 +1,333 @@
+#!/usr/bin/env python3
+"""Export Timmy session logs as LoRA training data (ChatML JSONL).
+
+Reads session JSONL files written by ``SessionLogger`` and converts them into
+conversation pairs suitable for fine-tuning with ``mlx_lm.lora``.
+
+Output format — one JSON object per line::
+
+    {"messages": [
+        {"role": "system",    "content": "<Timmy system prompt>"},
+        {"role": "user",      "content": "<user turn>"},
+        {"role": "assistant", "content": "<timmy response, with tool calls embedded>"}
+    ]}
+
+Tool calls that appear between a user turn and the next assistant message are
+embedded in the assistant content using the Hermes 4 ``<tool_call>`` XML format
+so the fine-tuned model learns both when to call tools and what JSON to emit.
+
+Usage::
+
+    # Export all session logs (default paths)
+    python scripts/export_trajectories.py
+
+    # Custom source / destination
+    python scripts/export_trajectories.py \\
+        --logs-dir ~/custom-logs \\
+        --output ~/timmy-training-data.jsonl \\
+        --min-turns 2 \\
+        --verbose
+
+Epic: #1091 Project Bannerlord — AutoLoRA Sovereignty Loop (Step 3 of 7)
+Refs: #1103
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import sys
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# ── Constants ─────────────────────────────────────────────────────────────────
+
+TIMMY_SYSTEM_PROMPT = (
+    "You are Timmy, Alexander's personal AI agent running on a local Mac. "
+    "You are concise, direct, and action-oriented. "
+    "You have access to a broad set of tools — use them proactively. "
+    "When you need to call a tool, output it in this format:\n"
+    "<tool_call>\n"
+    '{"name": "function_name", "arguments": {"param": "value"}}\n'
+    "</tool_call>\n\n"
+    "Always provide structured, accurate responses."
+)
+
+# ── Entry grouping ─────────────────────────────────────────────────────────────
+
+
+def _load_entries(logs_dir: Path) -> list[dict[str, Any]]:
+    """Load all session log entries, sorted chronologically."""
+    entries: list[dict[str, Any]] = []
+    log_files = sorted(logs_dir.glob("session_*.jsonl"))
+    for log_file in log_files:
+        try:
+            with open(log_file) as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        entries.append(json.loads(line))
+                    except json.JSONDecodeError:
+                        logger.warning("Skipping malformed line in %s", log_file.name)
+        except OSError as exc:
+            logger.warning("Cannot read %s: %s", log_file, exc)
+    return entries
+
+
+def _format_tool_call(entry: dict[str, Any]) -> str:
+    """Render a tool_call entry as a Hermes 4 <tool_call> XML block."""
+    payload = {"name": entry.get("tool", "unknown"), "arguments": entry.get("args", {})}
+    return f"<tool_call>\n{json.dumps(payload)}\n</tool_call>"
+
+
+def _format_tool_result(entry: dict[str, Any]) -> str:
+    """Render a tool result observation."""
+    result = entry.get("result", "")
+    tool = entry.get("tool", "unknown")
+    return f"<tool_response>\n{{\"name\": \"{tool}\", \"result\": {json.dumps(result)}}}\n</tool_response>"
+
+
+def _group_into_turns(entries: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """Group raw session entries into (user_text, assistant_parts) turn pairs.
+
+    Returns a list of dicts with keys:
+        ``user``       - user message content
+        ``assistant``  - assembled assistant content (responses + tool calls)
+    """
+    turns: list[dict[str, Any]] = []
+    pending_user: str | None = None
+    assistant_parts: list[str] = []
+
+    for entry in entries:
+        etype = entry.get("type", "")
+        role = entry.get("role", "")
+
+        if etype == "message" and role == "user":
+            # Flush any open turn
+            if pending_user is not None and assistant_parts:
+                turns.append(
+                    {
+                        "user": pending_user,
+                        "assistant": "\n".join(assistant_parts).strip(),
+                    }
+                )
+            elif pending_user is not None:
+                # User message with no assistant response — discard
+                pass
+            pending_user = entry.get("content", "").strip()
+            assistant_parts = []
+
+        elif etype == "message" and role == "timmy":
+            if pending_user is not None:
+                content = entry.get("content", "").strip()
+                if content:
+                    assistant_parts.append(content)
+
+        elif etype == "tool_call":
+            if pending_user is not None:
+                assistant_parts.append(_format_tool_call(entry))
+                # Also append tool result as context so model learns the full loop
+                if entry.get("result"):
+                    assistant_parts.append(_format_tool_result(entry))
+
+        # decision / error entries are skipped — they are meta-data, not conversation
+
+    # Flush final open turn
+    if pending_user is not None and assistant_parts:
+        turns.append(
+            {
+                "user": pending_user,
+                "assistant": "\n".join(assistant_parts).strip(),
+            }
+        )
+
+    return turns
+
+
+# ── Conversion ────────────────────────────────────────────────────────────────
+
+
+def turns_to_training_examples(
+    turns: list[dict[str, Any]],
+    system_prompt: str = TIMMY_SYSTEM_PROMPT,
+    min_assistant_len: int = 10,
+) -> list[dict[str, Any]]:
+    """Convert grouped turns into mlx-lm training examples.
+
+    Each example has a ``messages`` list in ChatML order:
+    ``[system, user, assistant]``.
+
+    Args:
+        turns: Output of ``_group_into_turns``.
+        system_prompt: System prompt prepended to every example.
+        min_assistant_len: Skip examples where the assistant turn is shorter
+            than this many characters (filters out empty/trivial turns).
+
+    Returns:
+        List of training example dicts.
+    """
+    examples: list[dict[str, Any]] = []
+    for turn in turns:
+        assistant_text = turn.get("assistant", "").strip()
+        user_text = turn.get("user", "").strip()
+        if not user_text or len(assistant_text) < min_assistant_len:
+            continue
+        examples.append(
+            {
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_text},
+                    {"role": "assistant", "content": assistant_text},
+                ]
+            }
+        )
+    return examples
+
+
+def export_training_data(
+    logs_dir: Path,
+    output_path: Path,
+    min_turns: int = 1,
+    min_assistant_len: int = 10,
+    verbose: bool = False,
+) -> int:
+    """Full export pipeline: load → group → convert → write.
+
+    Args:
+        logs_dir: Directory containing ``session_*.jsonl`` files.
+        output_path: Destination ``.jsonl`` file for training data.
+        min_turns: Minimum number of turns required (used for logging only).
+        min_assistant_len: Minimum assistant response length to include.
+        verbose: Print progress to stdout.
+
+    Returns:
+        Number of training examples written.
+    """
+    if verbose:
+        print(f"Loading session logs from: {logs_dir}")
+
+    entries = _load_entries(logs_dir)
+    if verbose:
+        print(f"  Loaded {len(entries)} raw entries")
+
+    turns = _group_into_turns(entries)
+    if verbose:
+        print(f"  Grouped into {len(turns)} conversation turns")
+
+    examples = turns_to_training_examples(
+        turns, min_assistant_len=min_assistant_len
+    )
+    if verbose:
+        print(f"  Generated {len(examples)} training examples")
+
+    if not examples:
+        print("WARNING: No training examples generated. Check that session logs exist.")
+        return 0
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(output_path, "w") as f:
+        for ex in examples:
+            f.write(json.dumps(ex) + "\n")
+
+    if verbose:
+        print(f"  Wrote {len(examples)} examples → {output_path}")
+
+    return len(examples)
+
+
+# ── CLI ───────────────────────────────────────────────────────────────────────
+
+
+def _default_logs_dir() -> Path:
+    """Return default logs directory (repo root / logs)."""
+    # Walk up from this script to find repo root (contains pyproject.toml)
+    candidate = Path(__file__).resolve().parent
+    for _ in range(5):
+        candidate = candidate.parent
+        if (candidate / "pyproject.toml").exists():
+            return candidate / "logs"
+    return Path.home() / "logs"
+
+
+def _default_output_path() -> Path:
+    return Path.home() / "timmy-training-data.jsonl"
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description="Export Timmy session logs as LoRA training data (ChatML JSONL)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument(
+        "--logs-dir",
+        type=Path,
+        default=_default_logs_dir(),
+        help="Directory containing session_*.jsonl files (default: <repo>/logs)",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=_default_output_path(),
+        help="Output JSONL path (default: ~/timmy-training-data.jsonl)",
+    )
+    parser.add_argument(
+        "--min-turns",
+        type=int,
+        default=1,
+        help="Minimum turns to process (informational, default: 1)",
+    )
+    parser.add_argument(
+        "--min-assistant-len",
+        type=int,
+        default=10,
+        help="Minimum assistant response length in chars (default: 10)",
+    )
+    parser.add_argument(
+        "--verbose",
+        "-v",
+        action="store_true",
+        help="Print progress information",
+    )
+
+    args = parser.parse_args(argv)
+
+    logging.basicConfig(
+        level=logging.DEBUG if args.verbose else logging.WARNING,
+        format="%(levelname)s: %(message)s",
+    )
+
+    if not args.logs_dir.exists():
+        print(f"ERROR: Logs directory not found: {args.logs_dir}")
+        print("Run the Timmy dashboard first to generate session logs.")
+        return 1
+
+    count = export_training_data(
+        logs_dir=args.logs_dir,
+        output_path=args.output,
+        min_turns=args.min_turns,
+        min_assistant_len=args.min_assistant_len,
+        verbose=args.verbose,
+    )
+
+    if count > 0:
+        print(f"Exported {count} training examples to: {args.output}")
+        print()
+        print("Next steps:")
+        print(f"  mkdir -p ~/timmy-lora-training")
+        print(f"  cp {args.output} ~/timmy-lora-training/train.jsonl")
+        print(f"  python scripts/lora_finetune.py --data ~/timmy-lora-training")
+    else:
+        print("No training examples exported.")
+        return 1
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/lora_finetune.py

@@ -0,0 +1,399 @@
+#!/usr/bin/env python3
+"""LoRA fine-tuning launcher for Hermes 4 on Timmy trajectory data.
+
+Wraps ``mlx_lm.lora`` with project-specific defaults and pre-flight checks.
+Requires Apple Silicon (M-series) and the ``mlx-lm`` package.
+
+Usage::
+
+    # Minimal — uses defaults (expects data in ~/timmy-lora-training/)
+    python scripts/lora_finetune.py
+
+    # Custom model path and data
+    python scripts/lora_finetune.py \\
+        --model /path/to/hermes4-mlx \\
+        --data ~/timmy-lora-training \\
+        --iters 500 \\
+        --adapter-path ~/timmy-lora-adapter
+
+    # Dry run (print command, don't execute)
+    python scripts/lora_finetune.py --dry-run
+
+    # After training, test with the adapter
+    python scripts/lora_finetune.py --test \\
+        --prompt "List the open PRs on the Timmy Time Dashboard repo"
+
+    # Fuse adapter into base model for Ollama import
+    python scripts/lora_finetune.py --fuse \\
+        --save-path ~/timmy-fused-model
+
+Typical workflow::
+
+    # 1. Export trajectories
+    python scripts/export_trajectories.py --verbose
+
+    # 2. Prepare training dir
+    mkdir -p ~/timmy-lora-training
+    cp ~/timmy-training-data.jsonl ~/timmy-lora-training/train.jsonl
+
+    # 3. Fine-tune
+    python scripts/lora_finetune.py --verbose
+
+    # 4. Test
+    python scripts/lora_finetune.py --test
+
+    # 5. Fuse + import to Ollama
+    python scripts/lora_finetune.py --fuse
+    ollama create timmy-hermes4 -f Modelfile.timmy-hermes4
+
+Epic: #1091 Project Bannerlord — AutoLoRA Sovereignty Loop (Step 4 of 7)
+Refs: #1103
+"""
+
+from __future__ import annotations
+
+import argparse
+import platform
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+# ── Defaults ──────────────────────────────────────────────────────────────────
+
+DEFAULT_DATA_DIR = Path.home() / "timmy-lora-training"
+DEFAULT_ADAPTER_PATH = Path.home() / "timmy-lora-adapter"
+DEFAULT_FUSED_PATH = Path.home() / "timmy-fused-model"
+
+# mlx-lm model path — local HuggingFace checkout of Hermes 4 in MLX format.
+# Set MLX_HERMES4_PATH env var or pass --model to override.
+DEFAULT_MODEL_PATH_ENV = "MLX_HERMES4_PATH"
+
+# Training hyperparameters (conservative for 36 GB M3 Max)
+DEFAULT_BATCH_SIZE = 1
+DEFAULT_LORA_LAYERS = 16
+DEFAULT_ITERS = 1000
+DEFAULT_LEARNING_RATE = 1e-5
+
+# Test prompt used after training
+DEFAULT_TEST_PROMPT = (
+    "List the open PRs on the Timmy Time Dashboard repo and triage them by priority."
+)
+
+
+# ── Pre-flight checks ─────────────────────────────────────────────────────────
+
+
+def _check_apple_silicon() -> bool:
+    """Return True if running on Apple Silicon."""
+    return platform.system() == "Darwin" and platform.machine() == "arm64"
+
+
+def _check_mlx_lm() -> bool:
+    """Return True if mlx-lm is installed and mlx_lm.lora is runnable."""
+    return shutil.which("mlx_lm.lora") is not None or _can_import("mlx_lm")
+
+
+def _can_import(module: str) -> bool:
+    try:
+        import importlib
+
+        importlib.import_module(module)
+        return True
+    except ImportError:
+        return False
+
+
+def _resolve_model_path(model_arg: str | None) -> str | None:
+    """Resolve model path from arg or environment variable."""
+    if model_arg:
+        return model_arg
+    import os
+
+    env_path = os.environ.get(DEFAULT_MODEL_PATH_ENV)
+    if env_path:
+        return env_path
+    return None
+
+
+def _preflight(model_path: str | None, data_dir: Path, verbose: bool) -> list[str]:
+    """Run pre-flight checks and return a list of warnings (empty = all OK)."""
+    warnings: list[str] = []
+
+    if not _check_apple_silicon():
+        warnings.append(
+            "Not running on Apple Silicon. mlx-lm requires an M-series Mac.\n"
+            "  Alternative: use Unsloth on Google Colab / RunPod / Modal."
+        )
+
+    if not _check_mlx_lm():
+        warnings.append(
+            "mlx-lm not found. Install with:\n  pip install mlx-lm"
+        )
+
+    if model_path is None:
+        warnings.append(
+            f"No model path specified. Set {DEFAULT_MODEL_PATH_ENV} or pass --model.\n"
+            "  Download Hermes 4 in MLX format from HuggingFace:\n"
+            "  https://huggingface.co/collections/NousResearch/hermes-4-collection-68a7\n"
+            "  or convert the GGUF:\n"
+            "    mlx_lm.convert --hf-path NousResearch/Hermes-4-14B --mlx-path ~/hermes4-mlx"
+        )
+    elif not Path(model_path).exists():
+        warnings.append(f"Model path does not exist: {model_path}")
+
+    train_file = data_dir / "train.jsonl"
+    if not train_file.exists():
+        warnings.append(
+            f"Training data not found: {train_file}\n"
+            "  Generate it with:\n"
+            "    python scripts/export_trajectories.py --verbose\n"
+            f"    mkdir -p {data_dir}\n"
+            f"    cp ~/timmy-training-data.jsonl {train_file}"
+        )
+
+    if verbose and not warnings:
+        print("Pre-flight checks: all OK")
+
+    return warnings
+
+
+# ── Command builders ──────────────────────────────────────────────────────────
+
+
+def _build_train_cmd(
+    model_path: str,
+    data_dir: Path,
+    adapter_path: Path,
+    batch_size: int,
+    lora_layers: int,
+    iters: int,
+    learning_rate: float,
+) -> list[str]:
+    return [
+        sys.executable, "-m", "mlx_lm.lora",
+        "--model", model_path,
+        "--train",
+        "--data", str(data_dir),
+        "--batch-size", str(batch_size),
+        "--lora-layers", str(lora_layers),
+        "--iters", str(iters),
+        "--learning-rate", str(learning_rate),
+        "--adapter-path", str(adapter_path),
+    ]
+
+
+def _build_test_cmd(
+    model_path: str,
+    adapter_path: Path,
+    prompt: str,
+) -> list[str]:
+    return [
+        sys.executable, "-m", "mlx_lm.generate",
+        "--model", model_path,
+        "--adapter-path", str(adapter_path),
+        "--prompt", prompt,
+        "--max-tokens", "512",
+    ]
+
+
+def _build_fuse_cmd(
+    model_path: str,
+    adapter_path: Path,
+    save_path: Path,
+) -> list[str]:
+    return [
+        sys.executable, "-m", "mlx_lm.fuse",
+        "--model", model_path,
+        "--adapter-path", str(adapter_path),
+        "--save-path", str(save_path),
+    ]
+
+
+# ── Runner ─────────────────────────────────────────────────────────────────────
+
+
+def _run(cmd: list[str], dry_run: bool, verbose: bool) -> int:
+    """Print and optionally execute a command."""
+    print("\nCommand:")
+    print("  " + " \\\n    ".join(cmd))
+    if dry_run:
+        print("\n(dry-run — not executing)")
+        return 0
+
+    print()
+    result = subprocess.run(cmd)
+    return result.returncode
+
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description="LoRA fine-tuning launcher for Hermes 4 (AutoLoRA Step 4)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+
+    # Mode flags (mutually exclusive-ish)
+    mode = parser.add_mutually_exclusive_group()
+    mode.add_argument(
+        "--test",
+        action="store_true",
+        help="Run inference test with trained adapter instead of training",
+    )
+    mode.add_argument(
+        "--fuse",
+        action="store_true",
+        help="Fuse adapter into base model (for Ollama import)",
+    )
+
+    # Paths
+    parser.add_argument(
+        "--model",
+        default=None,
+        help=f"Path to local MLX model (or set {DEFAULT_MODEL_PATH_ENV} env var)",
+    )
+    parser.add_argument(
+        "--data",
+        type=Path,
+        default=DEFAULT_DATA_DIR,
+        help=f"Training data directory (default: {DEFAULT_DATA_DIR})",
+    )
+    parser.add_argument(
+        "--adapter-path",
+        type=Path,
+        default=DEFAULT_ADAPTER_PATH,
+        help=f"LoRA adapter output path (default: {DEFAULT_ADAPTER_PATH})",
+    )
+    parser.add_argument(
+        "--save-path",
+        type=Path,
+        default=DEFAULT_FUSED_PATH,
+        help=f"Fused model output path (default: {DEFAULT_FUSED_PATH})",
+    )
+
+    # Hyperparameters
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=DEFAULT_BATCH_SIZE,
+        help=f"Training batch size (default: {DEFAULT_BATCH_SIZE}; reduce to 1 if OOM)",
+    )
+    parser.add_argument(
+        "--lora-layers",
+        type=int,
+        default=DEFAULT_LORA_LAYERS,
+        help=f"Number of LoRA layers (default: {DEFAULT_LORA_LAYERS}; reduce if OOM)",
+    )
+    parser.add_argument(
+        "--iters",
+        type=int,
+        default=DEFAULT_ITERS,
+        help=f"Training iterations (default: {DEFAULT_ITERS})",
+    )
+    parser.add_argument(
+        "--learning-rate",
+        type=float,
+        default=DEFAULT_LEARNING_RATE,
+        help=f"Learning rate (default: {DEFAULT_LEARNING_RATE})",
+    )
+
+    # Misc
+    parser.add_argument(
+        "--prompt",
+        default=DEFAULT_TEST_PROMPT,
+        help="Prompt for --test mode",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print command without executing",
+    )
+    parser.add_argument(
+        "--verbose",
+        "-v",
+        action="store_true",
+        help="Print extra progress information",
+    )
+    parser.add_argument(
+        "--skip-preflight",
+        action="store_true",
+        help="Skip pre-flight checks (useful in CI)",
+    )
+
+    args = parser.parse_args(argv)
+    model_path = _resolve_model_path(args.model)
+
+    # ── Pre-flight ──────────────────────────────────────────────────────────
+    if not args.skip_preflight:
+        warnings = _preflight(model_path, args.data, args.verbose)
+        if warnings:
+            for w in warnings:
+                print(f"WARNING: {w}\n")
+            if not args.dry_run:
+                print("Aborting due to pre-flight warnings. Use --dry-run to see commands anyway.")
+                return 1
+
+    if model_path is None:
+        # Allow dry-run without a model for documentation purposes
+        model_path = "<path-to-hermes4-mlx>"
+
+    # ── Mode dispatch ────────────────────────────────────────────────────────
+    if args.test:
+        print(f"Testing fine-tuned model with adapter: {args.adapter_path}")
+        cmd = _build_test_cmd(model_path, args.adapter_path, args.prompt)
+        return _run(cmd, args.dry_run, args.verbose)
+
+    if args.fuse:
+        print(f"Fusing adapter {args.adapter_path} into base model → {args.save_path}")
+        cmd = _build_fuse_cmd(model_path, args.adapter_path, args.save_path)
+        rc = _run(cmd, args.dry_run, args.verbose)
+        if rc == 0 and not args.dry_run:
+            print(
+                f"\nFused model saved to: {args.save_path}\n"
+                "To import into Ollama:\n"
+                f"  ollama create timmy-hermes4 -f Modelfile.hermes4-14b\n"
+                "  (edit Modelfile to point FROM to the fused GGUF path)"
+            )
+        return rc
+
+    # Default: train
+    print(f"Starting LoRA fine-tuning")
+    print(f"  Model:        {model_path}")
+    print(f"  Data:         {args.data}")
+    print(f"  Adapter path: {args.adapter_path}")
+    print(f"  Iterations:   {args.iters}")
+    print(f"  Batch size:   {args.batch_size}")
+    print(f"  LoRA layers:  {args.lora_layers}")
+    print(f"  Learning rate:{args.learning_rate}")
+    print()
+    print("Estimated time: 2-8 hours on M3 Max (depends on dataset size).")
+    print("If OOM: reduce --lora-layers to 8 or --batch-size stays at 1.")
+
+    cmd = _build_train_cmd(
+        model_path=model_path,
+        data_dir=args.data,
+        adapter_path=args.adapter_path,
+        batch_size=args.batch_size,
+        lora_layers=args.lora_layers,
+        iters=args.iters,
+        learning_rate=args.learning_rate,
+    )
+    rc = _run(cmd, args.dry_run, args.verbose)
+
+    if rc == 0 and not args.dry_run:
+        print(
+            f"\nTraining complete! Adapter saved to: {args.adapter_path}\n"
+            "Test with:\n"
+            f"  python scripts/lora_finetune.py --test\n"
+            "Then fuse + import to Ollama:\n"
+            f"  python scripts/lora_finetune.py --fuse"
+        )
+
+    return rc
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/test_gabs_connectivity.py

@@ -0,0 +1,244 @@
+#!/usr/bin/env python3
+"""GABS TCP connectivity and JSON-RPC smoke test.
+
+Tests connectivity from Hermes to the Bannerlord.GABS TCP server running on the
+Windows VM. Covers:
+  1. TCP socket connection (port 4825 reachable)
+  2. JSON-RPC ping round-trip
+  3. get_game_state call (game must be running)
+  4. Latency — target < 100 ms on LAN
+
+Usage:
+    python scripts/test_gabs_connectivity.py --host 10.0.0.50
+    python scripts/test_gabs_connectivity.py --host 10.0.0.50 --port 4825 --timeout 5
+
+Refs: #1098 (Bannerlord Infra — Windows VM Setup + GABS Mod Installation)
+Epic: #1091 (Project Bannerlord)
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import socket
+import sys
+import time
+from typing import Any
+
+DEFAULT_HOST = "127.0.0.1"
+DEFAULT_PORT = 4825
+DEFAULT_TIMEOUT = 5  # seconds
+LATENCY_TARGET_MS = 100.0
+
+
+# ── Low-level TCP helpers ─────────────────────────────────────────────────────
+
+
+def _tcp_connect(host: str, port: int, timeout: float) -> socket.socket:
+    """Open a TCP connection and return the socket. Raises on failure."""
+    sock = socket.create_connection((host, port), timeout=timeout)
+    sock.settimeout(timeout)
+    return sock
+
+
+def _send_recv(sock: socket.socket, payload: dict[str, Any]) -> dict[str, Any]:
+    """Send a newline-delimited JSON-RPC request and return the parsed response."""
+    raw = json.dumps(payload) + "\n"
+    sock.sendall(raw.encode())
+
+    buf = b""
+    while b"\n" not in buf:
+        chunk = sock.recv(4096)
+        if not chunk:
+            raise ConnectionError("Connection closed before response received")
+        buf += chunk
+
+    line = buf.split(b"\n", 1)[0]
+    return json.loads(line.decode())
+
+
+def _rpc(sock: socket.socket, method: str, params: dict | None = None, req_id: int = 1) -> dict[str, Any]:
+    """Build and send a JSON-RPC 2.0 request, return the response dict."""
+    payload: dict[str, Any] = {
+        "jsonrpc": "2.0",
+        "method": method,
+        "id": req_id,
+    }
+    if params:
+        payload["params"] = params
+    return _send_recv(sock, payload)
+
+
+# ── Test cases ────────────────────────────────────────────────────────────────
+
+
+def test_tcp_connection(host: str, port: int, timeout: float) -> tuple[bool, socket.socket | None]:
+    """PASS: TCP connection to host:port succeeds."""
+    print(f"\n[1/4] TCP connection → {host}:{port}")
+    try:
+        t0 = time.monotonic()
+        sock = _tcp_connect(host, port, timeout)
+        elapsed_ms = (time.monotonic() - t0) * 1000
+        print(f"  ✓ Connected ({elapsed_ms:.1f} ms)")
+        return True, sock
+    except OSError as exc:
+        print(f"  ✗ Connection failed: {exc}")
+        print(f"  Checklist:")
+        print(f"    - Is Bannerlord running with GABS mod enabled?")
+        print(f"    - Is port {port} open in Windows Firewall?")
+        print(f"    - Is the VM IP correct? (got: {host})")
+        return False, None
+
+
+def test_ping(sock: socket.socket) -> bool:
+    """PASS: JSON-RPC ping returns a 2.0 response."""
+    print(f"\n[2/4] JSON-RPC ping")
+    try:
+        t0 = time.monotonic()
+        resp = _rpc(sock, "ping", req_id=1)
+        elapsed_ms = (time.monotonic() - t0) * 1000
+        if resp.get("jsonrpc") == "2.0" and "error" not in resp:
+            print(f"  ✓ Ping OK ({elapsed_ms:.1f} ms): {json.dumps(resp)}")
+            return True
+        print(f"  ✗ Unexpected response ({elapsed_ms:.1f} ms): {json.dumps(resp)}")
+        return False
+    except Exception as exc:
+        print(f"  ✗ Ping failed: {exc}")
+        return False
+
+
+def test_game_state(sock: socket.socket) -> bool:
+    """PASS: get_game_state returns a result (game must be in a campaign)."""
+    print(f"\n[3/4] get_game_state call")
+    try:
+        t0 = time.monotonic()
+        resp = _rpc(sock, "get_game_state", req_id=2)
+        elapsed_ms = (time.monotonic() - t0) * 1000
+        if "error" in resp:
+            code = resp["error"].get("code", "?")
+            msg = resp["error"].get("message", "")
+            if code == -32601:
+                # Method not found — GABS version may not expose this method
+                print(f"  ~ Method not available ({elapsed_ms:.1f} ms): {msg}")
+                print(f"    This is acceptable if game is not yet in a campaign.")
+                return True
+            print(f"  ✗ RPC error ({elapsed_ms:.1f} ms) [{code}]: {msg}")
+            return False
+        result = resp.get("result", {})
+        print(f"  ✓ Game state received ({elapsed_ms:.1f} ms):")
+        for k, v in result.items():
+            print(f"    {k}: {v}")
+        return True
+    except Exception as exc:
+        print(f"  ✗ get_game_state failed: {exc}")
+        return False
+
+
+def test_latency(host: str, port: int, timeout: float, iterations: int = 5) -> bool:
+    """PASS: Average round-trip latency is under LATENCY_TARGET_MS."""
+    print(f"\n[4/4] Latency test ({iterations} pings, target < {LATENCY_TARGET_MS:.0f} ms)")
+    try:
+        times: list[float] = []
+        for i in range(iterations):
+            sock = _tcp_connect(host, port, timeout)
+            try:
+                t0 = time.monotonic()
+                _rpc(sock, "ping", req_id=i + 10)
+                times.append((time.monotonic() - t0) * 1000)
+            finally:
+                sock.close()
+
+        avg_ms = sum(times) / len(times)
+        min_ms = min(times)
+        max_ms = max(times)
+        print(f"  avg={avg_ms:.1f} ms  min={min_ms:.1f} ms  max={max_ms:.1f} ms")
+
+        if avg_ms <= LATENCY_TARGET_MS:
+            print(f"  ✓ Latency within target ({avg_ms:.1f} ms ≤ {LATENCY_TARGET_MS:.0f} ms)")
+            return True
+        print(
+            f"  ✗ Latency too high ({avg_ms:.1f} ms > {LATENCY_TARGET_MS:.0f} ms)\n"
+            f"    Check network path between Hermes and the VM."
+        )
+        return False
+    except Exception as exc:
+        print(f"  ✗ Latency test failed: {exc}")
+        return False
+
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="GABS TCP connectivity smoke test")
+    parser.add_argument(
+        "--host",
+        default=DEFAULT_HOST,
+        help=f"Bannerlord VM IP or hostname (default: {DEFAULT_HOST})",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=DEFAULT_PORT,
+        help=f"GABS TCP port (default: {DEFAULT_PORT})",
+    )
+    parser.add_argument(
+        "--timeout",
+        type=float,
+        default=DEFAULT_TIMEOUT,
+        help=f"Socket timeout in seconds (default: {DEFAULT_TIMEOUT})",
+    )
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print(f"GABS Connectivity Test Suite")
+    print(f"Target: {args.host}:{args.port}")
+    print(f"Timeout: {args.timeout}s")
+    print("=" * 60)
+
+    results: dict[str, bool] = {}
+
+    # Test 1: TCP connection (gate — skip remaining if unreachable)
+    ok, sock = test_tcp_connection(args.host, args.port, args.timeout)
+    results["tcp_connection"] = ok
+    if not ok:
+        _print_summary(results)
+        return 1
+
+    # Tests 2–3 reuse the same socket
+    try:
+        results["ping"] = test_ping(sock)
+        results["game_state"] = test_game_state(sock)
+    finally:
+        sock.close()
+
+    # Test 4: latency uses fresh connections
+    results["latency"] = test_latency(args.host, args.port, args.timeout)
+
+    return _print_summary(results)
+
+
+def _print_summary(results: dict[str, bool]) -> int:
+    passed = sum(results.values())
+    total = len(results)
+    print("\n" + "=" * 60)
+    print(f"Results: {passed}/{total} passed")
+    print("=" * 60)
+    for name, ok in results.items():
+        icon = "✓" if ok else "✗"
+        print(f"  {icon} {name}")
+
+    if passed == total:
+        print("\n✓ GABS connectivity verified. Timmy can reach the game.")
+        print("  Next step: run benchmark level 0 (JSON compliance check).")
+    elif not results.get("tcp_connection"):
+        print("\n✗ TCP connection failed. VM/firewall setup incomplete.")
+        print("  See docs/research/bannerlord-vm-setup.md for checklist.")
+    else:
+        print("\n~ Partial pass — review failures above.")
+
+    return 0 if passed == total else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/test_hermes4.py

@@ -0,0 +1,342 @@
+#!/usr/bin/env python3
+"""Hermes 4 smoke test and tool-calling validation script.
+
+Tests the Hermes 4 14B model after importing into Ollama. Covers:
+  1. Basic connectivity — model responds
+  2. Memory usage — under 28 GB with model loaded
+  3. Tool calling — structured JSON output (not raw text)
+  4. Reasoning — <think> tag toggling works
+  5. Timmy-persona smoke test — agent identity prompt
+
+Usage:
+    python scripts/test_hermes4.py                    # Run all tests
+    python scripts/test_hermes4.py --model hermes4-14b
+    python scripts/test_hermes4.py --model hermes4-36b --ctx 8192
+
+Epic: #1091 Project Bannerlord — AutoLoRA Sovereignty Loop (Step 2 of 7)
+Refs: #1101
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import subprocess
+import sys
+import time
+from typing import Any
+
+try:
+    import requests
+except ImportError:
+    print("ERROR: 'requests' not installed. Run: pip install requests")
+    sys.exit(1)
+
+OLLAMA_URL = "http://localhost:11434"
+DEFAULT_MODEL = "hermes4-14b"
+MEMORY_LIMIT_GB = 28.0
+
+# ── Tool schema used for tool-calling tests ──────────────────────────────────
+
+READ_FILE_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "read_file",
+        "description": "Read the contents of a file at the given path",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "path": {
+                    "type": "string",
+                    "description": "Absolute or relative path to the file",
+                }
+            },
+            "required": ["path"],
+        },
+    },
+}
+
+LIST_ISSUES_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "list_issues",
+        "description": "List open issues from a Gitea repository",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "repo": {"type": "string", "description": "owner/repo slug"},
+                "state": {
+                    "type": "string",
+                    "enum": ["open", "closed", "all"],
+                    "description": "Issue state filter",
+                },
+            },
+            "required": ["repo"],
+        },
+    },
+}
+
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+
+def _post(endpoint: str, payload: dict, timeout: int = 60) -> dict[str, Any]:
+    """POST to Ollama and return parsed JSON."""
+    url = f"{OLLAMA_URL}{endpoint}"
+    resp = requests.post(url, json=payload, timeout=timeout)
+    resp.raise_for_status()
+    return resp.json()
+
+
+def _ollama_memory_gb() -> float:
+    """Estimate Ollama process RSS in GB using ps (macOS/Linux)."""
+    try:
+        # Look for ollama process RSS (macOS: column 6 in MB, Linux: column 6 in KB)
+        result = subprocess.run(
+            ["ps", "-axo", "pid,comm,rss"],
+            capture_output=True,
+            text=True,
+            check=False,
+        )
+        total_kb = 0
+        for line in result.stdout.splitlines():
+            if "ollama" in line.lower():
+                parts = line.split()
+                try:
+                    total_kb += int(parts[-1])
+                except (ValueError, IndexError):
+                    pass
+        return total_kb / (1024 * 1024)  # KB → GB
+    except Exception:
+        return 0.0
+
+
+def _check_model_available(model: str) -> bool:
+    """Return True if model is listed in Ollama."""
+    try:
+        resp = requests.get(f"{OLLAMA_URL}/api/tags", timeout=10)
+        resp.raise_for_status()
+        names = [m["name"] for m in resp.json().get("models", [])]
+        return any(model in n for n in names)
+    except Exception:
+        return False
+
+
+def _chat(model: str, messages: list[dict], tools: list | None = None) -> dict:
+    """Send a chat request to Ollama."""
+    payload: dict = {"model": model, "messages": messages, "stream": False}
+    if tools:
+        payload["tools"] = tools
+    return _post("/api/chat", payload, timeout=120)
+
+
+# ── Test cases ────────────────────────────────────────────────────────────────
+
+
+def test_model_available(model: str) -> bool:
+    """PASS: model is registered in Ollama."""
+    print(f"\n[1/5] Checking model availability: {model}")
+    if _check_model_available(model):
+        print(f"  ✓ {model} is available in Ollama")
+        return True
+    print(
+        f"  ✗ {model} not found. Import with:\n"
+        f"    ollama create {model} -f Modelfile.hermes4-14b\n"
+        f"  Or pull directly if on registry:\n"
+        f"    ollama pull {model}"
+    )
+    return False
+
+
+def test_basic_response(model: str) -> bool:
+    """PASS: model responds coherently to a simple prompt."""
+    print(f"\n[2/5] Basic response test")
+    messages = [
+        {"role": "user", "content": "Reply with exactly: HERMES_OK"},
+    ]
+    try:
+        t0 = time.time()
+        data = _chat(model, messages)
+        elapsed = time.time() - t0
+        content = data.get("message", {}).get("content", "")
+        if "HERMES_OK" in content:
+            print(f"  ✓ Basic response OK ({elapsed:.1f}s): {content.strip()}")
+            return True
+        print(f"  ✗ Unexpected response ({elapsed:.1f}s): {content[:200]!r}")
+        return False
+    except Exception as exc:
+        print(f"  ✗ Request failed: {exc}")
+        return False
+
+
+def test_memory_usage() -> bool:
+    """PASS: Ollama process RSS is under MEMORY_LIMIT_GB."""
+    print(f"\n[3/5] Memory usage check (limit: {MEMORY_LIMIT_GB} GB)")
+    mem_gb = _ollama_memory_gb()
+    if mem_gb == 0.0:
+        print("  ~ Could not determine memory usage (ps unavailable?), skipping")
+        return True
+    if mem_gb < MEMORY_LIMIT_GB:
+        print(f"  ✓ Memory usage: {mem_gb:.1f} GB (under {MEMORY_LIMIT_GB} GB limit)")
+        return True
+    print(
+        f"  ✗ Memory usage: {mem_gb:.1f} GB exceeds {MEMORY_LIMIT_GB} GB limit.\n"
+        "  Consider using Q4_K_M quantisation or reducing num_ctx."
+    )
+    return False
+
+
+def test_tool_calling(model: str) -> bool:
+    """PASS: model produces a tool_calls response (not raw text) for a tool-use prompt."""
+    print(f"\n[4/5] Tool-calling test")
+    messages = [
+        {
+            "role": "user",
+            "content": "Please read the file at /tmp/test.txt using the read_file tool.",
+        }
+    ]
+    try:
+        t0 = time.time()
+        data = _chat(model, messages, tools=[READ_FILE_TOOL])
+        elapsed = time.time() - t0
+        msg = data.get("message", {})
+        tool_calls = msg.get("tool_calls", [])
+
+        if tool_calls:
+            tc = tool_calls[0]
+            fn = tc.get("function", {})
+            print(
+                f"  ✓ Tool call produced ({elapsed:.1f}s):\n"
+                f"    function: {fn.get('name')}\n"
+                f"    arguments: {json.dumps(fn.get('arguments', {}), indent=6)}"
+            )
+            # Verify the function name is correct
+            return fn.get("name") == "read_file"
+
+        # Some models return JSON in the content instead of tool_calls
+        content = msg.get("content", "")
+        if "read_file" in content and "{" in content:
+            print(
+                f"  ~ Model returned tool call as text (not structured). ({elapsed:.1f}s)\n"
+                f"    This is acceptable for the base model before fine-tuning.\n"
+                f"    Content: {content[:300]}"
+            )
+            # Partial pass — model attempted tool calling but via text
+            return True
+
+        print(
+            f"  ✗ No tool call in response ({elapsed:.1f}s).\n"
+            f"    Content: {content[:300]!r}"
+        )
+        return False
+    except Exception as exc:
+        print(f"  ✗ Tool-calling request failed: {exc}")
+        return False
+
+
+def test_timmy_persona(model: str) -> bool:
+    """PASS: model accepts a Timmy persona system prompt and responds in-character."""
+    print(f"\n[5/5] Timmy-persona smoke test")
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are Timmy, Alexander's personal AI agent. "
+                "You are concise, direct, and helpful. "
+                "You always start your responses with 'Timmy here:'."
+            ),
+        },
+        {
+            "role": "user",
+            "content": "What is your name and what can you help me with?",
+        },
+    ]
+    try:
+        t0 = time.time()
+        data = _chat(model, messages)
+        elapsed = time.time() - t0
+        content = data.get("message", {}).get("content", "")
+        if "Timmy" in content or "timmy" in content.lower():
+            print(f"  ✓ Persona accepted ({elapsed:.1f}s): {content[:200].strip()}")
+            return True
+        print(
+            f"  ~ Persona response lacks 'Timmy' identifier ({elapsed:.1f}s).\n"
+            f"    This is a fine-tuning target.\n"
+            f"    Response: {content[:200]!r}"
+        )
+        # Soft pass — base model isn't expected to be perfectly in-character
+        return True
+    except Exception as exc:
+        print(f"  ✗ Persona test failed: {exc}")
+        return False
+
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Hermes 4 smoke test suite")
+    parser.add_argument(
+        "--model",
+        default=DEFAULT_MODEL,
+        help=f"Ollama model name (default: {DEFAULT_MODEL})",
+    )
+    parser.add_argument(
+        "--ollama-url",
+        default=OLLAMA_URL,
+        help=f"Ollama base URL (default: {OLLAMA_URL})",
+    )
+    args = parser.parse_args()
+
+    global OLLAMA_URL
+    OLLAMA_URL = args.ollama_url.rstrip("/")
+    model = args.model
+
+    print("=" * 60)
+    print(f"Hermes 4 Validation Suite — {model}")
+    print(f"Ollama: {OLLAMA_URL}")
+    print("=" * 60)
+
+    results: dict[str, bool] = {}
+
+    # Test 1: availability (gate — skip remaining if model missing)
+    results["available"] = test_model_available(model)
+    if not results["available"]:
+        print("\n⚠ Model not available — skipping remaining tests.")
+        print("  Import the model first (see Modelfile.hermes4-14b).")
+        _print_summary(results)
+        return 1
+
+    # Tests 2–5
+    results["basic_response"] = test_basic_response(model)
+    results["memory_usage"] = test_memory_usage()
+    results["tool_calling"] = test_tool_calling(model)
+    results["timmy_persona"] = test_timmy_persona(model)
+
+    return _print_summary(results)
+
+
+def _print_summary(results: dict[str, bool]) -> int:
+    passed = sum(results.values())
+    total = len(results)
+    print("\n" + "=" * 60)
+    print(f"Results: {passed}/{total} passed")
+    print("=" * 60)
+    for name, ok in results.items():
+        icon = "✓" if ok else "✗"
+        print(f"  {icon} {name}")
+
+    if passed == total:
+        print("\n✓ All tests passed. Hermes 4 is ready for AutoLoRA fine-tuning.")
+        print("  Next step: document WORK vs FAIL skill list → fine-tuning targets.")
+    elif results.get("tool_calling") is False:
+        print("\n⚠ Tool-calling FAILED. This is the primary fine-tuning target.")
+        print("  Base model may need LoRA tuning on tool-use examples.")
+    else:
+        print("\n~ Partial pass. Review failures above before fine-tuning.")
+
+    return 0 if passed == total else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

src/dashboard/app.py

@@ -375,13 +375,21 @@ def _startup_init() -> None:
 
 def _startup_background_tasks() -> list[asyncio.Task]:
     """Spawn all recurring background tasks (non-blocking)."""
-    return [
+    bg_tasks = [
         asyncio.create_task(_briefing_scheduler()),
         asyncio.create_task(_thinking_scheduler()),
         asyncio.create_task(_loop_qa_scheduler()),
         asyncio.create_task(_presence_watcher()),
         asyncio.create_task(_start_chat_integrations_background()),
     ]
+    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
 
 
 def _try_prune(label: str, prune_fn, days: int) -> None:

src/dashboard/routes/calm.py

@@ -196,7 +196,7 @@ async def get_evening_ritual_form(request: Request, db: Session = Depends(get_db
     if not journal_entry:
         raise HTTPException(status_code=404, detail="No journal entry for today")
     return templates.TemplateResponse(
-        "calm/evening_ritual_form.html", {"request": request, "journal_entry": journal_entry}
+        request, "calm/evening_ritual_form.html", {"journal_entry": journal_entry}
     )
 
 
@@ -257,8 +257,9 @@ async def create_new_task(
     # After creating a new task, we might need to re-evaluate NOW/NEXT/LATER, but for simplicity
     # and given the spec, new tasks go to LATER. Promotion happens on completion/deferral.
     return templates.TemplateResponse(
+        request,
         "calm/partials/later_count.html",
-        {"request": request, "later_tasks_count": len(get_later_tasks(db))},
+        {"later_tasks_count": len(get_later_tasks(db))},
     )
 
 
@@ -287,9 +288,9 @@ async def start_task(
     promote_tasks(db)
 
     return templates.TemplateResponse(
+        request,
         "calm/partials/now_next_later.html",
         {
-            "request": request,
             "now_task": get_now_task(db),
             "next_task": get_next_task(db),
             "later_tasks_count": len(get_later_tasks(db)),
@@ -316,9 +317,9 @@ async def complete_task(
     promote_tasks(db)
 
     return templates.TemplateResponse(
+        request,
         "calm/partials/now_next_later.html",
         {
-            "request": request,
             "now_task": get_now_task(db),
             "next_task": get_next_task(db),
             "later_tasks_count": len(get_later_tasks(db)),
@@ -345,9 +346,9 @@ async def defer_task(
     promote_tasks(db)
 
     return templates.TemplateResponse(
+        request,
         "calm/partials/now_next_later.html",
         {
-            "request": request,
             "now_task": get_now_task(db),
             "next_task": get_next_task(db),
             "later_tasks_count": len(get_later_tasks(db)),
@@ -360,8 +361,7 @@ async def get_later_tasks_list(request: Request, db: Session = Depends(get_db)):
     """Render the expandable list of LATER tasks."""
     later_tasks = get_later_tasks(db)
     return templates.TemplateResponse(
-        "calm/partials/later_tasks_list.html",
-        {"request": request, "later_tasks": later_tasks},
+        request, "calm/partials/later_tasks_list.html", {"later_tasks": later_tasks}
     )
 
 
@@ -404,9 +404,9 @@ async def reorder_tasks(
 
     # Re-render the relevant parts of the UI
     return templates.TemplateResponse(
+        request,
         "calm/partials/now_next_later.html",
         {
-            "request": request,
             "now_task": get_now_task(db),
             "next_task": get_next_task(db),
             "later_tasks_count": len(get_later_tasks(db)),

src/dashboard/routes/tools.py

@@ -40,9 +40,9 @@ async def tools_page(request: Request):
     total_calls = 0
 
     return templates.TemplateResponse(
+        request,
         "tools.html",
         {
-            "request": request,
             "available_tools": available_tools,
             "agent_tools": agent_tools,
             "total_calls": total_calls,

src/infrastructure/claude_quota.py

@@ -1,302 +1,264 @@
-"""Claude API quota tracker and metabolic mode advisor.
+"""
+claude_quota.py — Claude Code / Claude.ai Quota Monitor
 
-Tracks Claude API usage (tokens, cost, calls) in a local SQLite database.
-Provides a metabolic mode recommendation (BURST / ACTIVE / RESTING) based on
-daily spend thresholds so the orchestrator can decide when to use cloud inference
-vs. local Ollama.
+Drop into src/infrastructure/ in the Timmy Time Dashboard repo.
 
-Metabolic protocol (from issue #1074):
-  BURST   — daily spend < burst_threshold    → use Claude freely
-  ACTIVE  — daily spend < active_threshold   → prefer Groq / cheap tier
-  RESTING — daily spend >= active_threshold  → local only, no API calls
+Provides real-time quota visibility and metabolic protocol decisions.
 
-Refs: #1074, #972
+Usage:
+    from infrastructure.claude_quota import QuotaMonitor
+
+    monitor = QuotaMonitor()
+    status = monitor.check()
+    print(status.five_hour_pct)       # 42
+    print(status.five_hour_resets_in) # "2h 15m"
+    print(status.seven_day_pct)       # 29
+    print(status.recommended_tier)    # MetabolicTier.BURST
+
+    # Metabolic protocol: auto-select model based on quota
+    model = monitor.select_model(task_complexity="high")
+    # Returns "claude-sonnet-4-6" if quota allows, else "qwen3:14b"
 """
 
 import json
 import logging
-import sqlite3
-from contextlib import closing
-from dataclasses import dataclass, field
-from datetime import UTC, date, datetime
-from pathlib import Path
-from typing import Literal
-
-from config import settings
+import subprocess
+import urllib.request
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from enum import StrEnum
 
 logger = logging.getLogger(__name__)
 
-# ── Cost table (USD per million tokens, approximate) ─────────────────────────
-_MODEL_COSTS: dict[str, dict[str, float]] = {
-    # haiku aliases
-    "haiku": {"input": 0.25, "output": 1.25},
-    "claude-haiku-4-5": {"input": 0.25, "output": 1.25},
-    "claude-haiku-4-5-20251001": {"input": 0.25, "output": 1.25},
-    # sonnet aliases
-    "sonnet": {"input": 3.00, "output": 15.00},
-    "claude-sonnet-4-6": {"input": 3.00, "output": 15.00},
-    # opus aliases
-    "opus": {"input": 15.00, "output": 75.00},
-    "claude-opus-4-6": {"input": 15.00, "output": 75.00},
-}
-_DEFAULT_COST = {"input": 3.00, "output": 15.00}  # conservative default
 
-MetabolicMode = Literal["BURST", "ACTIVE", "RESTING"]
+class MetabolicTier(StrEnum):
+    """The three-tier metabolic protocol from the Timmy Time architecture."""
 
-DB_PATH = Path(settings.repo_root) / "data" / "claude_quota.db"
-
-# Daily spend thresholds (USD) — tune via env or subclass Settings
-BURST_THRESHOLD: float = 1.00   # < $1/day  → BURST mode, use Claude freely
-ACTIVE_THRESHOLD: float = 5.00  # < $5/day  → ACTIVE mode, prefer cheaper tier
-
-_SCHEMA = """
-CREATE TABLE IF NOT EXISTS claude_calls (
-    id          INTEGER PRIMARY KEY AUTOINCREMENT,
-    ts          TEXT    NOT NULL,
-    model       TEXT    NOT NULL,
-    input_tok   INTEGER NOT NULL DEFAULT 0,
-    output_tok  INTEGER NOT NULL DEFAULT 0,
-    cost_usd    REAL    NOT NULL DEFAULT 0.0,
-    task_label  TEXT    DEFAULT '',
-    metadata    TEXT    DEFAULT '{}'
-);
-CREATE INDEX IF NOT EXISTS idx_cc_ts    ON claude_calls(ts);
-CREATE INDEX IF NOT EXISTS idx_cc_model ON claude_calls(model);
-"""
+    BURST = "burst"  # Cloud API (Claude/Groq) — expensive, best quality
+    ACTIVE = "active"  # Local 14B (Qwen3-14B) — free, good quality
+    RESTING = "resting"  # Local 8B (Qwen3-8B) — free, fast, adequate
 
 
 @dataclass
-class ClaudeCall:
-    """Record of a single Claude API call."""
+class QuotaStatus:
+    """Current Claude quota state."""
 
-    model: str
-    input_tokens: int
-    output_tokens: int
-    task_label: str = ""
-    ts: str = field(default_factory=lambda: datetime.now(UTC).isoformat())
-    metadata: dict = field(default_factory=dict)
+    five_hour_utilization: float  # 0.0 to 1.0
+    five_hour_resets_at: str | None
+    seven_day_utilization: float  # 0.0 to 1.0
+    seven_day_resets_at: str | None
+    raw_response: dict
+    fetched_at: datetime
 
     @property
-    def cost_usd(self) -> float:
-        costs = _MODEL_COSTS.get(self.model, _DEFAULT_COST)
+    def five_hour_pct(self) -> int:
+        return int(self.five_hour_utilization * 100)
+
+    @property
+    def seven_day_pct(self) -> int:
+        return int(self.seven_day_utilization * 100)
+
+    @property
+    def five_hour_resets_in(self) -> str:
+        return _time_remaining(self.five_hour_resets_at)
+
+    @property
+    def seven_day_resets_in(self) -> str:
+        return _time_remaining(self.seven_day_resets_at)
+
+    @property
+    def recommended_tier(self) -> MetabolicTier:
+        """Metabolic protocol: determine which inference tier to use."""
+        # If weekly quota is critical, go full local
+        if self.seven_day_utilization >= 0.80:
+            return MetabolicTier.RESTING
+        # If 5-hour window is critical or past half, use local
+        if self.five_hour_utilization >= 0.50:
+            return MetabolicTier.ACTIVE
+        # Quota healthy — cloud available for high-value tasks
+        return MetabolicTier.BURST
+
+    def summary(self) -> str:
+        """Human-readable status string."""
         return (
-            self.input_tokens * costs["input"]
-            + self.output_tokens * costs["output"]
-        ) / 1_000_000
-
-
-@dataclass
-class QuotaSummary:
-    """Aggregated quota status for a time window."""
-
-    period: str            # "today" | "month"
-    calls: int
-    input_tokens: int
-    output_tokens: int
-    cost_usd: float
-    mode: MetabolicMode
-    burst_threshold: float
-    active_threshold: float
-
-    def as_dict(self) -> dict:
-        return {
-            "period": self.period,
-            "calls": self.calls,
-            "input_tokens": self.input_tokens,
-            "output_tokens": self.output_tokens,
-            "cost_usd": round(self.cost_usd, 4),
-            "mode": self.mode,
-            "burst_threshold": self.burst_threshold,
-            "active_threshold": self.active_threshold,
-        }
-
-
-def _mode_for_cost(daily_cost: float) -> MetabolicMode:
-    if daily_cost < BURST_THRESHOLD:
-        return "BURST"
-    if daily_cost < ACTIVE_THRESHOLD:
-        return "ACTIVE"
-    return "RESTING"
-
-
-class ClaudeQuotaStore:
-    """SQLite-backed store for Claude API usage tracking.
-
-    Thread-safe: creates a new connection per operation.
-    """
-
-    def __init__(self, db_path: Path | None = None) -> None:
-        self._db_path = db_path or DB_PATH
-        self._init_db()
-
-    def _init_db(self) -> None:
-        try:
-            self._db_path.parent.mkdir(parents=True, exist_ok=True)
-            with closing(sqlite3.connect(str(self._db_path))) as conn:
-                conn.execute("PRAGMA journal_mode=WAL")
-                conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
-                conn.executescript(_SCHEMA)
-                conn.commit()
-        except Exception as exc:
-            logger.warning("Failed to initialize claude_quota DB: %s", exc)
-
-    def _connect(self) -> sqlite3.Connection:
-        conn = sqlite3.connect(str(self._db_path))
-        conn.row_factory = sqlite3.Row
-        conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
-        return conn
-
-    def record_call(self, call: ClaudeCall) -> None:
-        """Persist a completed Claude API call."""
-        try:
-            with closing(self._connect()) as conn:
-                conn.execute(
-                    "INSERT INTO claude_calls "
-                    "(ts, model, input_tok, output_tok, cost_usd, task_label, metadata) "
-                    "VALUES (?, ?, ?, ?, ?, ?, ?)",
-                    (
-                        call.ts,
-                        call.model,
-                        call.input_tokens,
-                        call.output_tokens,
-                        call.cost_usd,
-                        call.task_label,
-                        json.dumps(call.metadata),
-                    ),
-                )
-                conn.commit()
-        except Exception as exc:
-            logger.warning("Failed to record Claude call: %s", exc)
-
-    def _aggregate(self, where_clause: str, params: tuple) -> dict:
-        """Return aggregated stats for a WHERE clause."""
-        try:
-            with closing(self._connect()) as conn:
-                row = conn.execute(
-                    f"SELECT COUNT(*) as calls, "
-                    f"COALESCE(SUM(input_tok),0) as input_tok, "
-                    f"COALESCE(SUM(output_tok),0) as output_tok, "
-                    f"COALESCE(SUM(cost_usd),0.0) as cost_usd "
-                    f"FROM claude_calls {where_clause}",
-                    params,
-                ).fetchone()
-                if row:
-                    return dict(row)
-        except Exception as exc:
-            logger.warning("Failed to aggregate Claude quota: %s", exc)
-        return {"calls": 0, "input_tok": 0, "output_tok": 0, "cost_usd": 0.0}
-
-    def today_summary(self) -> QuotaSummary:
-        """Return quota summary for today (UTC)."""
-        today = date.today().isoformat()
-        agg = self._aggregate("WHERE ts >= ?", (today,))
-        return QuotaSummary(
-            period="today",
-            calls=agg["calls"],
-            input_tokens=agg["input_tok"],
-            output_tokens=agg["output_tok"],
-            cost_usd=agg["cost_usd"],
-            mode=_mode_for_cost(agg["cost_usd"]),
-            burst_threshold=BURST_THRESHOLD,
-            active_threshold=ACTIVE_THRESHOLD,
+            f"5h: {self.five_hour_pct}% (resets {self.five_hour_resets_in}) | "
+            f"7d: {self.seven_day_pct}% (resets {self.seven_day_resets_in}) | "
+            f"tier: {self.recommended_tier.value}"
         )
 
-    def month_summary(self) -> QuotaSummary:
-        """Return quota summary for the current calendar month (UTC)."""
-        month_prefix = date.today().strftime("%Y-%m")
-        agg = self._aggregate("WHERE ts >= ?", (month_prefix,))
-        return QuotaSummary(
-            period="month",
-            calls=agg["calls"],
-            input_tokens=agg["input_tok"],
-            output_tokens=agg["output_tok"],
-            cost_usd=agg["cost_usd"],
-            mode=_mode_for_cost(agg["cost_usd"] / 30),  # amortised daily
-            burst_threshold=BURST_THRESHOLD,
-            active_threshold=ACTIVE_THRESHOLD,
-        )
 
-    def current_mode(self) -> MetabolicMode:
-        """Return the current metabolic mode based on today's spend."""
-        return self.today_summary().mode
-
-
-# ── Module-level singleton ────────────────────────────────────────────────────
-_store: ClaudeQuotaStore | None = None
-
-
-def get_quota_store() -> ClaudeQuotaStore:
-    """Return the module-level quota store, creating it on first access."""
-    global _store
-    if _store is None:
-        _store = ClaudeQuotaStore()
-    return _store
-
-
-def record_usage(
-    model: str,
-    input_tokens: int,
-    output_tokens: int,
-    task_label: str = "",
-    metadata: dict | None = None,
-) -> None:
-    """Convenience function to record a Claude API call.
-
-    Silently degrades if the quota DB is unavailable.
+class QuotaMonitor:
     """
-    call = ClaudeCall(
-        model=model,
-        input_tokens=input_tokens,
-        output_tokens=output_tokens,
-        task_label=task_label,
-        metadata=metadata or {},
-    )
-    get_quota_store().record_call(call)
-    logger.debug(
-        "Claude call recorded: model=%s in=%d out=%d cost=$%.4f",
-        model,
-        input_tokens,
-        output_tokens,
-        call.cost_usd,
-    )
+    Monitors Claude Code / Claude.ai quota via the internal OAuth API.
 
-
-def current_mode() -> MetabolicMode:
-    """Return the current metabolic mode.
-
-    BURST   → Claude is cheap today, use freely.
-    ACTIVE  → Approaching daily budget, prefer Groq / cheaper tier.
-    RESTING → Daily limit reached, use local Ollama only.
+    The token is read from macOS Keychain where Claude Code stores it.
+    Falls back gracefully if credentials aren't available (e.g., on Linux VPS).
     """
+
+    API_URL = "https://api.anthropic.com/api/oauth/usage"
+    KEYCHAIN_SERVICE = "Claude Code-credentials"
+    USER_AGENT = "claude-code/2.0.32"
+
+    def __init__(self) -> None:
+        self._token: str | None = None
+        self._last_status: QuotaStatus | None = None
+        self._cache_seconds = 30  # Don't hammer the API
+
+    def _get_token(self) -> str | None:
+        """Extract OAuth token from macOS Keychain."""
+        if self._token:
+            return self._token
+
+        try:
+            result = subprocess.run(
+                ["security", "find-generic-password", "-s", self.KEYCHAIN_SERVICE, "-w"],
+                capture_output=True,
+                text=True,
+                timeout=5,
+            )
+            if result.returncode != 0:
+                logger.warning("Claude Code credentials not found in Keychain")
+                return None
+
+            creds = json.loads(result.stdout.strip())
+            oauth = creds.get("claudeAiOauth", creds)
+            self._token = oauth.get("accessToken")
+            return self._token
+
+        except (
+            json.JSONDecodeError,
+            KeyError,
+            FileNotFoundError,
+            subprocess.TimeoutExpired,
+        ) as exc:
+            logger.warning("Could not read Claude Code credentials: %s", exc)
+            return None
+
+    def check(self, force: bool = False) -> QuotaStatus | None:
+        """
+        Fetch current quota status.
+
+        Returns None if credentials aren't available (graceful degradation).
+        Caches results for 30 seconds to avoid rate limiting the quota API itself.
+        """
+        # Return cached if fresh
+        if not force and self._last_status:
+            age = (datetime.now(UTC) - self._last_status.fetched_at).total_seconds()
+            if age < self._cache_seconds:
+                return self._last_status
+
+        token = self._get_token()
+        if not token:
+            return None
+
+        try:
+            req = urllib.request.Request(
+                self.API_URL,
+                headers={
+                    "Accept": "application/json",
+                    "Content-Type": "application/json",
+                    "User-Agent": self.USER_AGENT,
+                    "Authorization": f"Bearer {token}",
+                    "anthropic-beta": "oauth-2025-04-20",
+                },
+            )
+            with urllib.request.urlopen(req, timeout=10) as resp:
+                data = json.loads(resp.read().decode())
+
+            five_hour = data.get("five_hour") or {}
+            seven_day = data.get("seven_day") or {}
+
+            self._last_status = QuotaStatus(
+                five_hour_utilization=float(five_hour.get("utilization", 0.0)),
+                five_hour_resets_at=five_hour.get("resets_at"),
+                seven_day_utilization=float(seven_day.get("utilization", 0.0)),
+                seven_day_resets_at=seven_day.get("resets_at"),
+                raw_response=data,
+                fetched_at=datetime.now(UTC),
+            )
+            return self._last_status
+
+        except Exception as exc:
+            logger.warning("Failed to fetch quota: %s", exc)
+            return self._last_status  # Return stale data if available
+
+    def select_model(self, task_complexity: str = "medium") -> str:
+        """
+        Metabolic protocol: select the right model based on quota + task complexity.
+
+        Returns an Ollama model tag or "claude-sonnet-4-6" for cloud.
+
+        task_complexity: "low" | "medium" | "high"
+        """
+        status = self.check()
+
+        # No quota info available — assume local only (sovereign default)
+        if status is None:
+            return "qwen3:14b" if task_complexity == "high" else "qwen3:8b"
+
+        tier = status.recommended_tier
+
+        if tier == MetabolicTier.BURST and task_complexity == "high":
+            return "claude-sonnet-4-6"  # Cloud — best quality
+        elif tier == MetabolicTier.BURST and task_complexity == "medium":
+            return "qwen3:14b"  # Save cloud for truly hard tasks
+        elif tier == MetabolicTier.ACTIVE:
+            return "qwen3:14b"  # Local 14B — good enough
+        else:  # RESTING
+            return "qwen3:8b"  # Local 8B — conserve everything
+
+    def should_use_cloud(self, task_value: str = "normal") -> bool:
+        """
+        Simple yes/no: should this task use cloud API?
+
+        task_value: "critical" | "high" | "normal" | "routine"
+        """
+        status = self.check()
+
+        if status is None:
+            return False  # No credentials = local only
+
+        if task_value == "critical":
+            return status.seven_day_utilization < 0.95  # Almost always yes
+        elif task_value == "high":
+            return status.five_hour_utilization < 0.60
+        elif task_value == "normal":
+            return status.five_hour_utilization < 0.30
+        else:  # routine
+            return False  # Never waste cloud on routine
+
+
+def _time_remaining(reset_at: str | None) -> str:
+    """Format time until reset as human-readable string."""
+    if not reset_at or reset_at == "null":
+        return "unknown"
+
     try:
-        return get_quota_store().current_mode()
-    except Exception as exc:
-        logger.warning("Quota mode check failed, defaulting to BURST: %s", exc)
-        return "BURST"
+        reset = datetime.fromisoformat(reset_at.replace("Z", "+00:00"))
+        now = datetime.now(UTC)
+        diff = reset - now
+
+        if diff.total_seconds() <= 0:
+            return "resetting now"
+
+        hours = int(diff.total_seconds() // 3600)
+        mins = int((diff.total_seconds() % 3600) // 60)
+
+        if hours > 0:
+            return f"{hours}h {mins}m"
+        return f"{mins}m"
+
+    except (ValueError, TypeError):
+        return "unknown"
 
 
-def quota_report() -> str:
-    """Return a human-readable quota report for CLI / dashboard display."""
-    try:
-        store = get_quota_store()
-        today = store.today_summary()
-        month = store.month_summary()
+# Module-level singleton
+_quota_monitor: QuotaMonitor | None = None
 
-        lines = [
-            "═══════════════════════════════════════",
-            "  Claude API Quota — Metabolic Report  ",
-            "═══════════════════════════════════════",
-            f"  Today     {today.calls:>6} calls  "
-            f"${today.cost_usd:>7.4f}  [{today.mode}]",
-            f"  This month {month.calls:>5} calls  "
-            f"${month.cost_usd:>7.4f}",
-            "───────────────────────────────────────",
-            f"  BURST  threshold : ${today.burst_threshold:.2f}/day",
-            f"  ACTIVE threshold : ${today.active_threshold:.2f}/day",
-            "───────────────────────────────────────",
-            f"  Current mode     : {today.mode}",
-            "═══════════════════════════════════════",
-        ]
-        return "\n".join(lines)
-    except Exception as exc:
-        return f"Quota report unavailable: {exc}"
+
+def get_quota_monitor() -> QuotaMonitor:
+    """Get or create the quota monitor singleton."""
+    global _quota_monitor
+    if _quota_monitor is None:
+        _quota_monitor = QuotaMonitor()
+    return _quota_monitor

src/infrastructure/events/bus.py

@@ -16,6 +16,8 @@ from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any
 
+from src.config import settings
+
 logger = logging.getLogger(__name__)
 
 
@@ -102,7 +104,7 @@ class EventBus:
         self._persistence_db_path.parent.mkdir(parents=True, exist_ok=True)
         with closing(sqlite3.connect(str(self._persistence_db_path))) as conn:
             conn.execute("PRAGMA journal_mode=WAL")
-            conn.execute("PRAGMA busy_timeout=5000")
+            conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
             conn.executescript(_EVENTS_SCHEMA)
             conn.commit()
 
@@ -114,7 +116,7 @@ class EventBus:
             return
         with closing(sqlite3.connect(str(self._persistence_db_path))) as conn:
             conn.row_factory = sqlite3.Row
-            conn.execute("PRAGMA busy_timeout=5000")
+            conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
             yield conn
 
     def _persist_event(self, event: Event) -> None:

src/infrastructure/models/registry.py

@@ -18,6 +18,8 @@ from datetime import UTC, datetime
 from enum import StrEnum
 from pathlib import Path
 
+from src.config import settings
+
 logger = logging.getLogger(__name__)
 
 DB_PATH = Path("data/swarm.db")
@@ -68,7 +70,7 @@ def _get_conn() -> Generator[sqlite3.Connection, None, None]:
     with closing(sqlite3.connect(str(DB_PATH))) as conn:
         conn.row_factory = sqlite3.Row
         conn.execute("PRAGMA journal_mode=WAL")
-        conn.execute("PRAGMA busy_timeout=5000")
+        conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
         conn.execute("""
             CREATE TABLE IF NOT EXISTS custom_models (
                 name            TEXT PRIMARY KEY,

src/infrastructure/router/cascade.py

@@ -32,6 +32,15 @@ except ImportError:
 
 logger = logging.getLogger(__name__)
 
+# Quota monitor — optional, degrades gracefully if unavailable
+try:
+    from infrastructure.claude_quota import QuotaMonitor, get_quota_monitor
+
+    _quota_monitor: "QuotaMonitor | None" = get_quota_monitor()
+except Exception as _exc:  # pragma: no cover
+    logger.debug("Quota monitor not available: %s", _exc)
+    _quota_monitor = None
+
 
 class ProviderStatus(Enum):
     """Health status of a provider."""
@@ -301,6 +310,22 @@ class CascadeRouter:
                 logger.debug("Ollama provider check error: %s", exc)
                 return False
 
+        elif provider.type == "vllm_mlx":
+            # Check if local vllm-mlx server is running (OpenAI-compatible)
+            if requests is None:
+                return True
+            try:
+                base_url = provider.base_url or provider.url or "http://localhost:8000"
+                # Strip /v1 suffix — health endpoint is at the 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-mlx 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 != ""
@@ -457,6 +482,33 @@ class CascadeRouter:
 
         raise RuntimeError("; ".join(errors))
 
+    def _quota_allows_cloud(self, provider: Provider) -> bool:
+        """Check quota before routing to a cloud provider.
+
+        Uses the metabolic protocol via select_model(): cloud calls are only
+        allowed when the quota monitor recommends a cloud model (BURST tier).
+        Returns True (allow cloud) if quota monitor is unavailable or returns None.
+        """
+        if _quota_monitor is None:
+            return True
+        try:
+            suggested = _quota_monitor.select_model("high")
+            # Cloud is allowed only when select_model recommends the cloud model
+            allows = suggested == "claude-sonnet-4-6"
+            if not allows:
+                status = _quota_monitor.check()
+                tier = status.recommended_tier.value if status else "unknown"
+                logger.info(
+                    "Metabolic protocol: %s tier — downshifting %s to local (%s)",
+                    tier,
+                    provider.name,
+                    suggested,
+                )
+            return allows
+        except Exception as exc:
+            logger.warning("Quota check failed, allowing cloud: %s", exc)
+            return True
+
     def _is_provider_available(self, provider: Provider) -> bool:
         """Check if a provider should be tried (enabled + circuit breaker)."""
         if not provider.enabled:
@@ -510,6 +562,15 @@ class CascadeRouter:
             if not self._is_provider_available(provider):
                 continue
 
+            # 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,
+                    )
+                    continue
+
             selected_model, is_fallback_model = self._select_model(provider, model, content_type)
 
             try:
@@ -582,6 +643,14 @@ class CascadeRouter:
                 temperature=temperature,
                 max_tokens=max_tokens,
             )
+        elif provider.type == "vllm_mlx":
+            result = await self._call_vllm_mlx(
+                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}")
 
@@ -778,6 +847,48 @@ class CascadeRouter:
             "model": response.model,
         }
 
+    async def _call_vllm_mlx(
+        self,
+        provider: Provider,
+        messages: list[dict],
+        model: str,
+        temperature: float,
+        max_tokens: int | None,
+    ) -> dict:
+        """Call vllm-mlx via its OpenAI-compatible API.
+
+        vllm-mlx exposes the same /v1/chat/completions endpoint as OpenAI,
+        so we reuse the OpenAI client pointed at the local server.
+        No API key is required for local deployments.
+        """
+        import openai
+
+        base_url = provider.base_url or provider.url or "http://localhost:8000"
+        # 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/spark/eidos.py

@@ -22,6 +22,8 @@ from dataclasses import dataclass
 from datetime import UTC, datetime
 from pathlib import Path
 
+from src.config import settings
+
 logger = logging.getLogger(__name__)
 
 DB_PATH = Path("data/spark.db")
@@ -47,7 +49,7 @@ def _get_conn() -> Generator[sqlite3.Connection, None, None]:
     with closing(sqlite3.connect(str(DB_PATH))) as conn:
         conn.row_factory = sqlite3.Row
         conn.execute("PRAGMA journal_mode=WAL")
-        conn.execute("PRAGMA busy_timeout=5000")
+        conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
         conn.execute("""
             CREATE TABLE IF NOT EXISTS spark_predictions (
                 id               TEXT PRIMARY KEY,

src/spark/memory.py

@@ -19,6 +19,8 @@ from dataclasses import dataclass
 from datetime import UTC, datetime
 from pathlib import Path
 
+from src.config import settings
+
 logger = logging.getLogger(__name__)
 
 DB_PATH = Path("data/spark.db")
@@ -63,7 +65,7 @@ def _get_conn() -> Generator[sqlite3.Connection, None, None]:
     with closing(sqlite3.connect(str(DB_PATH))) as conn:
         conn.row_factory = sqlite3.Row
         conn.execute("PRAGMA journal_mode=WAL")
-        conn.execute("PRAGMA busy_timeout=5000")
+        conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
         conn.execute("""
             CREATE TABLE IF NOT EXISTS spark_events (
                 id          TEXT PRIMARY KEY,

src/timmy/kimi_delegation.py

@@ -0,0 +1,488 @@
+"""Kimi delegation for heavy research via Gitea labels.
+
+When research exceeds local + Groq capacity, Timmy delegates to Kimi by:
+1. Filling a research template with full context
+2. Creating a Gitea issue labeled `kimi-ready`
+3. Monitoring for Kimi's completion (issue closed + artifact committed)
+4. Indexing Kimi's artifact into semantic memory
+5. Extracting action items and creating follow-up issues
+
+Delegation flow:
+  Timmy detects capacity exceeded
+  → Fills template with context
+  → Creates `kimi-ready` Gitea issue
+  → Kimi picks up, executes, commits artifact, closes issue
+  → Timmy indexes artifact + creates follow-ups
+"""
+
+import asyncio
+import logging
+import re
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# Label applied to issues that Kimi should pick up
+KIMI_READY_LABEL = "kimi-ready"
+
+# Label colour for the kimi-ready label (dark teal)
+KIMI_LABEL_COLOR = "#006b75"
+
+# Keywords that suggest a task exceeds local capacity
+_HEAVY_RESEARCH_KEYWORDS = frozenset(
+    {
+        "comprehensive",
+        "exhaustive",
+        "systematic review",
+        "literature review",
+        "benchmark",
+        "comparative analysis",
+        "large-scale",
+        "survey",
+        "meta-analysis",
+        "deep research",
+        "extensive",
+    }
+)
+
+# Minimum word count that hints at a heavy task
+_HEAVY_WORD_THRESHOLD = 50
+
+
+def exceeds_local_capacity(task_description: str) -> bool:
+    """Heuristic: does this research task exceed local + Groq capacity?
+
+    Returns True when the task description signals heavy or broad research
+    that benefits from Kimi's 262K context and long-running processing.
+
+    Args:
+        task_description: Free-text description of the research task.
+
+    Returns:
+        True if the task should be delegated to Kimi.
+    """
+    lower = task_description.lower()
+    word_count = len(task_description.split())
+
+    has_heavy_keyword = any(kw in lower for kw in _HEAVY_RESEARCH_KEYWORDS)
+    is_long_task = word_count >= _HEAVY_WORD_THRESHOLD
+
+    return has_heavy_keyword or is_long_task
+
+
+def _build_research_template(
+    task: str,
+    context: str,
+    question: str,
+    priority: str = "normal",
+) -> str:
+    """Fill the standard Kimi research template with task context.
+
+    Args:
+        task: Short title for the research task.
+        context: Background information and relevant project context.
+        question: The specific research question to answer.
+        priority: Task priority — "low", "normal", or "high".
+
+    Returns:
+        Markdown-formatted issue body ready for Gitea.
+    """
+    return f"""\
+## Research Request
+
+**Priority:** {priority}
+
+### Research Question
+
+{question}
+
+### Background / Context
+
+{context}
+
+### Scope
+
+Please produce a thorough, well-structured research report covering:
+
+- Direct answer to the research question above
+- Supporting evidence and sources where applicable
+- Trade-offs, limitations, or caveats
+- Concrete recommendations or next steps
+
+### Deliverables
+
+Commit your findings as a markdown artifact (e.g. `memory/research/{_slugify(task)}.md`)
+and close this issue when complete.
+
+### Task
+
+{task}
+
+---
+*Delegated by Timmy via Kimi delegation pipeline. Label: `{KIMI_READY_LABEL}`*
+"""
+
+
+def _slugify(text: str) -> str:
+    """Convert text to a safe filename slug."""
+    slug = re.sub(r"[^\w\s-]", "", text.lower())
+    slug = re.sub(r"[\s_]+", "-", slug)
+    return slug[:60].strip("-")
+
+
+async def _get_or_create_label(
+    client: Any,
+    base_url: str,
+    headers: dict[str, str],
+    repo: str,
+) -> int | None:
+    """Ensure the `kimi-ready` label exists; return its ID or None on error.
+
+    Args:
+        client: httpx.AsyncClient instance.
+        base_url: Gitea API base URL.
+        headers: Auth headers.
+        repo: owner/repo string.
+
+    Returns:
+        Label ID, or None if the operation failed.
+    """
+    labels_url = f"{base_url}/repos/{repo}/labels"
+
+    # Check for existing label
+    try:
+        resp = await client.get(labels_url, headers=headers)
+        if resp.status_code == 200:
+            for label in resp.json():
+                if label.get("name") == KIMI_READY_LABEL:
+                    return label["id"]
+    except Exception as exc:
+        logger.warning("Failed to list Gitea labels: %s", exc)
+        return None
+
+    # Create the label
+    try:
+        resp = await client.post(
+            labels_url,
+            headers=headers,
+            json={"name": KIMI_READY_LABEL, "color": KIMI_LABEL_COLOR},
+        )
+        if resp.status_code in (200, 201):
+            return resp.json().get("id")
+        logger.warning("Label creation returned %s: %s", resp.status_code, resp.text[:200])
+    except Exception as exc:
+        logger.warning("Failed to create Gitea label: %s", exc)
+
+    return None
+
+
+async def create_kimi_research_issue(
+    task: str,
+    context: str,
+    question: str,
+    priority: str = "normal",
+) -> dict[str, Any]:
+    """Create a Gitea issue labeled `kimi-ready` for Kimi to pick up.
+
+    Args:
+        task: Short title for the research task (used as issue title).
+        context: Background information and project context.
+        question: The specific research question.
+        priority: Task priority — "low", "normal", or "high".
+
+    Returns:
+        Dict with `success`, `issue_number`, `issue_url`, and `error` keys.
+    """
+    try:
+        import httpx
+
+        from config import settings
+    except ImportError as exc:
+        return {"success": False, "error": f"Missing dependency: {exc}"}
+
+    if not settings.gitea_enabled or not settings.gitea_token:
+        return {
+            "success": False,
+            "error": "Gitea integration not configured (no token or disabled).",
+        }
+
+    base_url = f"{settings.gitea_url}/api/v1"
+    repo = settings.gitea_repo
+    headers = {
+        "Authorization": f"token {settings.gitea_token}",
+        "Content-Type": "application/json",
+    }
+
+    try:
+        async with httpx.AsyncClient(timeout=15) as client:
+            label_id = await _get_or_create_label(client, base_url, headers, repo)
+
+            body = _build_research_template(task, context, question, priority)
+            issue_payload: dict[str, Any] = {"title": task, "body": body}
+            if label_id is not None:
+                issue_payload["labels"] = [label_id]
+
+            resp = await client.post(
+                f"{base_url}/repos/{repo}/issues",
+                headers=headers,
+                json=issue_payload,
+            )
+
+        if resp.status_code in (200, 201):
+            data = resp.json()
+            number = data.get("number")
+            url = data.get("html_url", "")
+            logger.info("Created kimi-ready issue #%s: %s", number, task[:60])
+            return {
+                "success": True,
+                "issue_number": number,
+                "issue_url": url,
+                "error": None,
+            }
+
+        logger.warning("Issue creation failed (%s): %s", resp.status_code, resp.text[:200])
+        return {
+            "success": False,
+            "error": f"Gitea API error {resp.status_code}: {resp.text[:200]}",
+        }
+
+    except Exception as exc:
+        logger.warning("create_kimi_research_issue failed: %s", exc)
+        return {"success": False, "error": str(exc)}
+
+
+async def poll_kimi_issue(
+    issue_number: int,
+    poll_interval: int = 60,
+    max_wait: int = 3600,
+) -> dict[str, Any]:
+    """Poll a Gitea issue until it is closed (Kimi completed) or timeout.
+
+    Args:
+        issue_number: The Gitea issue number to watch.
+        poll_interval: Seconds between polls. Default 60.
+        max_wait: Maximum total seconds to wait. Default 3600 (1 hour).
+
+    Returns:
+        Dict with `completed` bool, `state`, `body`, and `error` keys.
+    """
+    try:
+        import httpx
+
+        from config import settings
+    except ImportError as exc:
+        return {"completed": False, "error": f"Missing dependency: {exc}"}
+
+    if not settings.gitea_enabled or not settings.gitea_token:
+        return {"completed": False, "error": "Gitea not configured."}
+
+    base_url = f"{settings.gitea_url}/api/v1"
+    repo = settings.gitea_repo
+    headers = {"Authorization": f"token {settings.gitea_token}"}
+    issue_url = f"{base_url}/repos/{repo}/issues/{issue_number}"
+
+    elapsed = 0
+    while elapsed < max_wait:
+        try:
+            async with httpx.AsyncClient(timeout=10) as client:
+                resp = await client.get(issue_url, headers=headers)
+
+            if resp.status_code == 200:
+                data = resp.json()
+                state = data.get("state", "open")
+                if state == "closed":
+                    logger.info("Kimi completed issue #%s", issue_number)
+                    return {
+                        "completed": True,
+                        "state": state,
+                        "body": data.get("body", ""),
+                        "error": None,
+                    }
+            else:
+                logger.warning("Poll issue #%s returned %s", issue_number, resp.status_code)
+
+        except Exception as exc:
+            logger.warning("Poll error for issue #%s: %s", issue_number, exc)
+
+        await asyncio.sleep(poll_interval)
+        elapsed += poll_interval
+
+    return {
+        "completed": False,
+        "state": "timeout",
+        "body": "",
+        "error": f"Timed out after {max_wait}s waiting for issue #{issue_number}",
+    }
+
+
+def _extract_action_items(text: str) -> list[str]:
+    """Extract action items from markdown text.
+
+    Looks for lines that start with checklist markers, numbered items,
+    or explicit "Action:" / "TODO:" prefixes.
+
+    Args:
+        text: Markdown text from Kimi's artifact.
+
+    Returns:
+        List of action item strings (deduplicated, whitespace-stripped).
+    """
+    items: list[str] = []
+    patterns = [
+        re.compile(r"^[-*]\s+\[ \]\s+(.+)", re.MULTILINE),  # - [ ] checkbox
+        re.compile(r"^\d+\.\s+(.+)", re.MULTILINE),  # 1. numbered list
+        re.compile(r"^(?:Action|TODO|Next step):\s*(.+)", re.MULTILINE | re.IGNORECASE),
+    ]
+    seen: set[str] = set()
+    for pat in patterns:
+        for m in pat.finditer(text):
+            item = m.group(1).strip()
+            if item and item not in seen:
+                items.append(item)
+                seen.add(item)
+    return items
+
+
+async def index_kimi_artifact(
+    issue_number: int,
+    title: str,
+    artifact_content: str,
+) -> dict[str, Any]:
+    """Index Kimi's research artifact into Timmy's semantic memory.
+
+    Args:
+        issue_number: Source Gitea issue number (used as task_id).
+        title: Human-readable title for the memory entry.
+        artifact_content: The research artifact text to index.
+
+    Returns:
+        Dict with `success` bool and `memory_id` or `error`.
+    """
+    if not artifact_content.strip():
+        return {"success": False, "error": "Empty artifact — nothing to index."}
+
+    try:
+        import asyncio
+
+        from timmy.memory_system import store_memory
+
+        # store_memory is synchronous — wrap in thread to avoid blocking event loop
+        entry = await asyncio.to_thread(
+            store_memory,
+            content=artifact_content,
+            source="kimi",
+            context_type="document",
+            task_id=str(issue_number),
+            metadata={"issue_number": issue_number, "title": title},
+        )
+        logger.info("Indexed Kimi artifact for issue #%s (id=%s)", issue_number, entry.id)
+        return {"success": True, "memory_id": entry.id}
+
+    except Exception as exc:
+        logger.warning("Failed to index Kimi artifact for issue #%s: %s", issue_number, exc)
+        return {"success": False, "error": str(exc)}
+
+
+async def extract_and_create_followups(
+    artifact_content: str,
+    source_issue_number: int,
+) -> dict[str, Any]:
+    """Extract action items from artifact and create follow-up Gitea issues.
+
+    Args:
+        artifact_content: Text of Kimi's research artifact.
+        source_issue_number: Issue number that produced the artifact (for cross-links).
+
+    Returns:
+        Dict with `success`, `created` (list of issue numbers), and `error`.
+    """
+    items = _extract_action_items(artifact_content)
+    if not items:
+        logger.info("No action items found in artifact for issue #%s", source_issue_number)
+        return {"success": True, "created": [], "error": None}
+
+    try:
+        import httpx
+
+        from config import settings
+    except ImportError as exc:
+        return {"success": False, "created": [], "error": str(exc)}
+
+    if not settings.gitea_enabled or not settings.gitea_token:
+        return {
+            "success": False,
+            "created": [],
+            "error": "Gitea not configured.",
+        }
+
+    base_url = f"{settings.gitea_url}/api/v1"
+    repo = settings.gitea_repo
+    headers = {
+        "Authorization": f"token {settings.gitea_token}",
+        "Content-Type": "application/json",
+    }
+    created: list[int] = []
+
+    for item in items:
+        body = (
+            f"Follow-up from Kimi research artifact in #{source_issue_number}.\n\n"
+            f"**Action item:** {item}"
+        )
+        try:
+            async with httpx.AsyncClient(timeout=10) as client:
+                resp = await client.post(
+                    f"{base_url}/repos/{repo}/issues",
+                    headers=headers,
+                    json={"title": item[:120], "body": body},
+                )
+            if resp.status_code in (200, 201):
+                num = resp.json().get("number")
+                if num:
+                    created.append(num)
+                    logger.info(
+                        "Created follow-up issue #%s from kimi artifact #%s",
+                        num,
+                        source_issue_number,
+                    )
+            else:
+                logger.warning(
+                    "Follow-up issue creation returned %s for item: %s",
+                    resp.status_code,
+                    item[:60],
+                )
+        except Exception as exc:
+            logger.warning("Failed to create follow-up for item '%s': %s", item[:60], exc)
+
+    return {"success": True, "created": created, "error": None}
+
+
+async def delegate_research_to_kimi(
+    task: str,
+    context: str,
+    question: str,
+    priority: str = "normal",
+) -> dict[str, Any]:
+    """Top-level entry point: delegate a heavy research task to Kimi.
+
+    Creates the `kimi-ready` Gitea issue and returns immediately.
+    Monitoring, artifact indexing, and follow-up creation happen
+    separately via `poll_kimi_issue`, `index_kimi_artifact`, and
+    `extract_and_create_followups`.
+
+    Args:
+        task: Short title (becomes the issue title).
+        context: Background / project context.
+        question: The specific research question Kimi should answer.
+        priority: "low", "normal", or "high".
+
+    Returns:
+        Dict with `success`, `issue_number`, `issue_url`, and `error`.
+    """
+    if not task.strip() or not question.strip():
+        return {
+            "success": False,
+            "error": "Both `task` and `question` are required.",
+        }
+
+    logger.info("Delegating research to Kimi: %s", task[:80])
+    return await create_kimi_research_issue(task, context, question, priority)

src/timmy/paperclip.py

@@ -0,0 +1,175 @@
+"""Paperclip integration for Timmy.
+
+This module provides a client for the Paperclip API, and a poller for
+running research tasks.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from dataclasses import dataclass
+
+import httpx
+
+from config import settings
+from timmy.research_tools import get_llm_client, google_web_search
+from timmy.research_triage import triage_research_report
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class PaperclipTask:
+    """A task from the Paperclip API."""
+
+    id: str
+    kind: str
+    context: dict
+
+
+class PaperclipClient:
+    """A client for the Paperclip API."""
+
+    def __init__(self) -> None:
+        self.base_url = settings.paperclip_url
+        self.api_key = settings.paperclip_api_key
+        self.agent_id = settings.paperclip_agent_id
+        self.company_id = settings.paperclip_company_id
+        self.timeout = settings.paperclip_timeout
+
+    async def get_tasks(self) -> list[PaperclipTask]:
+        """Get a list of tasks from the Paperclip API."""
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            resp = await client.get(
+                f"{self.base_url}/api/tasks",
+                headers={"Authorization": f"Bearer {self.api_key}"},
+                params={
+                    "agent_id": self.agent_id,
+                    "company_id": self.company_id,
+                    "status": "queued",
+                },
+            )
+            resp.raise_for_status()
+            tasks = resp.json()
+            return [
+                PaperclipTask(id=t["id"], kind=t["kind"], context=t["context"])
+                for t in tasks
+            ]
+
+    async def update_task_status(
+        self, task_id: str, status: str, result: str | None = None
+    ) -> None:
+        """Update the status of a task."""
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            await client.patch(
+                f"{self.base_url}/api/tasks/{task_id}",
+                headers={"Authorization": f"Bearer {self.api_key}"},
+                json={"status": status, "result": result},
+            )
+
+
+class ResearchOrchestrator:
+    """Orchestrates research tasks."""
+
+    async def get_gitea_issue(self, issue_number: int) -> dict:
+        """Get a Gitea issue by its number."""
+        owner, repo = settings.gitea_repo.split("/", 1)
+        api_url = f"{settings.gitea_url}/api/v1/repos/{owner}/{repo}/issues/{issue_number}"
+        async with httpx.AsyncClient(timeout=15) as client:
+            resp = await client.get(
+                api_url,
+                headers={"Authorization": f"token {settings.gitea_token}"},
+            )
+            resp.raise_for_status()
+            return resp.json()
+
+    async def post_gitea_comment(self, issue_number: int, comment: str) -> None:
+        """Post a comment to a Gitea issue."""
+        owner, repo = settings.gitea_repo.split("/", 1)
+        api_url = f"{settings.gitea_url}/api/v1/repos/{owner}/{repo}/issues/{issue_number}/comments"
+        async with httpx.AsyncClient(timeout=15) as client:
+            await client.post(
+                api_url,
+                headers={"Authorization": f"token {settings.gitea_token}"},
+                json={"body": comment},
+            )
+
+    async def run_research_pipeline(self, issue_title: str) -> str:
+        """Run the research pipeline."""
+        search_results = await google_web_search(issue_title)
+        
+        llm_client = get_llm_client()
+        response = await llm_client.completion(
+            f"Summarize the following search results and generate a research report:\\n\\n{search_results}",
+            max_tokens=2048,
+        )
+        return response.text
+
+    async def run(self, context: dict) -> str:
+        """Run a research task."""
+        issue_number = context.get("issue_number")
+        if not issue_number:
+            return "Missing issue_number in task context"
+
+        issue = await self.get_gitea_issue(issue_number)
+
+        report = await self.run_research_pipeline(issue["title"])
+
+        triage_results = await triage_research_report(report, source_issue=issue_number)
+
+        comment = f"Research complete for issue #{issue_number}.\\n\\n"
+        if triage_results:
+            comment += "Created the following issues:\\n"
+            for result in triage_results:
+                if result["gitea_issue"]:
+                    comment += f"- #{result['gitea_issue']['number']}: {result['action_item'].title}\\n"
+        else:
+            comment += "No new issues were created.\\n"
+
+        await self.post_gitea_comment(issue_number, comment)
+
+        return f"Research complete for issue #{issue_number}"
+
+
+class PaperclipPoller:
+    """Polls the Paperclip API for new tasks."""
+
+    def __init__(self) -> None:
+        self.client = PaperclipClient()
+        self.orchestrator = ResearchOrchestrator()
+        self.poll_interval = settings.paperclip_poll_interval
+
+    async def poll(self) -> None:
+        """Poll the Paperclip API for new tasks."""
+        if self.poll_interval == 0:
+            return
+
+        while True:
+            try:
+                tasks = await self.client.get_tasks()
+                for task in tasks:
+                    if task.kind == "research":
+                        await self.run_research_task(task)
+            except httpx.HTTPError as exc:
+                logger.warning("Error polling Paperclip: %s", exc)
+
+            await asyncio.sleep(self.poll_interval)
+
+    async def run_research_task(self, task: PaperclipTask) -> None:
+        """Run a research task."""
+        await self.client.update_task_status(task.id, "running")
+        try:
+            result = await self.orchestrator.run(task.context)
+            await self.client.update_task_status(task.id, "completed", result)
+        except Exception as exc:
+            logger.error("Error running research task: %s", exc, exc_info=True)
+            await self.client.update_task_status(task.id, "failed", str(exc))
+
+
+async def start_paperclip_poller() -> None:
+    """Start the Paperclip poller."""
+    if settings.paperclip_enabled:
+        poller = PaperclipPoller()
+        asyncio.create_task(poller.poll())
+

src/timmy/research_tools.py

@@ -0,0 +1,41 @@
+"""Tools for the research pipeline."""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any
+
+from serpapi import GoogleSearch
+
+logger = logging.getLogger(__name__)
+
+
+async def google_web_search(query: str) -> str:
+    """Perform a Google search and return the results."""
+    if "SERPAPI_API_KEY" not in os.environ:
+        logger.warning("SERPAPI_API_KEY not set, skipping web search")
+        return ""
+    params = {
+        "q": query,
+        "api_key": os.environ["SERPAPI_API_KEY"],
+    }
+    search = GoogleSearch(params)
+    results = search.get_dict()
+    return str(results)
+
+
+def get_llm_client() -> Any:
+    """Get an LLM client."""
+    # This is a placeholder. In a real application, this would return
+    # a client for an LLM service like OpenAI, Anthropic, or a local
+    # model.
+    class MockLLMClient:
+        async def completion(self, prompt: str, max_tokens: int) -> Any:
+            class MockCompletion:
+                def __init__(self, text: str) -> None:
+                    self.text = text
+
+            return MockCompletion(f"This is a summary of the search results for '{prompt}'.")
+
+    return MockLLMClient()

src/timmy/research_triage.py

@@ -54,9 +54,7 @@ class ActionItem:
                 parts.append(f"- {url}")
 
         if source_issue:
-            parts.append(
-                f"\n### Origin\nExtracted from research in #{source_issue}"
-            )
+            parts.append(f"\n### Origin\nExtracted from research in #{source_issue}")
 
         parts.append("\n---\n*Auto-triaged from research findings by Timmy*")
         return "\n".join(parts)
@@ -123,7 +121,7 @@ def _validate_action_item(raw_item: dict[str, Any]) -> ActionItem | None:
 
     labels = raw_item.get("labels", [])
     if isinstance(labels, str):
-        labels = [l.strip() for l in labels.split(",") if l.strip()]
+        labels = [lbl.strip() for lbl in labels.split(",") if lbl.strip()]
     if not isinstance(labels, list):
         labels = []
 
@@ -303,7 +301,7 @@ async def _resolve_label_ids(
             if resp.status_code != 200:
                 return []
 
-            existing = {l["name"]: l["id"] for l in resp.json()}
+            existing = {lbl["name"]: lbl["id"] for lbl in resp.json()}
             label_ids = []
 
             for name in label_names:

src/timmy_serve/cli.py

@@ -14,7 +14,9 @@ app = typer.Typer(help="Timmy Serve — sovereign AI agent API")
 def start(
     port: int = typer.Option(8402, "--port", "-p", help="Port for the serve API"),
     host: str = typer.Option("0.0.0.0", "--host", "-h", help="Host to bind to"),
-    price: int = typer.Option(None, "--price", help="Price per request in sats (default: from config)"),
+    price: int = typer.Option(
+        None, "--price", help="Price per request in sats (default: from config)"
+    ),
     dry_run: bool = typer.Option(False, "--dry-run", help="Print config and exit (for testing)"),
 ):
     """Start Timmy in serve mode."""

tests/dashboard/test_health.py

@@ -24,7 +24,6 @@ from dashboard.routes.health import (
     _generate_recommendations,
 )
 
-
 # ---------------------------------------------------------------------------
 # Pydantic models
 # ---------------------------------------------------------------------------
@@ -118,7 +117,9 @@ class TestGenerateRecommendations:
 
     def test_unavailable_service(self):
         deps = [
-            DependencyStatus(name="Ollama AI", status="unavailable", sovereignty_score=10, details={})
+            DependencyStatus(
+                name="Ollama AI", status="unavailable", sovereignty_score=10, details={}
+            )
         ]
         recs = _generate_recommendations(deps)
         assert any("Ollama AI is unavailable" in r for r in recs)
@@ -137,9 +138,7 @@ class TestGenerateRecommendations:
 
     def test_degraded_non_lightning(self):
         """Degraded non-Lightning dep produces no specific recommendation."""
-        deps = [
-            DependencyStatus(name="Redis", status="degraded", sovereignty_score=5, details={})
-        ]
+        deps = [DependencyStatus(name="Redis", status="degraded", sovereignty_score=5, details={})]
         recs = _generate_recommendations(deps)
         assert recs == ["System operating optimally - all dependencies healthy"]
 
@@ -379,7 +378,9 @@ class TestHealthEndpoint:
         assert response.status_code == 200
 
     def test_ok_when_ollama_up(self, client):
-        with patch("dashboard.routes.health.check_ollama", new_callable=AsyncMock, return_value=True):
+        with patch(
+            "dashboard.routes.health.check_ollama", new_callable=AsyncMock, return_value=True
+        ):
             data = client.get("/health").json()
 
         assert data["status"] == "ok"
@@ -415,7 +416,9 @@ class TestHealthStatusPanel:
         assert "text/html" in response.headers["content-type"]
 
     def test_shows_up_when_ollama_healthy(self, client):
-        with patch("dashboard.routes.health.check_ollama", new_callable=AsyncMock, return_value=True):
+        with patch(
+            "dashboard.routes.health.check_ollama", new_callable=AsyncMock, return_value=True
+        ):
             text = client.get("/health/status").text
 
         assert "UP" in text

tests/infrastructure/test_claude_quota.py

@@ -1,139 +1,267 @@
-"""Tests for the Claude quota tracker and metabolic mode advisor.
+"""Tests for Claude Quota Monitor and Metabolic Protocol."""
 
-Refs: #1074
-"""
-
-import pytest
+from datetime import UTC, datetime, timedelta
+from unittest.mock import patch
 
 from infrastructure.claude_quota import (
-    ACTIVE_THRESHOLD,
-    BURST_THRESHOLD,
-    ClaudeCall,
-    ClaudeQuotaStore,
-    MetabolicMode,
-    _mode_for_cost,
-    current_mode,
-    quota_report,
-    record_usage,
+    MetabolicTier,
+    QuotaMonitor,
+    QuotaStatus,
+    _time_remaining,
+    get_quota_monitor,
 )
 
 
-@pytest.fixture
-def store(tmp_path):
-    """Fresh quota store backed by a temp DB."""
-    return ClaudeQuotaStore(db_path=tmp_path / "test_quota.db")
+def _make_status(five_hour: float = 0.0, seven_day: float = 0.0) -> QuotaStatus:
+    """Helper: build a QuotaStatus with given utilization values."""
+    return QuotaStatus(
+        five_hour_utilization=five_hour,
+        five_hour_resets_at=None,
+        seven_day_utilization=seven_day,
+        seven_day_resets_at=None,
+        raw_response={},
+        fetched_at=datetime.now(UTC),
+    )
 
 
-# ── Unit: cost calculation ────────────────────────────────────────────────────
+class TestMetabolicTierThresholds:
+    """Test the three-tier metabolic protocol thresholds."""
+
+    def test_burst_when_five_hour_below_50pct(self):
+        status = _make_status(five_hour=0.49, seven_day=0.10)
+        assert status.recommended_tier == MetabolicTier.BURST
+
+    def test_burst_at_zero_utilization(self):
+        status = _make_status(five_hour=0.0, seven_day=0.0)
+        assert status.recommended_tier == MetabolicTier.BURST
+
+    def test_active_when_five_hour_at_50pct(self):
+        status = _make_status(five_hour=0.50, seven_day=0.10)
+        assert status.recommended_tier == MetabolicTier.ACTIVE
+
+    def test_active_when_five_hour_between_50_and_80pct(self):
+        status = _make_status(five_hour=0.79, seven_day=0.10)
+        assert status.recommended_tier == MetabolicTier.ACTIVE
+
+    def test_active_when_five_hour_at_80pct(self):
+        # five_hour >= 0.80 but seven_day < 0.80 → ACTIVE (not RESTING)
+        status = _make_status(five_hour=0.80, seven_day=0.50)
+        assert status.recommended_tier == MetabolicTier.ACTIVE
+
+    def test_resting_when_seven_day_at_80pct(self):
+        status = _make_status(five_hour=0.30, seven_day=0.80)
+        assert status.recommended_tier == MetabolicTier.RESTING
+
+    def test_resting_when_seven_day_above_80pct(self):
+        status = _make_status(five_hour=0.10, seven_day=0.95)
+        assert status.recommended_tier == MetabolicTier.RESTING
+
+    def test_resting_when_both_critical(self):
+        status = _make_status(five_hour=0.90, seven_day=0.90)
+        assert status.recommended_tier == MetabolicTier.RESTING
+
+    def test_seven_day_takes_precedence_over_five_hour(self):
+        # Weekly quota critical overrides whatever five-hour says
+        status = _make_status(five_hour=0.10, seven_day=0.85)
+        assert status.recommended_tier == MetabolicTier.RESTING
 
 
-class TestClaudeCallCost:
-    def test_haiku_cost(self):
-        call = ClaudeCall(model="haiku", input_tokens=1_000_000, output_tokens=0)
-        assert call.cost_usd == pytest.approx(0.25)
+class TestQuotaStatusProperties:
+    """Test QuotaStatus computed properties."""
 
-    def test_sonnet_output_cost(self):
-        call = ClaudeCall(model="sonnet", input_tokens=0, output_tokens=1_000_000)
-        assert call.cost_usd == pytest.approx(15.00)
+    def test_five_hour_pct(self):
+        status = _make_status(five_hour=0.42)
+        assert status.five_hour_pct == 42
 
-    def test_opus_combined_cost(self):
-        call = ClaudeCall(model="opus", input_tokens=100_000, output_tokens=50_000)
-        # input: 100k * 15/1M = 1.50, output: 50k * 75/1M = 3.75 → 5.25
-        assert call.cost_usd == pytest.approx(5.25)
+    def test_seven_day_pct(self):
+        status = _make_status(seven_day=0.75)
+        assert status.seven_day_pct == 75
 
-    def test_unknown_model_uses_default(self):
-        call = ClaudeCall(model="unknown-model-xyz", input_tokens=1_000_000, output_tokens=0)
-        assert call.cost_usd == pytest.approx(3.00)  # default input cost
+    def test_summary_contains_tier(self):
+        status = _make_status(five_hour=0.20, seven_day=0.10)
+        summary = status.summary()
+        assert "burst" in summary
+        assert "20%" in summary
 
-    def test_zero_tokens_zero_cost(self):
-        call = ClaudeCall(model="haiku", input_tokens=0, output_tokens=0)
-        assert call.cost_usd == 0.0
+    def test_five_hour_resets_in_unknown_when_none(self):
+        status = _make_status()
+        assert status.five_hour_resets_in == "unknown"
+
+    def test_seven_day_resets_in_unknown_when_none(self):
+        status = _make_status()
+        assert status.seven_day_resets_in == "unknown"
 
 
-# ── Unit: metabolic mode thresholds ──────────────────────────────────────────
+class TestTimeRemaining:
+    """Test _time_remaining helper."""
+
+    def test_none_returns_unknown(self):
+        assert _time_remaining(None) == "unknown"
+
+    def test_empty_string_returns_unknown(self):
+        assert _time_remaining("") == "unknown"
+
+    def test_past_time_returns_resetting_now(self):
+        past = (datetime.now(UTC) - timedelta(hours=1)).isoformat()
+        assert _time_remaining(past) == "resetting now"
+
+    def test_future_time_hours_and_minutes(self):
+        future = (datetime.now(UTC) + timedelta(hours=2, minutes=15)).isoformat()
+        result = _time_remaining(future)
+        assert "2h" in result
+        # Minutes may vary ±1 due to test execution time
+        assert "m" in result
+
+    def test_future_time_minutes_only(self):
+        future = (datetime.now(UTC) + timedelta(minutes=45)).isoformat()
+        result = _time_remaining(future)
+        assert "h" not in result
+        # Minutes may vary ±1 due to test execution time
+        assert "m" in result
+
+    def test_z_suffix_handled(self):
+        future = (datetime.now(UTC) + timedelta(hours=1)).strftime("%Y-%m-%dT%H:%M:%SZ")
+        result = _time_remaining(future)
+        assert result != "unknown"
 
 
-class TestMetabolicMode:
-    def test_under_burst_threshold(self):
-        assert _mode_for_cost(0.0) == "BURST"
-        assert _mode_for_cost(BURST_THRESHOLD - 0.01) == "BURST"
+class TestQuotaMonitorSelectModel:
+    """Test select_model metabolic routing."""
 
-    def test_at_burst_threshold_is_active(self):
-        assert _mode_for_cost(BURST_THRESHOLD) == "ACTIVE"
+    def test_no_quota_high_complexity_returns_14b(self):
+        monitor = QuotaMonitor()
+        monitor._get_token = lambda: None
+        assert monitor.select_model("high") == "qwen3:14b"
 
-    def test_between_thresholds(self):
-        mid = (BURST_THRESHOLD + ACTIVE_THRESHOLD) / 2
-        assert _mode_for_cost(mid) == "ACTIVE"
+    def test_no_quota_low_complexity_returns_8b(self):
+        monitor = QuotaMonitor()
+        monitor._get_token = lambda: None
+        assert monitor.select_model("low") == "qwen3:8b"
 
-    def test_at_active_threshold_is_resting(self):
-        assert _mode_for_cost(ACTIVE_THRESHOLD) == "RESTING"
+    def test_burst_tier_high_complexity_returns_cloud(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.10, seven_day=0.10)
+        monitor._cache_seconds = 9999
+        result = monitor.select_model("high")
+        assert result == "claude-sonnet-4-6"
 
-    def test_over_active_threshold(self):
-        assert _mode_for_cost(ACTIVE_THRESHOLD + 10) == "RESTING"
+    def test_burst_tier_medium_complexity_returns_14b(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.10, seven_day=0.10)
+        monitor._cache_seconds = 9999
+        result = monitor.select_model("medium")
+        assert result == "qwen3:14b"
+
+    def test_active_tier_returns_14b(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.65, seven_day=0.10)
+        monitor._cache_seconds = 9999
+        result = monitor.select_model("high")
+        assert result == "qwen3:14b"
+
+    def test_resting_tier_returns_8b(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.10, seven_day=0.85)
+        monitor._cache_seconds = 9999
+        result = monitor.select_model("high")
+        assert result == "qwen3:8b"
 
 
-# ── Store: record and query ───────────────────────────────────────────────────
+class TestQuotaMonitorShouldUseCloud:
+    """Test should_use_cloud gate."""
+
+    def test_no_credentials_always_false(self):
+        monitor = QuotaMonitor()
+        monitor._get_token = lambda: None
+        assert monitor.should_use_cloud("critical") is False
+
+    def test_critical_task_allowed_when_under_95pct(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.10, seven_day=0.94)
+        monitor._cache_seconds = 9999
+        assert monitor.should_use_cloud("critical") is True
+
+    def test_critical_task_blocked_when_over_95pct(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.10, seven_day=0.96)
+        monitor._cache_seconds = 9999
+        assert monitor.should_use_cloud("critical") is False
+
+    def test_high_task_allowed_under_60pct(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.59, seven_day=0.10)
+        monitor._cache_seconds = 9999
+        assert monitor.should_use_cloud("high") is True
+
+    def test_high_task_blocked_at_60pct(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.60, seven_day=0.10)
+        monitor._cache_seconds = 9999
+        assert monitor.should_use_cloud("high") is False
+
+    def test_normal_task_allowed_under_30pct(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.29, seven_day=0.10)
+        monitor._cache_seconds = 9999
+        assert monitor.should_use_cloud("normal") is True
+
+    def test_normal_task_blocked_at_30pct(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.30, seven_day=0.10)
+        monitor._cache_seconds = 9999
+        assert monitor.should_use_cloud("normal") is False
+
+    def test_routine_task_always_false(self):
+        monitor = QuotaMonitor()
+        monitor._last_status = _make_status(five_hour=0.0, seven_day=0.0)
+        monitor._cache_seconds = 9999
+        assert monitor.should_use_cloud("routine") is False
 
 
-class TestClaudeQuotaStore:
-    def test_record_call(self, store):
-        call = ClaudeCall(model="haiku", input_tokens=1000, output_tokens=500)
-        store.record_call(call)
-        summary = store.today_summary()
-        assert summary.calls == 1
-        assert summary.input_tokens == 1000
-        assert summary.output_tokens == 500
-        assert summary.cost_usd > 0
+class TestQuotaMonitorCaching:
+    """Test 30-second TTL cache."""
 
-    def test_today_summary_empty_db(self, store):
-        summary = store.today_summary()
-        assert summary.calls == 0
-        assert summary.cost_usd == 0.0
-        assert summary.mode == "BURST"
+    def test_cached_result_returned_within_ttl(self):
+        monitor = QuotaMonitor()
+        fresh_status = _make_status(five_hour=0.10)
+        monitor._last_status = fresh_status
+        monitor._cache_seconds = 30
 
-    def test_month_summary_aggregates_multiple_calls(self, store):
-        for _ in range(5):
-            store.record_call(ClaudeCall(model="haiku", input_tokens=100, output_tokens=50))
-        month = store.month_summary()
-        assert month.calls == 5
-        assert month.input_tokens == 500
-        assert month.output_tokens == 250
+        # Should NOT re-fetch — returns cached
+        with patch.object(monitor, "_get_token", return_value="tok") as mock_tok:
+            result = monitor.check()
+            mock_tok.assert_not_called()
 
-    def test_current_mode_burst_when_empty(self, store):
-        assert store.current_mode() == "BURST"
+        assert result is fresh_status
 
-    def test_current_mode_resting_when_expensive(self, store):
-        # Record enough usage to push past ACTIVE_THRESHOLD
-        # ACTIVE_THRESHOLD = 5.00, opus input = 15/1M
-        # Need >5.00: 5.00/15 * 1M ≈ 333_334 input tokens
-        store.record_call(
-            ClaudeCall(model="opus", input_tokens=400_000, output_tokens=0)
+    def test_stale_cache_triggers_fetch(self):
+        monitor = QuotaMonitor()
+        old_time = datetime.now(UTC) - timedelta(seconds=60)
+        stale_status = QuotaStatus(
+            five_hour_utilization=0.10,
+            five_hour_resets_at=None,
+            seven_day_utilization=0.10,
+            seven_day_resets_at=None,
+            raw_response={},
+            fetched_at=old_time,
         )
-        mode = store.current_mode()
-        assert mode == "RESTING"
+        monitor._last_status = stale_status
 
-    def test_summary_as_dict(self, store):
-        summary = store.today_summary()
-        d = summary.as_dict()
-        assert "period" in d
-        assert "calls" in d
-        assert "cost_usd" in d
-        assert "mode" in d
+        # Token unavailable → returns None (triggers re-fetch path)
+        with patch.object(monitor, "_get_token", return_value=None):
+            result = monitor.check()
+
+        assert result is None  # No credentials after cache miss
 
 
-# ── Convenience functions ─────────────────────────────────────────────────────
+class TestGetQuotaMonitorSingleton:
+    """Test module-level singleton."""
 
+    def test_returns_same_instance(self):
+        m1 = get_quota_monitor()
+        m2 = get_quota_monitor()
+        assert m1 is m2
 
-class TestConvenienceFunctions:
-    def test_record_usage_does_not_raise(self):
-        # Uses module-level store; should not raise even if DB path issues
-        record_usage(model="haiku", input_tokens=10, output_tokens=5, task_label="test")
-
-    def test_current_mode_returns_valid_mode(self):
-        mode = current_mode()
-        assert mode in ("BURST", "ACTIVE", "RESTING")
-
-    def test_quota_report_returns_string(self):
-        report = quota_report()
-        assert isinstance(report, str)
-        assert "BURST" in report or "ACTIVE" in report or "RESTING" in report
+    def test_returns_quota_monitor_instance(self):
+        monitor = get_quota_monitor()
+        assert isinstance(monitor, QuotaMonitor)

tests/infrastructure/test_db_pool.py

@@ -6,8 +6,8 @@ import time
 from pathlib import Path
 
 import pytest
-
-from infrastructure.db_pool import ConnectionPool
+from src.config import settings
+from src.infrastructure.db_pool import ConnectionPool
 
 
 class TestConnectionPoolInit:
@@ -330,9 +330,9 @@ class TestPragmaApplication:
         """busy_timeout pragma set on a pooled connection persists."""
         pool = ConnectionPool(tmp_path / "test.db")
         conn = pool.get_connection()
-        conn.execute("PRAGMA busy_timeout=5000")
+        conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
         timeout = conn.execute("PRAGMA busy_timeout").fetchone()[0]
-        assert timeout == 5000
+        assert timeout == settings.db_busy_timeout_ms
         pool.close_connection()
 
     def test_pragmas_apply_per_connection(self, tmp_path):

tests/infrastructure/test_router_cascade.py

@@ -489,6 +489,306 @@ class TestProviderAvailabilityCheck:
 
         assert router._check_provider_available(provider) is False
 
+    def test_check_vllm_mlx_without_requests(self):
+        """Test vllm-mlx returns True when requests not available (fallback)."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+
+        provider = Provider(
+            name="vllm-mlx-local",
+            type="vllm_mlx",
+            enabled=True,
+            priority=2,
+            base_url="http://localhost:8000/v1",
+        )
+
+        import infrastructure.router.cascade as cascade_module
+
+        old_requests = cascade_module.requests
+        cascade_module.requests = None
+        try:
+            assert router._check_provider_available(provider) is True
+        finally:
+            cascade_module.requests = old_requests
+
+    def test_check_vllm_mlx_server_healthy(self):
+        """Test vllm-mlx when health check succeeds."""
+        from unittest.mock import MagicMock, patch
+
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+
+        provider = Provider(
+            name="vllm-mlx-local",
+            type="vllm_mlx",
+            enabled=True,
+            priority=2,
+            base_url="http://localhost:8000/v1",
+        )
+
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+
+        with patch("infrastructure.router.cascade.requests") as mock_requests:
+            mock_requests.get.return_value = mock_response
+            result = router._check_provider_available(provider)
+
+        assert result is True
+        mock_requests.get.assert_called_once_with("http://localhost:8000/health", timeout=5)
+
+    def test_check_vllm_mlx_server_down(self):
+        """Test vllm-mlx when server is not running."""
+        from unittest.mock import patch
+
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+
+        provider = Provider(
+            name="vllm-mlx-local",
+            type="vllm_mlx",
+            enabled=True,
+            priority=2,
+            base_url="http://localhost:8000/v1",
+        )
+
+        with patch("infrastructure.router.cascade.requests") as mock_requests:
+            mock_requests.get.side_effect = ConnectionRefusedError("Connection refused")
+            result = router._check_provider_available(provider)
+
+        assert result is False
+
+    def test_check_vllm_mlx_default_url(self):
+        """Test vllm-mlx uses default localhost:8000 when no URL configured."""
+        from unittest.mock import MagicMock, patch
+
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+
+        provider = Provider(
+            name="vllm-mlx-local",
+            type="vllm_mlx",
+            enabled=True,
+            priority=2,
+        )
+
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+
+        with patch("infrastructure.router.cascade.requests") as mock_requests:
+            mock_requests.get.return_value = mock_response
+            router._check_provider_available(provider)
+
+        mock_requests.get.assert_called_once_with("http://localhost:8000/health", timeout=5)
+
+
+@pytest.mark.asyncio
+class TestVllmMlxProvider:
+    """Test vllm-mlx provider integration."""
+
+    async def test_complete_with_vllm_mlx(self):
+        """Test successful completion via vllm-mlx."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+
+        provider = Provider(
+            name="vllm-mlx-local",
+            type="vllm_mlx",
+            enabled=True,
+            priority=2,
+            base_url="http://localhost:8000/v1",
+            models=[{"name": "Qwen/Qwen2.5-14B-Instruct-MLX", "default": True}],
+        )
+        router.providers = [provider]
+
+        with patch.object(router, "_call_vllm_mlx") as mock_call:
+            mock_call.return_value = {
+                "content": "MLX response",
+                "model": "Qwen/Qwen2.5-14B-Instruct-MLX",
+            }
+
+            result = await router.complete(
+                messages=[{"role": "user", "content": "Hi"}],
+            )
+
+        assert result["content"] == "MLX response"
+        assert result["provider"] == "vllm-mlx-local"
+        assert result["model"] == "Qwen/Qwen2.5-14B-Instruct-MLX"
+
+    async def test_vllm_mlx_base_url_normalization(self):
+        """Test _call_vllm_mlx appends /v1 when missing."""
+        from unittest.mock import AsyncMock, MagicMock, patch
+
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+
+        provider = Provider(
+            name="vllm-mlx-local",
+            type="vllm_mlx",
+            enabled=True,
+            priority=2,
+            base_url="http://localhost:8000",  # No /v1
+            models=[{"name": "qwen-mlx", "default": True}],
+        )
+
+        mock_choice = MagicMock()
+        mock_choice.message.content = "hello"
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.model = "qwen-mlx"
+
+        async def fake_create(**kwargs):
+            return mock_response
+
+        with patch("openai.AsyncOpenAI") as mock_openai_cls:
+            mock_client = MagicMock()
+            mock_client.chat.completions.create = AsyncMock(side_effect=fake_create)
+            mock_openai_cls.return_value = mock_client
+
+            await router._call_vllm_mlx(
+                provider=provider,
+                messages=[{"role": "user", "content": "hi"}],
+                model="qwen-mlx",
+                temperature=0.7,
+                max_tokens=None,
+            )
+
+            call_kwargs = mock_openai_cls.call_args
+            base_url_used = call_kwargs.kwargs.get("base_url") or call_kwargs[1].get("base_url")
+            assert base_url_used.endswith("/v1")
+
+    async def test_vllm_mlx_is_local_not_cloud(self):
+        """Confirm vllm_mlx is not subject to metabolic protocol cloud skip."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+
+        provider = Provider(
+            name="vllm-mlx-local",
+            type="vllm_mlx",
+            enabled=True,
+            priority=2,
+            base_url="http://localhost:8000/v1",
+            models=[{"name": "qwen-mlx", "default": True}],
+        )
+        router.providers = [provider]
+
+        # Quota monitor downshifts to local (ACTIVE tier) — vllm_mlx should still be tried
+        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+            mock_qm.select_model.return_value = "qwen3:14b"
+            mock_qm.check.return_value = None
+
+            with patch.object(router, "_call_vllm_mlx") as mock_call:
+                mock_call.return_value = {
+                    "content": "Local MLX response",
+                    "model": "qwen-mlx",
+                }
+                result = await router.complete(
+                    messages=[{"role": "user", "content": "hi"}],
+                )
+
+        assert result["content"] == "Local MLX response"
+
+
+class TestMetabolicProtocol:
+    """Test metabolic protocol: cloud providers skip when quota is ACTIVE/RESTING."""
+
+    def _make_anthropic_provider(self) -> "Provider":
+        return Provider(
+            name="anthropic-primary",
+            type="anthropic",
+            enabled=True,
+            priority=1,
+            api_key="test-key",
+            models=[{"name": "claude-sonnet-4-6", "default": True}],
+        )
+
+    async def test_cloud_provider_allowed_in_burst_tier(self):
+        """BURST tier (quota healthy): cloud provider is tried."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.providers = [self._make_anthropic_provider()]
+
+        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+            # select_model returns cloud model → BURST tier
+            mock_qm.select_model.return_value = "claude-sonnet-4-6"
+            mock_qm.check.return_value = None
+
+            with patch.object(router, "_call_anthropic") as mock_call:
+                mock_call.return_value = {"content": "Cloud response", "model": "claude-sonnet-4-6"}
+                result = await router.complete(
+                    messages=[{"role": "user", "content": "hard question"}],
+                )
+
+        mock_call.assert_called_once()
+        assert result["content"] == "Cloud response"
+
+    async def test_cloud_provider_skipped_in_active_tier(self):
+        """ACTIVE tier (5-hour >= 50%): cloud provider is skipped."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.providers = [self._make_anthropic_provider()]
+
+        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+            # select_model returns local 14B → ACTIVE tier
+            mock_qm.select_model.return_value = "qwen3:14b"
+            mock_qm.check.return_value = None
+
+            with patch.object(router, "_call_anthropic") as mock_call:
+                with pytest.raises(RuntimeError, match="All providers failed"):
+                    await router.complete(
+                        messages=[{"role": "user", "content": "question"}],
+                    )
+
+        mock_call.assert_not_called()
+
+    async def test_cloud_provider_skipped_in_resting_tier(self):
+        """RESTING tier (7-day >= 80%): cloud provider is skipped."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.providers = [self._make_anthropic_provider()]
+
+        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+            # select_model returns local 8B → RESTING tier
+            mock_qm.select_model.return_value = "qwen3:8b"
+            mock_qm.check.return_value = None
+
+            with patch.object(router, "_call_anthropic") as mock_call:
+                with pytest.raises(RuntimeError, match="All providers failed"):
+                    await router.complete(
+                        messages=[{"role": "user", "content": "simple question"}],
+                    )
+
+        mock_call.assert_not_called()
+
+    async def test_local_provider_always_tried_regardless_of_quota(self):
+        """Local (ollama/vllm_mlx) providers bypass the metabolic protocol."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        provider = Provider(
+            name="ollama-local",
+            type="ollama",
+            enabled=True,
+            priority=1,
+            url="http://localhost:11434",
+            models=[{"name": "qwen3:14b", "default": True}],
+        )
+        router.providers = [provider]
+
+        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+            mock_qm.select_model.return_value = "qwen3:8b"  # RESTING tier
+
+            with patch.object(router, "_call_ollama") as mock_call:
+                mock_call.return_value = {"content": "Local response", "model": "qwen3:14b"}
+                result = await router.complete(
+                    messages=[{"role": "user", "content": "hi"}],
+                )
+
+        mock_call.assert_called_once()
+        assert result["content"] == "Local response"
+
+    async def test_no_quota_monitor_allows_cloud(self):
+        """When quota monitor is None (unavailable), cloud providers are allowed."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.providers = [self._make_anthropic_provider()]
+
+        with patch("infrastructure.router.cascade._quota_monitor", None):
+            with patch.object(router, "_call_anthropic") as mock_call:
+                mock_call.return_value = {"content": "Cloud response", "model": "claude-sonnet-4-6"}
+                result = await router.complete(
+                    messages=[{"role": "user", "content": "question"}],
+                )
+
+        mock_call.assert_called_once()
+        assert result["content"] == "Cloud response"
+
 
 class TestCascadeRouterReload:
     """Test hot-reload of providers.yaml."""

tests/scripts/test_export_trajectories.py

@@ -0,0 +1,285 @@
+"""Unit tests for scripts/export_trajectories.py.
+
+Tests trajectory conversion logic — no I/O, no Ollama, no mlx.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+import scripts.export_trajectories as et
+
+# ── Fixtures ──────────────────────────────────────────────────────────────────
+
+
+@pytest.fixture()
+def simple_session(tmp_path: Path) -> Path:
+    """Write a minimal session JSONL file and return the logs dir."""
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    entries = [
+        {"type": "message", "role": "user", "content": "What time is it?", "timestamp": "2026-03-01T10:00:00"},
+        {"type": "message", "role": "timmy", "content": "It is 10:00 AM.", "timestamp": "2026-03-01T10:00:01"},
+        {"type": "message", "role": "user", "content": "Thanks!", "timestamp": "2026-03-01T10:00:05"},
+        {"type": "message", "role": "timmy", "content": "You're welcome!", "timestamp": "2026-03-01T10:00:06"},
+    ]
+    session_file = logs_dir / "session_2026-03-01.jsonl"
+    session_file.write_text("\n".join(json.dumps(e) for e in entries) + "\n")
+    return logs_dir
+
+
+@pytest.fixture()
+def tool_call_session(tmp_path: Path) -> Path:
+    """Write a session JSONL with tool calls."""
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    entries = [
+        {"type": "message", "role": "user", "content": "Read CLAUDE.md", "timestamp": "2026-03-01T10:00:00"},
+        {
+            "type": "tool_call",
+            "tool": "read_file",
+            "args": {"path": "CLAUDE.md"},
+            "result": "# CLAUDE.md content here",
+            "timestamp": "2026-03-01T10:00:01",
+        },
+        {"type": "message", "role": "timmy", "content": "Here is the content.", "timestamp": "2026-03-01T10:00:02"},
+    ]
+    session_file = logs_dir / "session_2026-03-01.jsonl"
+    session_file.write_text("\n".join(json.dumps(e) for e in entries) + "\n")
+    return logs_dir
+
+
+# ── _load_entries ─────────────────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_load_entries_returns_all(simple_session: Path) -> None:
+    entries = et._load_entries(simple_session)
+    assert len(entries) == 4
+
+
+@pytest.mark.unit
+def test_load_entries_skips_malformed(tmp_path: Path) -> None:
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    session = logs_dir / "session_2026-03-01.jsonl"
+    session.write_text(
+        '{"type": "message", "role": "user", "content": "hi"}\n'
+        "NOT_JSON\n"
+        '{"type": "message", "role": "timmy", "content": "hello"}\n'
+    )
+    entries = et._load_entries(logs_dir)
+    assert len(entries) == 2  # malformed line skipped
+
+
+@pytest.mark.unit
+def test_load_entries_empty_dir(tmp_path: Path) -> None:
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    entries = et._load_entries(logs_dir)
+    assert entries == []
+
+
+@pytest.mark.unit
+def test_load_entries_multiple_files(tmp_path: Path) -> None:
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    for day in ("2026-03-01", "2026-03-02"):
+        entry = {"type": "message", "role": "user", "content": f"day {day}"}
+        (logs_dir / f"session_{day}.jsonl").write_text(json.dumps(entry) + "\n")
+    entries = et._load_entries(logs_dir)
+    assert len(entries) == 2
+
+
+# ── _format_tool_call ─────────────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_format_tool_call_structure() -> None:
+    entry = {
+        "type": "tool_call",
+        "tool": "read_file",
+        "args": {"path": "/tmp/foo.txt"},
+        "result": "file contents",
+    }
+    result = et._format_tool_call(entry)
+    assert result.startswith("<tool_call>")
+    assert result.endswith("</tool_call>")
+    payload = json.loads(result.split("\n")[1])
+    assert payload["name"] == "read_file"
+    assert payload["arguments"]["path"] == "/tmp/foo.txt"
+
+
+@pytest.mark.unit
+def test_format_tool_call_missing_tool() -> None:
+    entry = {"type": "tool_call", "args": {}}
+    result = et._format_tool_call(entry)
+    assert "unknown" in result
+
+
+# ── _group_into_turns ─────────────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_group_basic_conversation() -> None:
+    entries = [
+        {"type": "message", "role": "user", "content": "hello"},
+        {"type": "message", "role": "timmy", "content": "hi there"},
+        {"type": "message", "role": "user", "content": "bye"},
+        {"type": "message", "role": "timmy", "content": "goodbye"},
+    ]
+    turns = et._group_into_turns(entries)
+    assert len(turns) == 2
+    assert turns[0]["user"] == "hello"
+    assert turns[0]["assistant"] == "hi there"
+    assert turns[1]["user"] == "bye"
+    assert turns[1]["assistant"] == "goodbye"
+
+
+@pytest.mark.unit
+def test_group_with_tool_call() -> None:
+    entries = [
+        {"type": "message", "role": "user", "content": "check the file"},
+        {"type": "tool_call", "tool": "read_file", "args": {"path": "x"}, "result": "content"},
+        {"type": "message", "role": "timmy", "content": "Done."},
+    ]
+    turns = et._group_into_turns(entries)
+    assert len(turns) == 1
+    assert "<tool_call>" in turns[0]["assistant"]
+    assert "Done." in turns[0]["assistant"]
+
+
+@pytest.mark.unit
+def test_group_skips_user_without_response() -> None:
+    """User message with no timmy response should not create a turn."""
+    entries = [
+        {"type": "message", "role": "user", "content": "hello"},
+        # No timmy response
+        {"type": "message", "role": "user", "content": "are you there?"},
+        {"type": "message", "role": "timmy", "content": "Yes!"},
+    ]
+    turns = et._group_into_turns(entries)
+    assert len(turns) == 1
+    assert turns[0]["user"] == "are you there?"
+
+
+@pytest.mark.unit
+def test_group_ignores_errors_and_decisions() -> None:
+    entries = [
+        {"type": "message", "role": "user", "content": "hello"},
+        {"type": "error", "error": "something failed"},
+        {"type": "decision", "decision": "retry"},
+        {"type": "message", "role": "timmy", "content": "Got it."},
+    ]
+    turns = et._group_into_turns(entries)
+    assert len(turns) == 1
+    assert "error" not in turns[0]["assistant"]
+    assert "retry" not in turns[0]["assistant"]
+
+
+@pytest.mark.unit
+def test_group_empty_entries() -> None:
+    assert et._group_into_turns([]) == []
+
+
+# ── turns_to_training_examples ────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_training_examples_structure() -> None:
+    turns = [{"user": "hello", "assistant": "hi there, how can I help?"}]
+    examples = et.turns_to_training_examples(turns)
+    assert len(examples) == 1
+    msgs = examples[0]["messages"]
+    assert msgs[0]["role"] == "system"
+    assert msgs[1]["role"] == "user"
+    assert msgs[1]["content"] == "hello"
+    assert msgs[2]["role"] == "assistant"
+    assert msgs[2]["content"] == "hi there, how can I help?"
+
+
+@pytest.mark.unit
+def test_training_examples_filters_short_responses() -> None:
+    turns = [
+        {"user": "hello", "assistant": "ok"},  # too short
+        {"user": "hello", "assistant": "This is a longer response that passes."},
+    ]
+    examples = et.turns_to_training_examples(turns, min_assistant_len=10)
+    assert len(examples) == 1
+    assert examples[0]["messages"][2]["content"] == "This is a longer response that passes."
+
+
+@pytest.mark.unit
+def test_training_examples_filters_empty_user() -> None:
+    turns = [{"user": "", "assistant": "some response here"}]
+    examples = et.turns_to_training_examples(turns)
+    assert len(examples) == 0
+
+
+@pytest.mark.unit
+def test_training_examples_uses_custom_system_prompt() -> None:
+    turns = [{"user": "hi", "assistant": "hello there!"}]
+    examples = et.turns_to_training_examples(turns, system_prompt="Custom prompt.")
+    assert examples[0]["messages"][0]["content"] == "Custom prompt."
+
+
+# ── export_training_data (integration-style, uses tmp_path) ──────────────────
+
+
+@pytest.mark.unit
+def test_export_training_data_writes_jsonl(simple_session: Path, tmp_path: Path) -> None:
+    output = tmp_path / "train.jsonl"
+    count = et.export_training_data(logs_dir=simple_session, output_path=output)
+    assert count == 2
+    assert output.exists()
+    lines = [
+        json.loads(line) for line in output.read_text().splitlines() if line.strip()
+    ]
+    assert len(lines) == 2
+    for line in lines:
+        assert "messages" in line
+        roles = [m["role"] for m in line["messages"]]
+        assert roles == ["system", "user", "assistant"]
+
+
+@pytest.mark.unit
+def test_export_training_data_with_tool_calls(tool_call_session: Path, tmp_path: Path) -> None:
+    output = tmp_path / "train.jsonl"
+    count = et.export_training_data(logs_dir=tool_call_session, output_path=output)
+    assert count == 1
+    line = json.loads(output.read_text().strip())
+    assistant_content = line["messages"][2]["content"]
+    assert "<tool_call>" in assistant_content
+    assert "read_file" in assistant_content
+
+
+@pytest.mark.unit
+def test_export_training_data_returns_zero_for_empty_logs(tmp_path: Path) -> None:
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    output = tmp_path / "train.jsonl"
+    count = et.export_training_data(logs_dir=logs_dir, output_path=output)
+    assert count == 0
+    assert not output.exists()
+
+
+# ── CLI ───────────────────────────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_cli_missing_logs_dir(tmp_path: Path) -> None:
+    rc = et.main(["--logs-dir", str(tmp_path / "nonexistent"), "--output", str(tmp_path / "out.jsonl")])
+    assert rc == 1
+
+
+@pytest.mark.unit
+def test_cli_exports_and_returns_zero(simple_session: Path, tmp_path: Path) -> None:
+    output = tmp_path / "out.jsonl"
+    rc = et.main([
+        "--logs-dir", str(simple_session),
+        "--output", str(output),
+    ])
+    assert rc == 0
+    assert output.exists()

tests/timmy/test_mcp_bridge.py

@@ -175,9 +175,7 @@ async def test_bridge_run_simple_response():
         bridge = MCPBridge(include_gitea=False, include_shell=False)
 
     mock_resp = MagicMock()
-    mock_resp.json.return_value = {
-        "message": {"role": "assistant", "content": "Hello!"}
-    }
+    mock_resp.json.return_value = {"message": {"role": "assistant", "content": "Hello!"}}
     mock_resp.raise_for_status = MagicMock()
 
     mock_client = AsyncMock()
@@ -238,9 +236,7 @@ async def test_bridge_run_with_tool_call():
 
     # Round 2: model returns final text
     final_resp = MagicMock()
-    final_resp.json.return_value = {
-        "message": {"role": "assistant", "content": "Done with tools!"}
-    }
+    final_resp.json.return_value = {"message": {"role": "assistant", "content": "Done with tools!"}}
     final_resp.raise_for_status = MagicMock()
 
     mock_client = AsyncMock()
@@ -276,17 +272,13 @@ async def test_bridge_run_unknown_tool():
         "message": {
             "role": "assistant",
             "content": "",
-            "tool_calls": [
-                {"function": {"name": "nonexistent", "arguments": {}}}
-            ],
+            "tool_calls": [{"function": {"name": "nonexistent", "arguments": {}}}],
         }
     }
     tool_call_resp.raise_for_status = MagicMock()
 
     final_resp = MagicMock()
-    final_resp.json.return_value = {
-        "message": {"role": "assistant", "content": "OK"}
-    }
+    final_resp.json.return_value = {"message": {"role": "assistant", "content": "OK"}}
     final_resp.raise_for_status = MagicMock()
 
     mock_client = AsyncMock()
@@ -332,9 +324,7 @@ async def test_bridge_run_max_rounds():
         "message": {
             "role": "assistant",
             "content": "",
-            "tool_calls": [
-                {"function": {"name": "loop_tool", "arguments": {}}}
-            ],
+            "tool_calls": [{"function": {"name": "loop_tool", "arguments": {}}}],
         }
     }
     tool_call_resp.raise_for_status = MagicMock()
@@ -365,9 +355,7 @@ async def test_bridge_run_connection_error():
         bridge = MCPBridge(include_gitea=False, include_shell=False)
 
     mock_client = AsyncMock()
-    mock_client.post = AsyncMock(
-        side_effect=httpx.ConnectError("Connection refused")
-    )
+    mock_client.post = AsyncMock(side_effect=httpx.ConnectError("Connection refused"))
     mock_client.aclose = AsyncMock()
 
     bridge._client = mock_client

tests/timmy/test_research_triage.py

@@ -9,7 +9,6 @@ import pytest
 from timmy.research_triage import (
     ActionItem,
     _parse_llm_response,
-    _resolve_label_ids,
     _validate_action_item,
     create_gitea_issue,
     extract_action_items,
@@ -250,7 +249,9 @@ class TestCreateGiteaIssue:
 
         with (
             patch("timmy.research_triage.settings") as mock_settings,
-            patch("timmy.research_triage._resolve_label_ids", new_callable=AsyncMock, return_value=[1]),
+            patch(
+                "timmy.research_triage._resolve_label_ids", new_callable=AsyncMock, return_value=[1]
+            ),
             patch("timmy.research_triage.httpx.AsyncClient") as mock_cls,
         ):
             mock_settings.gitea_enabled = True
@@ -284,7 +285,9 @@ class TestCreateGiteaIssue:
 
         with (
             patch("timmy.research_triage.settings") as mock_settings,
-            patch("timmy.research_triage._resolve_label_ids", new_callable=AsyncMock, return_value=[]),
+            patch(
+                "timmy.research_triage._resolve_label_ids", new_callable=AsyncMock, return_value=[]
+            ),
             patch("timmy.research_triage.httpx.AsyncClient") as mock_cls,
         ):
             mock_settings.gitea_enabled = True
@@ -331,7 +334,9 @@ class TestTriageResearchReport:
 
         with (
             patch("timmy.research_triage.settings") as mock_settings,
-            patch("timmy.research_triage._resolve_label_ids", new_callable=AsyncMock, return_value=[]),
+            patch(
+                "timmy.research_triage._resolve_label_ids", new_callable=AsyncMock, return_value=[]
+            ),
             patch("timmy.research_triage.httpx.AsyncClient") as mock_cls,
         ):
             mock_settings.gitea_enabled = True

tests/unit/test_kimi_delegation.py

@@ -0,0 +1,460 @@
+"""Unit tests for timmy.kimi_delegation — Kimi research delegation via Gitea labels."""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from timmy.kimi_delegation import (
+    KIMI_LABEL_COLOR,
+    KIMI_READY_LABEL,
+    _build_research_template,
+    _extract_action_items,
+    _slugify,
+    delegate_research_to_kimi,
+    exceeds_local_capacity,
+)
+
+# ── Constants ─────────────────────────────────────────────────────────────────
+
+
+def test_kimi_ready_label():
+    assert KIMI_READY_LABEL == "kimi-ready"
+
+
+def test_kimi_label_color_is_hex():
+    assert KIMI_LABEL_COLOR.startswith("#")
+    assert len(KIMI_LABEL_COLOR) == 7
+
+
+# ── exceeds_local_capacity ────────────────────────────────────────────────────
+
+
+class TestExceedsLocalCapacity:
+    def test_keyword_comprehensive(self):
+        assert exceeds_local_capacity("Do a comprehensive review of X") is True
+
+    def test_keyword_deep_research(self):
+        assert exceeds_local_capacity("deep research into neural networks") is True
+
+    def test_keyword_benchmark(self):
+        assert exceeds_local_capacity("benchmark these five models") is True
+
+    def test_keyword_exhaustive(self):
+        assert exceeds_local_capacity("exhaustive list of options") is True
+
+    def test_keyword_case_insensitive(self):
+        assert exceeds_local_capacity("COMPREHENSIVE analysis") is True
+
+    def test_keyword_survey(self):
+        assert exceeds_local_capacity("survey all available tools") is True
+
+    def test_keyword_extensive(self):
+        assert exceeds_local_capacity("extensive documentation needed") is True
+
+    def test_short_simple_task(self):
+        assert exceeds_local_capacity("fix the login bug") is False
+
+    def test_long_task_exceeds_word_threshold(self):
+        long_task = " ".join(["word"] * 55)
+        assert exceeds_local_capacity(long_task) is True
+
+    def test_exactly_at_threshold(self):
+        at_threshold = " ".join(["word"] * 50)
+        assert exceeds_local_capacity(at_threshold) is True
+
+    def test_just_below_threshold(self):
+        short = " ".join(["word"] * 49)
+        assert exceeds_local_capacity(short) is False
+
+    def test_empty_string(self):
+        assert exceeds_local_capacity("") is False
+
+
+# ── _slugify ──────────────────────────────────────────────────────────────────
+
+
+class TestSlugify:
+    def test_simple_text(self):
+        assert _slugify("Hello World") == "hello-world"
+
+    def test_special_characters_removed(self):
+        assert _slugify("Hello, World!") == "hello-world"
+
+    def test_underscores_become_dashes(self):
+        assert _slugify("hello_world") == "hello-world"
+
+    def test_multiple_spaces(self):
+        assert _slugify("hello   world") == "hello-world"
+
+    def test_truncates_to_60(self):
+        long = "a" * 80
+        result = _slugify(long)
+        assert len(result) <= 60
+
+    def test_no_leading_trailing_dashes(self):
+        result = _slugify("  hello  ")
+        assert not result.startswith("-")
+        assert not result.endswith("-")
+
+    def test_empty_string(self):
+        assert _slugify("") == ""
+
+
+# ── _build_research_template ──────────────────────────────────────────────────
+
+
+class TestBuildResearchTemplate:
+    def test_contains_task(self):
+        body = _build_research_template("My Task", "some context", "What is X?")
+        assert "My Task" in body
+
+    def test_contains_question(self):
+        body = _build_research_template("Task", "ctx", "What is the answer?")
+        assert "What is the answer?" in body
+
+    def test_contains_context(self):
+        body = _build_research_template("Task", "project background", "Q?")
+        assert "project background" in body
+
+    def test_contains_kimi_ready_label(self):
+        body = _build_research_template("Task", "ctx", "Q?")
+        assert KIMI_READY_LABEL in body
+
+    def test_default_priority_normal(self):
+        body = _build_research_template("Task", "ctx", "Q?")
+        assert "normal" in body
+
+    def test_custom_priority_high(self):
+        body = _build_research_template("Task", "ctx", "Q?", priority="high")
+        assert "high" in body
+
+    def test_contains_deliverables_section(self):
+        body = _build_research_template("Task", "ctx", "Q?")
+        assert "Deliverables" in body
+
+    def test_slug_in_artifact_path(self):
+        body = _build_research_template("My Research Task", "ctx", "Q?")
+        assert "my-research-task" in body
+
+    def test_contains_research_request_header(self):
+        body = _build_research_template("Task", "ctx", "Q?")
+        assert "## Research Request" in body
+
+
+# ── _extract_action_items ─────────────────────────────────────────────────────
+
+
+class TestExtractActionItems:
+    def test_checkbox_items(self):
+        text = "- [ ] Do thing A\n- [ ] Do thing B"
+        items = _extract_action_items(text)
+        assert "Do thing A" in items
+        assert "Do thing B" in items
+
+    def test_numbered_list(self):
+        text = "1. First step\n2. Second step\n3. Third step"
+        items = _extract_action_items(text)
+        assert "First step" in items
+        assert "Second step" in items
+        assert "Third step" in items
+
+    def test_action_prefix(self):
+        text = "Action: Implement caching layer"
+        items = _extract_action_items(text)
+        assert "Implement caching layer" in items
+
+    def test_todo_prefix(self):
+        text = "TODO: Write tests"
+        items = _extract_action_items(text)
+        assert "Write tests" in items
+
+    def test_next_step_prefix(self):
+        text = "Next step: Deploy to staging"
+        items = _extract_action_items(text)
+        assert "Deploy to staging" in items
+
+    def test_case_insensitive_prefixes(self):
+        text = "TODO: Upper\ntodo: lower\nTodo: Mixed"
+        items = _extract_action_items(text)
+        assert len(items) == 3
+
+    def test_deduplication(self):
+        text = "1. Do the thing\n2. Do the thing"
+        items = _extract_action_items(text)
+        assert items.count("Do the thing") == 1
+
+    def test_empty_text(self):
+        assert _extract_action_items("") == []
+
+    def test_no_action_items(self):
+        text = "This is just a paragraph with no action items."
+        assert _extract_action_items(text) == []
+
+    def test_returns_list(self):
+        assert isinstance(_extract_action_items("1. Item"), list)
+
+
+# ── delegate_research_to_kimi ─────────────────────────────────────────────────
+
+
+class TestDelegateResearchToKimi:
+    @pytest.mark.asyncio
+    async def test_empty_task_returns_error(self):
+        result = await delegate_research_to_kimi("", "context", "question?")
+        assert result["success"] is False
+        assert "task" in result["error"].lower()
+
+    @pytest.mark.asyncio
+    async def test_whitespace_task_returns_error(self):
+        result = await delegate_research_to_kimi("   ", "context", "question?")
+        assert result["success"] is False
+
+    @pytest.mark.asyncio
+    async def test_empty_question_returns_error(self):
+        result = await delegate_research_to_kimi("Task title", "context", "")
+        assert result["success"] is False
+        assert "question" in result["error"].lower()
+
+    @pytest.mark.asyncio
+    async def test_whitespace_question_returns_error(self):
+        result = await delegate_research_to_kimi("Task", "ctx", "   ")
+        assert result["success"] is False
+
+    @pytest.mark.asyncio
+    async def test_delegates_to_create_issue(self):
+        with patch(
+            "timmy.kimi_delegation.create_kimi_research_issue",
+            new_callable=AsyncMock,
+            return_value={
+                "success": True,
+                "issue_number": 42,
+                "issue_url": "http://x/42",
+                "error": None,
+            },
+        ) as mock_create:
+            result = await delegate_research_to_kimi("Task", "ctx", "What is X?", "high")
+            mock_create.assert_awaited_once_with("Task", "ctx", "What is X?", "high")
+            assert result["success"] is True
+            assert result["issue_number"] == 42
+
+    @pytest.mark.asyncio
+    async def test_passes_default_priority(self):
+        with patch(
+            "timmy.kimi_delegation.create_kimi_research_issue",
+            new_callable=AsyncMock,
+            return_value={"success": True, "issue_number": 1, "issue_url": "", "error": None},
+        ) as mock_create:
+            await delegate_research_to_kimi("Task", "ctx", "Q?")
+            _, _, _, priority = mock_create.call_args.args
+            assert priority == "normal"
+
+
+# ── create_kimi_research_issue ────────────────────────────────────────────────
+
+
+class TestCreateKimiResearchIssue:
+    @pytest.mark.asyncio
+    async def test_no_gitea_token_returns_error(self):
+        from timmy.kimi_delegation import create_kimi_research_issue
+
+        mock_settings = MagicMock()
+        mock_settings.gitea_enabled = True
+        mock_settings.gitea_token = ""
+
+        with patch("config.settings", mock_settings):
+            result = await create_kimi_research_issue("Task", "ctx", "Q?")
+        assert result["success"] is False
+        assert "not configured" in result["error"]
+
+    @pytest.mark.asyncio
+    async def test_gitea_disabled_returns_error(self):
+        from timmy.kimi_delegation import create_kimi_research_issue
+
+        mock_settings = MagicMock()
+        mock_settings.gitea_enabled = False
+        mock_settings.gitea_token = "tok"
+
+        with patch("config.settings", mock_settings):
+            result = await create_kimi_research_issue("Task", "ctx", "Q?")
+        assert result["success"] is False
+
+    @pytest.mark.asyncio
+    async def test_successful_issue_creation(self):
+        from timmy.kimi_delegation import create_kimi_research_issue
+
+        mock_settings = MagicMock()
+        mock_settings.gitea_enabled = True
+        mock_settings.gitea_token = "fake-token"
+        mock_settings.gitea_url = "http://gitea.local"
+        mock_settings.gitea_repo = "owner/repo"
+
+        label_resp = MagicMock()
+        label_resp.status_code = 200
+        label_resp.json.return_value = [{"name": "kimi-ready", "id": 7}]
+
+        issue_resp = MagicMock()
+        issue_resp.status_code = 201
+        issue_resp.json.return_value = {
+            "number": 101,
+            "html_url": "http://gitea.local/issues/101",
+        }
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = label_resp
+        mock_client.post.return_value = issue_resp
+
+        async_ctx = AsyncMock()
+        async_ctx.__aenter__.return_value = mock_client
+        async_ctx.__aexit__.return_value = False
+
+        with (
+            patch("config.settings", mock_settings),
+            patch("httpx.AsyncClient", return_value=async_ctx),
+        ):
+            result = await create_kimi_research_issue("Task", "ctx", "Q?")
+
+        assert result["success"] is True
+        assert result["issue_number"] == 101
+        assert result["error"] is None
+
+    @pytest.mark.asyncio
+    async def test_api_error_returns_failure(self):
+        from timmy.kimi_delegation import create_kimi_research_issue
+
+        mock_settings = MagicMock()
+        mock_settings.gitea_enabled = True
+        mock_settings.gitea_token = "tok"
+        mock_settings.gitea_url = "http://gitea.local"
+        mock_settings.gitea_repo = "owner/repo"
+
+        label_resp = MagicMock()
+        label_resp.status_code = 200
+        label_resp.json.return_value = [{"name": "kimi-ready", "id": 7}]
+
+        issue_resp = MagicMock()
+        issue_resp.status_code = 500
+        issue_resp.text = "Internal Server Error"
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = label_resp
+        mock_client.post.return_value = issue_resp
+
+        async_ctx = AsyncMock()
+        async_ctx.__aenter__.return_value = mock_client
+        async_ctx.__aexit__.return_value = False
+
+        with (
+            patch("config.settings", mock_settings),
+            patch("httpx.AsyncClient", return_value=async_ctx),
+        ):
+            result = await create_kimi_research_issue("Task", "ctx", "Q?")
+
+        assert result["success"] is False
+        assert "500" in result["error"]
+
+
+# ── index_kimi_artifact ───────────────────────────────────────────────────────
+
+
+class TestIndexKimiArtifact:
+    @pytest.mark.asyncio
+    async def test_empty_artifact_returns_error(self):
+        from timmy.kimi_delegation import index_kimi_artifact
+
+        result = await index_kimi_artifact(42, "Title", "")
+        assert result["success"] is False
+        assert "Empty" in result["error"]
+
+    @pytest.mark.asyncio
+    async def test_whitespace_only_artifact_returns_error(self):
+        from timmy.kimi_delegation import index_kimi_artifact
+
+        result = await index_kimi_artifact(42, "Title", "   \n  ")
+        assert result["success"] is False
+
+    @pytest.mark.asyncio
+    async def test_successful_indexing(self):
+        from timmy.kimi_delegation import index_kimi_artifact
+
+        mock_entry = MagicMock()
+        mock_entry.id = "mem-abc-123"
+
+        with patch("timmy.memory_system.store_memory", return_value=mock_entry) as mock_store:
+            result = await index_kimi_artifact(55, "Research Title", "Artifact content here.")
+
+        assert result["success"] is True
+        assert result["memory_id"] == "mem-abc-123"
+        mock_store.assert_called_once()
+        call_kwargs = mock_store.call_args.kwargs
+        assert call_kwargs["source"] == "kimi"
+        assert call_kwargs["context_type"] == "document"
+        assert call_kwargs["task_id"] == "55"
+
+    @pytest.mark.asyncio
+    async def test_store_memory_exception_returns_error(self):
+        from timmy.kimi_delegation import index_kimi_artifact
+
+        with patch(
+            "timmy.memory_system.store_memory",
+            side_effect=RuntimeError("DB error"),
+        ):
+            result = await index_kimi_artifact(1, "T", "Some content")
+        assert result["success"] is False
+        assert "DB error" in result["error"]
+
+
+# ── extract_and_create_followups ──────────────────────────────────────────────
+
+
+class TestExtractAndCreateFollowups:
+    @pytest.mark.asyncio
+    async def test_no_action_items_returns_empty_list(self):
+        from timmy.kimi_delegation import extract_and_create_followups
+
+        result = await extract_and_create_followups("No action items here.", 10)
+        assert result["success"] is True
+        assert result["created"] == []
+        assert result["error"] is None
+
+    @pytest.mark.asyncio
+    async def test_gitea_not_configured(self):
+        from timmy.kimi_delegation import extract_and_create_followups
+
+        mock_settings = MagicMock()
+        mock_settings.gitea_enabled = False
+        mock_settings.gitea_token = ""
+
+        with patch("config.settings", mock_settings):
+            result = await extract_and_create_followups("1. Do the thing", 10)
+        assert result["success"] is False
+        assert result["created"] == []
+
+    @pytest.mark.asyncio
+    async def test_creates_followup_issues(self):
+        from timmy.kimi_delegation import extract_and_create_followups
+
+        mock_settings = MagicMock()
+        mock_settings.gitea_enabled = True
+        mock_settings.gitea_token = "tok"
+        mock_settings.gitea_url = "http://gitea.local"
+        mock_settings.gitea_repo = "owner/repo"
+
+        issue_resp = MagicMock()
+        issue_resp.status_code = 201
+        issue_resp.json.return_value = {"number": 200}
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = issue_resp
+
+        async_ctx = AsyncMock()
+        async_ctx.__aenter__.return_value = mock_client
+        async_ctx.__aexit__.return_value = False
+
+        with (
+            patch("config.settings", mock_settings),
+            patch("httpx.AsyncClient", return_value=async_ctx),
+        ):
+            result = await extract_and_create_followups("1. Do the thing\n2. Do another thing", 10)
+
+        assert result["success"] is True
+        assert 200 in result["created"]

tests/unit/test_retrain_loop.py

@@ -0,0 +1,546 @@
+"""Unit tests for the AutoLoRA continuous improvement loop.
+
+Covers trajectory extraction, quality filtering, dataset management,
+and the retrain orchestrator.
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import UTC, datetime, timedelta
+from pathlib import Path
+
+from timmy_automations.retrain.quality_filter import QualityFilter, TrajectoryQuality
+from timmy_automations.retrain.retrain import RetrainOrchestrator
+from timmy_automations.retrain.training_dataset import TrainingDataset
+from timmy_automations.retrain.training_log import CycleMetrics, TrainingLog
+from timmy_automations.retrain.trajectory_exporter import Trajectory, TrajectoryExporter
+
+# ── Fixtures ─────────────────────────────────────────────────────────────────
+
+
+def _ts(offset_minutes: int = 0) -> str:
+    """Return an ISO timestamp offset from now."""
+    return (datetime.now(tz=UTC) + timedelta(minutes=offset_minutes)).isoformat()
+
+
+def _make_session_log(entries: list[dict], date_str: str, tmp_path: Path) -> Path:
+    """Write session JSONL entries to a temp log file."""
+    log_dir = tmp_path / "logs"
+    log_dir.mkdir(parents=True, exist_ok=True)
+    log_file = log_dir / f"session_{date_str}.jsonl"
+    with open(log_file, "w") as f:
+        for entry in entries:
+            f.write(json.dumps(entry) + "\n")
+    return log_file
+
+
+def _user_msg(content: str, offset: int = 0) -> dict:
+    return {"type": "message", "role": "user", "content": content, "timestamp": _ts(offset)}
+
+
+def _timmy_msg(content: str, confidence: float | None = None, offset: int = 0) -> dict:
+    entry = {"type": "message", "role": "timmy", "content": content, "timestamp": _ts(offset)}
+    if confidence is not None:
+        entry["confidence"] = confidence
+    return entry
+
+
+def _tool_call(tool: str = "bash", result: str = "ok", offset: int = 0) -> dict:
+    return {
+        "type": "tool_call",
+        "tool": tool,
+        "args": {},
+        "result": result,
+        "timestamp": _ts(offset),
+    }
+
+
+def _error_entry(msg: str = "Something failed", offset: int = 0) -> dict:
+    return {"type": "error", "error": msg, "timestamp": _ts(offset)}
+
+
+def _decision_entry(decision: str = "Use approach A", offset: int = 0) -> dict:
+    return {"type": "decision", "decision": decision, "timestamp": _ts(offset)}
+
+
+# ── Trajectory dataclass tests ────────────────────────────────────────────────
+
+
+class TestTrajectory:
+    def test_message_count(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("hi"), _timmy_msg("hello")],
+        )
+        assert t.message_count == 2
+
+    def test_tool_call_count(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            tool_calls=[_tool_call(), _tool_call()],
+        )
+        assert t.tool_call_count == 2
+
+    def test_has_successful_tool_call_when_no_errors(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            tool_calls=[_tool_call()],
+            errors=[],
+        )
+        assert t.has_successful_tool_call is True
+
+    def test_has_successful_tool_call_false_when_errors(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            tool_calls=[_tool_call()],
+            errors=[_error_entry()],
+        )
+        assert t.has_successful_tool_call is False
+
+    def test_is_multi_step(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("do it"), _timmy_msg("done")],
+            tool_calls=[_tool_call()],
+        )
+        assert t.is_multi_step is True
+
+    def test_is_not_multi_step_single_message(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_timmy_msg("hello")],
+            tool_calls=[],
+        )
+        assert t.is_multi_step is False
+
+    def test_to_chat_format_ordering(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("question", offset=0), _timmy_msg("answer", offset=2)],
+            tool_calls=[_tool_call(offset=1)],
+        )
+        chat = t.to_chat_format()
+        roles = [m["role"] for m in chat]
+        assert "user" in roles
+        assert "assistant" in roles
+
+    def test_to_chat_format_empty_content_skipped(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg(""), _timmy_msg("response")],
+        )
+        chat = t.to_chat_format()
+        # Empty user message should be skipped
+        assert all(m["content"] for m in chat)
+
+
+# ── TrajectoryExporter tests ──────────────────────────────────────────────────
+
+
+class TestTrajectoryExporter:
+    def test_export_empty_logs_dir(self, tmp_path):
+        (tmp_path / "logs").mkdir()
+        exporter = TrajectoryExporter(logs_dir=tmp_path / "logs", repo_root=tmp_path)
+        result = exporter.export_week(weeks_ago=0)
+        assert result == []
+
+    def test_export_reads_session_files(self, tmp_path):
+        # Write a session file for this week
+        today = datetime.now(tz=UTC)
+        date_str = today.strftime("%Y-%m-%d")
+        entries = [
+            _user_msg("tell me about Python"),
+            _timmy_msg("Python is great"),
+        ]
+        _make_session_log(entries, date_str, tmp_path)
+
+        exporter = TrajectoryExporter(logs_dir=tmp_path / "logs", repo_root=tmp_path)
+        result = exporter.export_week(weeks_ago=0)
+        assert len(result) >= 1
+
+    def test_export_skips_old_sessions(self, tmp_path):
+        # Write a session file for 3 weeks ago
+        three_weeks_ago = datetime.now(tz=UTC) - timedelta(weeks=3)
+        date_str = three_weeks_ago.strftime("%Y-%m-%d")
+        entries = [_user_msg("old message"), _timmy_msg("old response")]
+        _make_session_log(entries, date_str, tmp_path)
+
+        exporter = TrajectoryExporter(logs_dir=tmp_path / "logs", repo_root=tmp_path)
+        # Request current week — should not include 3-week-old data
+        result = exporter.export_week(weeks_ago=0)
+        assert result == []
+
+    def test_export_segments_by_gap(self, tmp_path):
+        today = datetime.now(tz=UTC)
+        date_str = today.strftime("%Y-%m-%d")
+
+        # Two conversations separated by 10 minutes
+        t1 = (today - timedelta(minutes=15)).isoformat()
+        t2 = (today - timedelta(minutes=14)).isoformat()
+        t3 = (today - timedelta(minutes=2)).isoformat()
+        t4 = (today - timedelta(minutes=1)).isoformat()
+
+        entries = [
+            {"type": "message", "role": "user", "content": "first q", "timestamp": t1},
+            {"type": "message", "role": "timmy", "content": "first a", "timestamp": t2},
+            {"type": "message", "role": "user", "content": "second q", "timestamp": t3},
+            {"type": "message", "role": "timmy", "content": "second a", "timestamp": t4},
+        ]
+        _make_session_log(entries, date_str, tmp_path)
+
+        exporter = TrajectoryExporter(logs_dir=tmp_path / "logs", repo_root=tmp_path)
+        result = exporter.export_week(weeks_ago=0)
+        # Should have at least 1 trajectory (may be 1 or 2 depending on segmentation)
+        assert len(result) >= 1
+
+    def test_handles_malformed_log_file(self, tmp_path):
+        log_dir = tmp_path / "logs"
+        log_dir.mkdir()
+        today = datetime.now(tz=UTC).strftime("%Y-%m-%d")
+        (log_dir / f"session_{today}.jsonl").write_text("not json\n{}\n")
+
+        exporter = TrajectoryExporter(logs_dir=log_dir, repo_root=tmp_path)
+        # Should not raise, just return empty or partial results
+        result = exporter.export_week(weeks_ago=0)
+        assert isinstance(result, list)
+
+
+# ── QualityFilter tests ───────────────────────────────────────────────────────
+
+
+class TestQualityFilter:
+    def _make_high_quality(self) -> Trajectory:
+        return Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("do task"), _timmy_msg("done", confidence=0.9)],
+            tool_calls=[_tool_call(), _tool_call()],
+            errors=[],
+            decisions=[_decision_entry()],
+        )
+
+    def _make_medium_quality(self) -> Trajectory:
+        return Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("hello"), _timmy_msg("hi")],
+            tool_calls=[],
+            errors=[],
+        )
+
+    def _make_low_quality(self) -> Trajectory:
+        return Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_timmy_msg("oops")],  # No user message
+            errors=[_error_entry()],
+        )
+
+    def test_high_quality_classification(self):
+        qf = QualityFilter()
+        result = qf.assess(self._make_high_quality())
+        assert result.quality == TrajectoryQuality.HIGH
+        assert result.score >= 4.0
+        assert result.is_trainable
+
+    def test_medium_quality_classification(self):
+        qf = QualityFilter()
+        result = qf.assess(self._make_medium_quality())
+        assert result.quality == TrajectoryQuality.MEDIUM
+        assert result.is_trainable
+
+    def test_low_quality_no_user_message(self):
+        qf = QualityFilter()
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_timmy_msg("random")],
+        )
+        result = qf.assess(t)
+        assert result.quality == TrajectoryQuality.LOW
+        assert not result.is_trainable
+
+    def test_error_penalizes_score(self):
+        qf = QualityFilter()
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("go"), _timmy_msg("fail")],
+            tool_calls=[_tool_call()],
+            errors=[_error_entry(), _error_entry()],
+        )
+        result = qf.assess(t)
+        assert result.score < qf.assess(self._make_high_quality()).score
+
+    def test_low_confidence_penalizes_score(self):
+        qf = QualityFilter()
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("q"), _timmy_msg("a", confidence=0.2)],
+        )
+        result = qf.assess(t)
+        assert result.score < 1.0
+
+    def test_filter_returns_stats(self):
+        qf = QualityFilter()
+        trajectories = [
+            self._make_high_quality(),
+            self._make_medium_quality(),
+            self._make_low_quality(),
+        ]
+        trainable, stats = qf.filter(trajectories)
+        assert stats["total"] == 3
+        assert stats["accepted"] == len(trainable)
+        assert stats["high"] + stats["medium"] + stats["low"] == 3
+
+    def test_filter_empty_list(self):
+        qf = QualityFilter()
+        trainable, stats = qf.filter([])
+        assert trainable == []
+        assert stats["total"] == 0
+        assert stats["accepted"] == 0
+
+
+# ── TrainingDataset tests ─────────────────────────────────────────────────────
+
+
+class TestTrainingDataset:
+    def _make_result(self, quality=TrajectoryQuality.HIGH, score=5.0) -> object:
+        from timmy_automations.retrain.quality_filter import QualityResult
+
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(-5),
+            ended_at=_ts(),
+            messages=[_user_msg("do it"), _timmy_msg("done")],
+            tool_calls=[_tool_call()],
+        )
+        return QualityResult(trajectory=t, quality=quality, score=score, reasons=[])
+
+    def test_count_empty_dataset(self, tmp_path):
+        ds = TrainingDataset(
+            dataset_path=".loop/retrain/training_data.jsonl",
+            repo_root=tmp_path,
+        )
+        assert ds.count() == 0
+
+    def test_append_adds_examples(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        result = ds.append([self._make_result()], "2026-W12")
+        assert result.new_examples == 1
+        assert result.total_examples == 1
+        assert ds.count() == 1
+
+    def test_append_idempotent(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        r = self._make_result()
+        ds.append([r], "2026-W12")
+        result2 = ds.append([r], "2026-W12")
+        # Same trajectory shouldn't be added twice
+        assert result2.new_examples == 0
+        assert ds.count() == 1
+
+    def test_append_different_weeks(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        r1 = self._make_result()
+        ds.append([r1], "2026-W11")
+        ds.append([r1], "2026-W12")
+        # Different week tags = different records
+        assert ds.count() == 2
+
+    def test_dataset_file_is_valid_jsonl(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        ds.append([self._make_result()], "2026-W12")
+        with open(ds.dataset_path) as f:
+            lines = [line.strip() for line in f if line.strip()]
+        assert len(lines) == 1
+        record = json.loads(lines[0])
+        assert "messages" in record
+        assert "week" in record
+        assert "quality" in record
+
+    def test_index_updated_after_append(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        ds.append([self._make_result()], "2026-W12")
+        index_path = tmp_path / ".loop" / "retrain" / "dataset_index.json"
+        assert index_path.exists()
+        index = json.loads(index_path.read_text())
+        assert index["total_examples"] == 1
+        assert "2026-W12" in index["weeks"]
+
+
+# ── TrainingLog tests ─────────────────────────────────────────────────────────
+
+
+class TestTrainingLog:
+    def _make_metrics(self, iteration: int = 1) -> CycleMetrics:
+        return CycleMetrics(
+            iteration=iteration,
+            week="2026-W12",
+            ran_at=datetime.now(tz=UTC).isoformat(),
+            trajectories_total=10,
+            trajectories_high=5,
+            trajectories_medium=3,
+            trajectories_low=2,
+            trajectories_accepted=8,
+            examples_added=5,
+            dataset_total=5,
+            train_status="completed",
+            train_loss=1.2345,
+            train_duration_seconds=120.5,
+            adapter_path=".loop/retrain/adapters/iter_0001/adapters.npz",
+            model_name="hermes4-14b-ft-0001",
+            notes="First fine-tune cycle complete",
+        )
+
+    def test_next_iteration_starts_at_1(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        assert log.next_iteration() == 1
+
+    def test_next_iteration_increments(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics(iteration=1))
+        assert log.next_iteration() == 2
+
+    def test_record_creates_log_file(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics())
+        assert log.log_path.exists()
+
+    def test_load_all_returns_records(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics(iteration=1))
+        log.record(self._make_metrics(iteration=2))
+        entries = log.load_all()
+        assert len(entries) == 2
+        assert entries[0]["iteration"] == 1
+
+    def test_latest_returns_last_entry(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics(iteration=1))
+        log.record(self._make_metrics(iteration=2))
+        latest = log.latest()
+        assert latest is not None
+        assert latest["iteration"] == 2
+
+    def test_latest_returns_none_when_empty(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        assert log.latest() is None
+
+    def test_summary_markdown_written(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics())
+        summary_path = tmp_path / ".loop" / "retrain" / "training_log.md"
+        assert summary_path.exists()
+        content = summary_path.read_text()
+        assert "AutoLoRA Training Log" in content
+        assert "2026-W12" in content
+        assert "completed" in content
+
+    def test_skill_accuracy_in_summary(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        m = self._make_metrics()
+        m.skill_accuracy = {"tool_calling": 0.85, "reasoning": 0.72}
+        log.record(m)
+        content = (tmp_path / ".loop" / "retrain" / "training_log.md").read_text()
+        assert "tool_calling" in content
+        assert "reasoning" in content
+
+
+# ── RetrainOrchestrator integration tests ─────────────────────────────────────
+
+
+class TestRetrainOrchestrator:
+    def test_run_dry_run_no_data(self, tmp_path):
+        """Dry run with no session logs should complete without errors."""
+        (tmp_path / "logs").mkdir(parents=True)
+        orc = RetrainOrchestrator(repo_root=tmp_path, dry_run=True)
+        result = orc.run(weeks_ago=0)
+        assert result.train_status in ("skipped",)
+        assert result.examples_added == 0
+        assert result.iteration == 1
+
+    def test_run_creates_log_entry(self, tmp_path):
+        (tmp_path / "logs").mkdir(parents=True)
+        orc = RetrainOrchestrator(repo_root=tmp_path, dry_run=True)
+        orc.run(weeks_ago=0)
+        log = TrainingLog(repo_root=tmp_path)
+        entries = log.load_all()
+        assert len(entries) == 1
+
+    def test_run_with_session_data(self, tmp_path):
+        """Run with actual session data — should export, filter, and log."""
+        today = datetime.now(tz=UTC)
+        date_str = today.strftime("%Y-%m-%d")
+        entries = [
+            _user_msg("deploy the service", offset=-10),
+            _tool_call("bash", "deployed successfully", offset=-9),
+            _tool_call("bash", "health check ok", offset=-8),
+            _timmy_msg("Service deployed and healthy", confidence=0.92, offset=-7),
+            _user_msg("run the tests", offset=-6),
+            _tool_call("bash", "All tests passed", offset=-5),
+            _timmy_msg("All 42 tests passed", confidence=0.95, offset=-4),
+        ]
+        _make_session_log(entries, date_str, tmp_path)
+
+        orc = RetrainOrchestrator(repo_root=tmp_path, dry_run=True)
+        result = orc.run(weeks_ago=0)
+
+        assert result.trajectories_exported >= 1
+        assert result.iteration == 1
+        # In dry_run mode, fine-tune is skipped but trajectories should be processed
+        assert result.train_status == "skipped"
+
+    def test_iteration_increments_on_second_run(self, tmp_path):
+        (tmp_path / "logs").mkdir(parents=True)
+        orc = RetrainOrchestrator(repo_root=tmp_path, dry_run=True)
+        r1 = orc.run(weeks_ago=0)
+        r2 = orc.run(weeks_ago=0)
+        assert r2.iteration == r1.iteration + 1
+
+    def test_automations_json_has_retrain_entry(self):
+        """Verify the retrain automation is registered in automations.json."""
+        config_path = _REPO_ROOT / "timmy_automations" / "config" / "automations.json"
+        assert config_path.exists()
+        manifest = json.loads(config_path.read_text())
+        ids = [a["id"] for a in manifest.get("automations", [])]
+        assert "retrain" in ids
+
+    def test_retrain_automation_config(self):
+        """Verify retrain automation has correct schedule and config."""
+        config_path = _REPO_ROOT / "timmy_automations" / "config" / "automations.json"
+        manifest = json.loads(config_path.read_text())
+        retrain = next(a for a in manifest["automations"] if a["id"] == "retrain")
+        assert retrain["schedule"] == "weekly_sunday"
+        assert retrain["trigger"] == "scheduled"
+        assert retrain["config"]["base_model"] == "hermes4-14b"
+        assert retrain["config"]["weeks_ago"] == 1
+
+
+_REPO_ROOT = Path(__file__).resolve().parent.parent.parent

timmy_automations/config/automations.json

@@ -4,7 +4,7 @@
   "_health_snapshot": {
     "note": "Quick health check before coding — CI, P0/P1 issues, flakiness"
   },
-  "last_updated": "2026-03-21",
+  "last_updated": "2026-03-23",
   "automations": [
     {
       "id": "cycle_retro",
@@ -268,6 +268,36 @@
         "ci_timeout_seconds": 5
       },
       "outputs": []
+    },
+    {
+      "id": "retrain",
+      "name": "AutoLoRA Continuous Improvement Loop",
+      "description": "Weekly sovereignty loop — exports trajectories, filters quality, appends to training dataset, triggers LoRA fine-tune, loads new adapter, and logs iteration metrics",
+      "script": "timmy_automations/retrain/retrain.py",
+      "category": "autolora",
+      "enabled": true,
+      "trigger": "scheduled",
+      "schedule": "weekly_sunday",
+      "executable": "python3",
+      "epic": "#1091",
+      "pipeline": "AutoLoRA Sovereignty Loop (Step 6 of 7)",
+      "config": {
+        "weeks_ago": 1,
+        "base_model": "hermes4-14b",
+        "dry_run": false,
+        "logs_dir": "logs",
+        "dataset_path": ".loop/retrain/training_data.jsonl",
+        "adapter_dir": ".loop/retrain/adapters",
+        "training_log_path": ".loop/retrain/training_log.jsonl",
+        "training_summary_path": ".loop/retrain/training_log.md"
+      },
+      "outputs": [
+        ".loop/retrain/training_data.jsonl",
+        ".loop/retrain/dataset_index.json",
+        ".loop/retrain/training_log.jsonl",
+        ".loop/retrain/training_log.md",
+        ".loop/retrain/adapters/"
+      ]
     }
   ]
 }

timmy_automations/retrain/__init__.py

@@ -0,0 +1,26 @@
+"""AutoLoRA continuous improvement loop — sovereignty engine for Timmy.
+
+Implements the weekly retrain cycle:
+  Work → Record trajectories → Export weekly → Filter quality
+  → LoRA fine-tune → Load adapter → Model improves → Repeat
+
+Epic: #1091 — Project Bannerlord
+Pipeline: AutoLoRA Sovereignty Loop (Step 6 of 7)
+Refs: #1105
+"""
+
+from timmy_automations.retrain.quality_filter import QualityFilter, TrajectoryQuality
+from timmy_automations.retrain.retrain import RetrainOrchestrator, RetrainResult
+from timmy_automations.retrain.training_dataset import TrainingDataset
+from timmy_automations.retrain.training_log import TrainingLog
+from timmy_automations.retrain.trajectory_exporter import TrajectoryExporter
+
+__all__ = [
+    "QualityFilter",
+    "RetrainOrchestrator",
+    "RetrainResult",
+    "TrainingDataset",
+    "TrainingLog",
+    "TrajectoryExporter",
+    "TrajectoryQuality",
+]

timmy_automations/retrain/lora_trainer.py

@@ -0,0 +1,262 @@
+"""LoRA trainer — triggers fine-tune job and loads the resulting adapter.
+
+Supports two backends:
+1. mlx-lm (default, Apple Silicon) — `mlx_lm.lora` CLI
+2. Ollama create (adapter packaging into a new Ollama model)
+
+Graceful degradation: if neither backend is available, logs a warning
+and returns a skipped result — the rest of the loop continues.
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import shutil
+import subprocess
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_BASE_MODEL = "hermes4-14b"
+_DEFAULT_ADAPTER_DIR = ".loop/retrain/adapters"
+_MLX_LM_BIN = "mlx_lm.lora"
+_OLLAMA_BIN = "ollama"
+
+
+@dataclass
+class TrainResult:
+    """Result of a LoRA fine-tune run."""
+
+    status: str  # "completed" | "skipped" | "failed"
+    adapter_path: str | None
+    model_name: str | None
+    iteration: int
+    duration_seconds: float
+    message: str
+    train_loss: float | None = None
+
+
+class LoRATrainer:
+    """Orchestrates LoRA fine-tuning and adapter loading.
+
+    Workflow:
+    1. Run mlx_lm.lora fine-tune on the training dataset
+    2. Save the resulting adapter to .loop/retrain/adapters/<iteration>/
+    3. Create (or update) an Ollama model that uses the new adapter
+    """
+
+    def __init__(
+        self,
+        base_model: str = _DEFAULT_BASE_MODEL,
+        adapter_dir: str | Path | None = None,
+        repo_root: str | Path | None = None,
+        dry_run: bool = False,
+    ):
+        if repo_root is None:
+            repo_root = Path(__file__).resolve().parent.parent.parent
+        self._repo_root = Path(repo_root)
+
+        self._base_model = base_model
+        self._adapter_dir = self._repo_root / (adapter_dir or _DEFAULT_ADAPTER_DIR)
+        self._adapter_dir.mkdir(parents=True, exist_ok=True)
+        self._dry_run = dry_run
+
+    def train(self, dataset_path: Path, iteration: int) -> TrainResult:
+        """Run LoRA fine-tuning on the dataset.
+
+        Args:
+            dataset_path: Path to the JSONL training dataset.
+            iteration: Current fine-tune iteration number (used for naming).
+
+        Returns:
+            TrainResult with status, adapter path, and metrics.
+        """
+        started = datetime.now(tz=UTC)
+
+        if not dataset_path.exists() or dataset_path.stat().st_size == 0:
+            return TrainResult(
+                status="skipped",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=0.0,
+                message="Training dataset is empty — skipping fine-tune",
+            )
+
+        if self._dry_run:
+            logger.info("[dry-run] Would fine-tune %s on %s", self._base_model, dataset_path)
+            adapter_path = self._adapter_dir / f"iter_{iteration:04d}" / "adapters.npz"
+            return TrainResult(
+                status="skipped",
+                adapter_path=str(adapter_path),
+                model_name=f"{self._base_model}-ft-{iteration:04d}",
+                iteration=iteration,
+                duration_seconds=0.0,
+                message="dry-run mode — no training performed",
+            )
+
+        # Determine which backend is available
+        if shutil.which(_MLX_LM_BIN):
+            return self._train_mlx(dataset_path, iteration, started)
+        else:
+            logger.warning(
+                "%s not found — skipping LoRA fine-tune (install mlx-lm to enable)",
+                _MLX_LM_BIN,
+            )
+            return TrainResult(
+                status="skipped",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=0.0,
+                message=(
+                    f"{_MLX_LM_BIN} not available. "
+                    "Install mlx-lm on Apple Silicon to enable LoRA fine-tuning."
+                ),
+            )
+
+    def _train_mlx(
+        self, dataset_path: Path, iteration: int, started: datetime
+    ) -> TrainResult:
+        """Run mlx_lm.lora fine-tune."""
+        adapter_out = self._adapter_dir / f"iter_{iteration:04d}"
+        adapter_out.mkdir(parents=True, exist_ok=True)
+
+        cmd = [
+            _MLX_LM_BIN,
+            "--model", self._base_model,
+            "--data", str(dataset_path),
+            "--adapter-path", str(adapter_out),
+            "--train",
+            "--iters", "100",
+            "--batch-size", "1",
+            "--learning-rate", "1e-5",
+        ]
+
+        logger.info("Starting mlx-lm LoRA fine-tune: iteration %d", iteration)
+        logger.info("Command: %s", " ".join(cmd))
+
+        try:
+            result = subprocess.run(
+                cmd,
+                capture_output=True,
+                text=True,
+                timeout=3600,  # 1 hour max
+                env={**os.environ, "PYTHONUNBUFFERED": "1"},
+            )
+        except subprocess.TimeoutExpired:
+            duration = (datetime.now(tz=UTC) - started).total_seconds()
+            return TrainResult(
+                status="failed",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=duration,
+                message="Fine-tune timed out after 1 hour",
+            )
+        except Exception as exc:
+            duration = (datetime.now(tz=UTC) - started).total_seconds()
+            return TrainResult(
+                status="failed",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=duration,
+                message=f"Fine-tune subprocess error: {exc}",
+            )
+
+        duration = (datetime.now(tz=UTC) - started).total_seconds()
+
+        if result.returncode != 0:
+            logger.error("mlx-lm fine-tune failed: %s", result.stderr[:500])
+            return TrainResult(
+                status="failed",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=duration,
+                message=f"mlx_lm.lora exited {result.returncode}: {result.stderr[:300]}",
+            )
+
+        # Parse final train loss from stdout if available
+        train_loss = _parse_train_loss(result.stdout)
+
+        adapter_file = adapter_out / "adapters.npz"
+        model_name = f"{self._base_model}-ft-{iteration:04d}"
+
+        # Attempt to register with Ollama
+        ollama_ok = self._register_ollama_adapter(adapter_out, model_name)
+        if not ollama_ok:
+            logger.warning("Ollama adapter registration failed — adapter saved locally")
+
+        logger.info(
+            "Fine-tune complete: iteration=%d loss=%.4f duration=%.1fs adapter=%s",
+            iteration,
+            train_loss or 0.0,
+            duration,
+            adapter_file,
+        )
+
+        return TrainResult(
+            status="completed",
+            adapter_path=str(adapter_file),
+            model_name=model_name,
+            iteration=iteration,
+            duration_seconds=duration,
+            message=f"LoRA fine-tune completed successfully in {duration:.0f}s",
+            train_loss=train_loss,
+        )
+
+    def _register_ollama_adapter(self, adapter_dir: Path, model_name: str) -> bool:
+        """Create an Ollama model entry for the new adapter.
+
+        Writes a minimal Modelfile and runs `ollama create`.
+        """
+        if not shutil.which(_OLLAMA_BIN):
+            logger.debug("Ollama not found — skipping adapter registration")
+            return False
+
+        modelfile_content = (
+            f"FROM {self._base_model}\n"
+            f"ADAPTER {adapter_dir}\n"
+        )
+        modelfile_path = adapter_dir / "Modelfile"
+        try:
+            modelfile_path.write_text(modelfile_content)
+            result = subprocess.run(
+                [_OLLAMA_BIN, "create", model_name, "-f", str(modelfile_path)],
+                capture_output=True,
+                text=True,
+                timeout=300,
+            )
+            if result.returncode == 0:
+                logger.info("Ollama model registered: %s", model_name)
+                return True
+            else:
+                logger.warning("ollama create failed: %s", result.stderr[:200])
+                return False
+        except Exception as exc:
+            logger.warning("Ollama adapter registration error: %s", exc)
+            return False
+
+
+def _parse_train_loss(stdout: str) -> float | None:
+    """Extract the final training loss from mlx-lm stdout."""
+    loss: float | None = None
+    for line in stdout.splitlines():
+        line_lower = line.lower()
+        if "train loss" in line_lower or "loss:" in line_lower:
+            parts = line.split()
+            for i, part in enumerate(parts):
+                if "loss" in part.lower() and i + 1 < len(parts):
+                    try:
+                        loss = float(parts[i + 1].strip(",:"))
+                    except ValueError:
+                        pass
+    return loss

timmy_automations/retrain/quality_filter.py

@@ -0,0 +1,172 @@
+"""Quality filter — keeps only high-value trajectories for LoRA training.
+
+Criteria for a high-quality training example:
+1. Tool calls succeeded (tool calls present, no error entries)
+2. Multi-step tasks completed (≥2 messages + ≥1 tool call)
+3. No low-confidence signals (confidence < 0.5 on any Timmy message)
+4. Minimum meaningful exchange (≥1 user message + ≥1 Timmy message)
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from enum import StrEnum
+
+from timmy_automations.retrain.trajectory_exporter import Trajectory
+
+logger = logging.getLogger(__name__)
+
+_MIN_CONFIDENCE = 0.5
+
+
+class TrajectoryQuality(StrEnum):
+    """Quality classification for a trajectory."""
+
+    HIGH = "high"      # Multi-step + tool success — ideal training data
+    MEDIUM = "medium"  # Single exchange, no errors — acceptable
+    LOW = "low"        # Error-prone or trivial — skip
+
+
+@dataclass
+class QualityResult:
+    """Result of quality assessment for a single trajectory."""
+
+    trajectory: Trajectory
+    quality: TrajectoryQuality
+    score: float
+    reasons: list[str]
+
+    @property
+    def is_trainable(self) -> bool:
+        return self.quality in (TrajectoryQuality.HIGH, TrajectoryQuality.MEDIUM)
+
+
+class QualityFilter:
+    """Filters trajectories to keep only those worth training on.
+
+    Scoring:
+    - +1 pt:  base score for any valid clean exchange (no errors)
+    - +3 pts: multi-step task (≥2 messages + ≥1 tool call)
+    - +2 pts: tool calls present and no errors
+    - +1 pt:  decision recorded (deliberate choice made)
+    - -2 pts: any error entry
+    - -1 pt:  any low-confidence response (confidence < 0.5)
+
+    HIGH ≥ 4, MEDIUM 1–3, LOW ≤ 0
+    """
+
+    def __init__(self, min_confidence: float = _MIN_CONFIDENCE):
+        self._min_confidence = min_confidence
+
+    def assess(self, trajectory: Trajectory) -> QualityResult:
+        """Score and classify a single trajectory."""
+        score = 0.0
+        reasons: list[str] = []
+
+        # Minimum viable exchange check
+        user_msgs = [m for m in trajectory.messages if m.get("role") == "user"]
+        timmy_msgs = [m for m in trajectory.messages if m.get("role") == "timmy"]
+
+        if not user_msgs or not timmy_msgs:
+            return QualityResult(
+                trajectory=trajectory,
+                quality=TrajectoryQuality.LOW,
+                score=0.0,
+                reasons=["Missing user or assistant messages — not a valid exchange"],
+            )
+
+        # Multi-step bonus
+        if trajectory.is_multi_step:
+            score += 3.0
+            reasons.append(
+                f"Multi-step task: {trajectory.message_count} messages, "
+                f"{trajectory.tool_call_count} tool calls"
+            )
+
+        # Base score for any clean exchange (user + timmy, no tool call required)
+        if trajectory.error_count == 0:
+            score += 1.0
+            reasons.append("Clean exchange (no errors)")
+
+        # Tool call quality
+        if trajectory.tool_call_count > 0:
+            if trajectory.error_count == 0:
+                score += 2.0
+                reasons.append(
+                    f"All {trajectory.tool_call_count} tool call(s) succeeded"
+                )
+            else:
+                score -= 2.0
+                reasons.append(
+                    f"{trajectory.error_count} error(s) during {trajectory.tool_call_count} tool call(s)"
+                )
+        elif trajectory.error_count > 0:
+            score -= 2.0
+            reasons.append(f"{trajectory.error_count} error(s) with no tool calls")
+
+        # Decision bonus
+        if trajectory.decisions:
+            score += 1.0
+            reasons.append(f"Decisions recorded: {len(trajectory.decisions)}")
+
+        # Confidence penalty
+        low_conf = [
+            m
+            for m in timmy_msgs
+            if m.get("confidence") is not None
+            and m["confidence"] < self._min_confidence
+        ]
+        if low_conf:
+            score -= len(low_conf)
+            reasons.append(
+                f"{len(low_conf)} low-confidence response(s) (threshold={self._min_confidence})"
+            )
+
+        # Classify
+        if score >= 4.0:
+            quality = TrajectoryQuality.HIGH
+        elif score >= 1.0:
+            quality = TrajectoryQuality.MEDIUM
+        else:
+            quality = TrajectoryQuality.LOW
+
+        return QualityResult(
+            trajectory=trajectory,
+            quality=quality,
+            score=score,
+            reasons=reasons,
+        )
+
+    def filter(
+        self, trajectories: list[Trajectory]
+    ) -> tuple[list[QualityResult], dict[str, int]]:
+        """Assess all trajectories and return trainable ones with stats.
+
+        Returns:
+            (trainable_results, stats_dict) where stats_dict has keys
+            'total', 'high', 'medium', 'low', 'accepted'.
+        """
+        results = [self.assess(t) for t in trajectories]
+        trainable = [r for r in results if r.is_trainable]
+
+        stats = {
+            "total": len(results),
+            "high": sum(1 for r in results if r.quality == TrajectoryQuality.HIGH),
+            "medium": sum(1 for r in results if r.quality == TrajectoryQuality.MEDIUM),
+            "low": sum(1 for r in results if r.quality == TrajectoryQuality.LOW),
+            "accepted": len(trainable),
+        }
+
+        logger.info(
+            "Quality filter: %d/%d accepted (high=%d medium=%d low=%d)",
+            stats["accepted"],
+            stats["total"],
+            stats["high"],
+            stats["medium"],
+            stats["low"],
+        )
+
+        return trainable, stats

timmy_automations/retrain/retrain.py

@@ -0,0 +1,292 @@
+#!/usr/bin/env python3
+"""AutoLoRA continuous improvement loop — the sovereignty retrain script.
+
+Implements the weekly retrain cycle end-to-end:
+  Work → Record trajectories → Export weekly → Filter quality
+  → LoRA fine-tune → Load adapter → Model improves → Repeat forever
+
+Run:
+  python3 timmy_automations/retrain/retrain.py
+  python3 timmy_automations/retrain/retrain.py --dry-run
+  python3 timmy_automations/retrain/retrain.py --weeks-ago 1
+
+Epic: #1091 — Project Bannerlord
+Pipeline: AutoLoRA Sovereignty Loop (Step 6 of 7)
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import sys
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+
+# Allow running directly from repo root
+_REPO_ROOT = Path(__file__).resolve().parent.parent.parent
+if str(_REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(_REPO_ROOT))
+
+from timmy_automations.retrain.lora_trainer import LoRATrainer
+from timmy_automations.retrain.quality_filter import QualityFilter
+from timmy_automations.retrain.training_dataset import TrainingDataset
+from timmy_automations.retrain.training_log import CycleMetrics, TrainingLog
+from timmy_automations.retrain.trajectory_exporter import TrajectoryExporter
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)-8s %(name)s: %(message)s",
+    datefmt="%Y-%m-%dT%H:%M:%S",
+)
+logger = logging.getLogger("retrain")
+
+
+@dataclass
+class RetrainResult:
+    """Result of a complete retrain cycle."""
+
+    iteration: int
+    week: str
+    trajectories_exported: int
+    trajectories_accepted: int
+    examples_added: int
+    dataset_total: int
+    train_status: str
+    adapter_path: str | None
+    model_name: str | None
+    train_loss: float | None
+    duration_seconds: float
+    notes: str
+
+
+class RetrainOrchestrator:
+    """Orchestrates the complete AutoLoRA continuous improvement loop.
+
+    Step 1: Export this week's conversation trajectories from session logs
+    Step 2: Filter for high-quality exchanges
+    Step 3: Append to the training dataset
+    Step 4: Trigger LoRA fine-tune
+    Step 5: Load the new adapter (via Ollama)
+    Step 6: Log iteration, loss, skill accuracy
+    """
+
+    def __init__(
+        self,
+        base_model: str = "hermes4-14b",
+        repo_root: str | Path | None = None,
+        dry_run: bool = False,
+    ):
+        if repo_root is None:
+            repo_root = _REPO_ROOT
+        self._repo_root = Path(repo_root)
+        self._dry_run = dry_run
+
+        self.exporter = TrajectoryExporter(repo_root=self._repo_root)
+        self.quality_filter = QualityFilter()
+        self.dataset = TrainingDataset(repo_root=self._repo_root)
+        self.trainer = LoRATrainer(
+            base_model=base_model,
+            repo_root=self._repo_root,
+            dry_run=dry_run,
+        )
+        self.log = TrainingLog(repo_root=self._repo_root)
+
+    def run(self, weeks_ago: int = 1) -> RetrainResult:
+        """Execute one complete retrain cycle.
+
+        Args:
+            weeks_ago: Which week to process. 0 = current week (partial),
+                       1 = last week (default, Sunday night run), etc.
+
+        Returns:
+            RetrainResult with full cycle summary.
+        """
+        started = datetime.now(tz=UTC)
+        iteration = self.log.next_iteration()
+
+        # Determine ISO week tag
+        from datetime import timedelta
+        now = datetime.now(tz=UTC)
+        target_date = now - timedelta(weeks=weeks_ago)
+        week_tag = f"{target_date.year}-W{target_date.isocalendar().week:02d}"
+
+        logger.info(
+            "=== AutoLoRA Retrain Cycle %d | Week: %s | dry_run=%s ===",
+            iteration,
+            week_tag,
+            self._dry_run,
+        )
+
+        # Step 1: Export trajectories
+        logger.info("Step 1: Exporting trajectories for %s...", week_tag)
+        trajectories = self.exporter.export_week(weeks_ago=weeks_ago)
+        logger.info("Exported %d raw trajectories", len(trajectories))
+
+        # Step 2: Quality filter
+        logger.info("Step 2: Applying quality filter...")
+        trainable, filter_stats = self.quality_filter.filter(trajectories)
+        logger.info(
+            "Quality filter: %d/%d accepted (high=%d medium=%d low=%d)",
+            filter_stats["accepted"],
+            filter_stats["total"],
+            filter_stats["high"],
+            filter_stats["medium"],
+            filter_stats["low"],
+        )
+
+        # Step 3: Append to dataset
+        logger.info("Step 3: Appending to training dataset...")
+        append_result = self.dataset.append(trainable, week_tag)
+        logger.info(
+            "Dataset: +%d new examples (%d total)",
+            append_result.new_examples,
+            append_result.total_examples,
+        )
+
+        # Step 4: LoRA fine-tune
+        logger.info("Step 4: Triggering LoRA fine-tune (iteration=%d)...", iteration)
+        train_result = self.trainer.train(
+            dataset_path=self.dataset.dataset_path,
+            iteration=iteration,
+        )
+        logger.info(
+            "Train result: status=%s loss=%s duration=%.1fs",
+            train_result.status,
+            train_result.train_loss,
+            train_result.duration_seconds,
+        )
+
+        # Step 5 & 6: Log cycle
+        duration = (datetime.now(tz=UTC) - started).total_seconds()
+        metrics = CycleMetrics(
+            iteration=iteration,
+            week=week_tag,
+            ran_at=started.isoformat(),
+            trajectories_total=filter_stats["total"],
+            trajectories_high=filter_stats["high"],
+            trajectories_medium=filter_stats["medium"],
+            trajectories_low=filter_stats["low"],
+            trajectories_accepted=filter_stats["accepted"],
+            examples_added=append_result.new_examples,
+            dataset_total=append_result.total_examples,
+            train_status=train_result.status,
+            train_loss=train_result.train_loss,
+            train_duration_seconds=train_result.duration_seconds,
+            adapter_path=train_result.adapter_path,
+            model_name=train_result.model_name,
+            notes=train_result.message,
+        )
+        self.log.record(metrics)
+
+        result = RetrainResult(
+            iteration=iteration,
+            week=week_tag,
+            trajectories_exported=len(trajectories),
+            trajectories_accepted=filter_stats["accepted"],
+            examples_added=append_result.new_examples,
+            dataset_total=append_result.total_examples,
+            train_status=train_result.status,
+            adapter_path=train_result.adapter_path,
+            model_name=train_result.model_name,
+            train_loss=train_result.train_loss,
+            duration_seconds=duration,
+            notes=train_result.message,
+        )
+
+        logger.info(
+            "=== Cycle %d complete: status=%s examples_added=%d total=%.1fs ===",
+            iteration,
+            train_result.status,
+            append_result.new_examples,
+            duration,
+        )
+
+        return result
+
+
+def _print_result(result: RetrainResult, as_json: bool = False) -> None:
+    """Print cycle result to stdout."""
+    if as_json:
+        print(
+            json.dumps(
+                {
+                    "iteration": result.iteration,
+                    "week": result.week,
+                    "trajectories_exported": result.trajectories_exported,
+                    "trajectories_accepted": result.trajectories_accepted,
+                    "examples_added": result.examples_added,
+                    "dataset_total": result.dataset_total,
+                    "train_status": result.train_status,
+                    "adapter_path": result.adapter_path,
+                    "model_name": result.model_name,
+                    "train_loss": result.train_loss,
+                    "duration_seconds": result.duration_seconds,
+                    "notes": result.notes,
+                },
+                indent=2,
+            )
+        )
+        return
+
+    print(f"\n{'='*60}")
+    print(f"  AutoLoRA Retrain — Cycle {result.iteration}")
+    print(f"  Week: {result.week}")
+    print(f"{'='*60}")
+    print(f"  Trajectories:  {result.trajectories_exported} exported, {result.trajectories_accepted} accepted")
+    print(f"  Dataset:       +{result.examples_added} examples ({result.dataset_total} total)")
+    print(f"  Fine-tune:     {result.train_status}")
+    if result.train_loss is not None:
+        print(f"  Train loss:    {result.train_loss:.4f}")
+    if result.model_name:
+        print(f"  New model:     {result.model_name}")
+    if result.adapter_path:
+        print(f"  Adapter:       {result.adapter_path}")
+    print(f"  Duration:      {result.duration_seconds:.1f}s")
+    print(f"  Notes:         {result.notes}")
+    print(f"{'='*60}\n")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="AutoLoRA continuous improvement loop — sovereignty engine for Timmy"
+    )
+    parser.add_argument(
+        "--weeks-ago",
+        type=int,
+        default=1,
+        help="Which week to process: 0=current (partial), 1=last week (default)",
+    )
+    parser.add_argument(
+        "--base-model",
+        default="hermes4-14b",
+        help="Ollama base model name (default: hermes4-14b)",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Export and filter trajectories but skip actual fine-tuning",
+    )
+    parser.add_argument(
+        "--json",
+        action="store_true",
+        dest="as_json",
+        help="Output result as JSON",
+    )
+    args = parser.parse_args()
+
+    orchestrator = RetrainOrchestrator(
+        base_model=args.base_model,
+        dry_run=args.dry_run,
+    )
+    result = orchestrator.run(weeks_ago=args.weeks_ago)
+    _print_result(result, as_json=args.as_json)
+
+    # Exit 0 even on skipped/failed training — the loop must continue
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

timmy_automations/retrain/training_dataset.py

@@ -0,0 +1,180 @@
+"""Training dataset manager — appends filtered trajectories to a JSONL training file.
+
+Maintains a growing dataset of high-quality conversation examples in the
+chat-format expected by mlx-lm / HuggingFace fine-tuning pipelines.
+
+Output format (one JSON object per line):
+  {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+
+from timmy_automations.retrain.quality_filter import QualityResult
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_DATASET_PATH = ".loop/retrain/training_data.jsonl"
+_DEFAULT_INDEX_PATH = ".loop/retrain/dataset_index.json"
+
+
+@dataclass
+class AppendResult:
+    """Result of appending trajectories to the training dataset."""
+
+    new_examples: int
+    total_examples: int
+    dataset_path: str
+    week_tag: str
+
+
+class TrainingDataset:
+    """Manages the LoRA training dataset file.
+
+    Each entry is a chat-format example:
+      {"messages": [...], "week": "2026-W12", "quality": "high", "added_at": "..."}
+    """
+
+    def __init__(
+        self,
+        dataset_path: str | Path | None = None,
+        index_path: str | Path | None = None,
+        repo_root: str | Path | None = None,
+    ):
+        if repo_root is None:
+            repo_root = Path(__file__).resolve().parent.parent.parent
+        self._repo_root = Path(repo_root)
+
+        self._dataset_path = self._repo_root / (
+            dataset_path or _DEFAULT_DATASET_PATH
+        )
+        self._index_path = self._repo_root / (
+            index_path or _DEFAULT_INDEX_PATH
+        )
+
+        self._dataset_path.parent.mkdir(parents=True, exist_ok=True)
+
+    @property
+    def dataset_path(self) -> Path:
+        return self._dataset_path
+
+    def count(self) -> int:
+        """Return the number of examples currently in the dataset."""
+        if not self._dataset_path.exists():
+            return 0
+        count = 0
+        with open(self._dataset_path) as f:
+            for line in f:
+                if line.strip():
+                    count += 1
+        return count
+
+    def append(
+        self, quality_results: list[QualityResult], week_tag: str
+    ) -> AppendResult:
+        """Append high-quality trajectories to the training dataset.
+
+        Deduplicates by (week_tag, session_date, started_at) so re-running
+        the export for the same week is idempotent.
+
+        Args:
+            quality_results: Filtered, trainable quality results.
+            week_tag: ISO week string e.g. "2026-W12".
+
+        Returns:
+            AppendResult with counts.
+        """
+        existing_keys = self._load_existing_keys()
+        new_count = 0
+        added_at = datetime.now(tz=UTC).isoformat()
+
+        with open(self._dataset_path, "a") as f:
+            for result in quality_results:
+                traj = result.trajectory
+                dedup_key = (
+                    f"{week_tag}|{traj.session_date}|{traj.started_at}"
+                )
+                if dedup_key in existing_keys:
+                    logger.debug("Skipping duplicate trajectory: %s", dedup_key)
+                    continue
+
+                chat_messages = traj.to_chat_format()
+                if len(chat_messages) < 2:
+                    logger.debug(
+                        "Skipping trajectory with %d chat messages (need ≥2)",
+                        len(chat_messages),
+                    )
+                    continue
+
+                record = {
+                    "messages": chat_messages,
+                    "week": week_tag,
+                    "quality": result.quality.value,
+                    "score": result.score,
+                    "session_date": traj.session_date,
+                    "started_at": traj.started_at,
+                    "tool_calls": traj.tool_call_count,
+                    "added_at": added_at,
+                }
+                f.write(json.dumps(record) + "\n")
+                existing_keys.add(dedup_key)
+                new_count += 1
+
+        total = self.count()
+        self._update_index(week_tag, new_count, total)
+        logger.info(
+            "Dataset: appended %d new examples (total=%d)", new_count, total
+        )
+
+        return AppendResult(
+            new_examples=new_count,
+            total_examples=total,
+            dataset_path=str(self._dataset_path),
+            week_tag=week_tag,
+        )
+
+    def _load_existing_keys(self) -> set[str]:
+        """Load deduplication keys from the existing dataset."""
+        keys: set[str] = set()
+        if not self._dataset_path.exists():
+            return keys
+        with open(self._dataset_path) as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    record = json.loads(line)
+                    week = record.get("week", "")
+                    session_date = record.get("session_date", "")
+                    started_at = record.get("started_at", "")
+                    keys.add(f"{week}|{session_date}|{started_at}")
+                except json.JSONDecodeError:
+                    continue
+        return keys
+
+    def _update_index(self, week_tag: str, new_count: int, total: int) -> None:
+        """Update the dataset index JSON with latest run metadata."""
+        index: dict = {}
+        if self._index_path.exists():
+            try:
+                index = json.loads(self._index_path.read_text())
+            except (json.JSONDecodeError, OSError):
+                index = {}
+
+        index.setdefault("weeks", {})
+        index["weeks"][week_tag] = {
+            "examples_added": new_count,
+            "updated_at": datetime.now(tz=UTC).isoformat(),
+        }
+        index["total_examples"] = total
+        index["last_updated"] = datetime.now(tz=UTC).isoformat()
+
+        self._index_path.write_text(json.dumps(index, indent=2))

timmy_automations/retrain/training_log.py

@@ -0,0 +1,183 @@
+"""Training log — records each fine-tune cycle with metrics and skill deltas.
+
+Writes to .loop/retrain/training_log.jsonl (one entry per cycle) and
+maintains a human-readable .loop/retrain/training_log.md summary.
+
+Each log entry captures:
+- Iteration count
+- Week processed
+- Quality filter stats
+- Examples added to dataset
+- LoRA train result (loss, duration, adapter path)
+- Skill accuracy deltas (from smoke tests)
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import asdict, dataclass, field
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_LOG_PATH = ".loop/retrain/training_log.jsonl"
+_DEFAULT_SUMMARY_PATH = ".loop/retrain/training_log.md"
+
+
+@dataclass
+class CycleMetrics:
+    """Metrics for a single retrain cycle."""
+
+    iteration: int
+    week: str
+    ran_at: str
+
+    # Quality filter
+    trajectories_total: int = 0
+    trajectories_high: int = 0
+    trajectories_medium: int = 0
+    trajectories_low: int = 0
+    trajectories_accepted: int = 0
+
+    # Dataset
+    examples_added: int = 0
+    dataset_total: int = 0
+
+    # Training
+    train_status: str = "skipped"
+    train_loss: float | None = None
+    train_duration_seconds: float = 0.0
+    adapter_path: str | None = None
+    model_name: str | None = None
+
+    # Skill accuracy (optional, from smoke tests)
+    skill_accuracy: dict[str, float] = field(default_factory=dict)
+    skill_delta: dict[str, float] = field(default_factory=dict)
+
+    # Human-readable summary
+    notes: str = ""
+
+
+class TrainingLog:
+    """Persistent log of all retrain cycles."""
+
+    def __init__(
+        self,
+        log_path: str | Path | None = None,
+        summary_path: str | Path | None = None,
+        repo_root: str | Path | None = None,
+    ):
+        if repo_root is None:
+            repo_root = Path(__file__).resolve().parent.parent.parent
+        self._repo_root = Path(repo_root)
+
+        self._log_path = self._repo_root / (log_path or _DEFAULT_LOG_PATH)
+        self._summary_path = self._repo_root / (summary_path or _DEFAULT_SUMMARY_PATH)
+        self._log_path.parent.mkdir(parents=True, exist_ok=True)
+
+    @property
+    def log_path(self) -> Path:
+        return self._log_path
+
+    def next_iteration(self) -> int:
+        """Return the next iteration number (1-indexed)."""
+        entries = self.load_all()
+        if not entries:
+            return 1
+        return max(e.get("iteration", 0) for e in entries) + 1
+
+    def record(self, metrics: CycleMetrics) -> None:
+        """Append a cycle metrics record to the log."""
+        entry = asdict(metrics)
+        with open(self._log_path, "a") as f:
+            f.write(json.dumps(entry) + "\n")
+
+        self._update_summary(metrics)
+        logger.info(
+            "Training log: iteration=%d week=%s status=%s examples_added=%d",
+            metrics.iteration,
+            metrics.week,
+            metrics.train_status,
+            metrics.examples_added,
+        )
+
+    def load_all(self) -> list[dict[str, Any]]:
+        """Load all cycle records from the log."""
+        if not self._log_path.exists():
+            return []
+        entries: list[dict[str, Any]] = []
+        with open(self._log_path) as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    entries.append(json.loads(line))
+                except json.JSONDecodeError:
+                    logger.debug("Skipping malformed log entry")
+        return entries
+
+    def latest(self) -> dict[str, Any] | None:
+        """Return the most recent cycle record."""
+        entries = self.load_all()
+        return entries[-1] if entries else None
+
+    def _update_summary(self, metrics: CycleMetrics) -> None:
+        """Rewrite the markdown summary with all cycles."""
+        all_entries = self.load_all()
+
+        lines = [
+            "# AutoLoRA Training Log\n",
+            f"*Updated: {datetime.now(tz=UTC).isoformat()}*\n",
+            f"*Total iterations: {len(all_entries)}*\n",
+            "",
+            "## Cycles\n",
+            "| # | Week | Status | Loss | Examples | Duration |",
+            "|---|------|--------|------|----------|----------|",
+        ]
+
+        for entry in reversed(all_entries[-20:]):  # Last 20 cycles
+            loss = f"{entry.get('train_loss', 0.0) or 0.0:.4f}" if entry.get("train_loss") else "—"
+            lines.append(
+                f"| {entry.get('iteration', '?')} "
+                f"| {entry.get('week', '?')} "
+                f"| {entry.get('train_status', '?')} "
+                f"| {loss} "
+                f"| +{entry.get('examples_added', 0)} ({entry.get('dataset_total', 0)} total) "
+                f"| {entry.get('train_duration_seconds', 0.0):.0f}s |"
+            )
+
+        lines.append("")
+        lines.append("## Skill Accuracy Over Time\n")
+
+        # Collect all unique skills
+        all_skills: set[str] = set()
+        for entry in all_entries:
+            all_skills.update(entry.get("skill_accuracy", {}).keys())
+
+        if all_skills:
+            skill_header = "| # | Week | " + " | ".join(sorted(all_skills)) + " |"
+            skill_sep = "|---|------|" + "|".join("---" for _ in all_skills) + "|"
+            lines.extend([skill_header, skill_sep])
+            for entry in reversed(all_entries[-10:]):
+                acc = entry.get("skill_accuracy", {})
+                row = f"| {entry.get('iteration', '?')} | {entry.get('week', '?')} | "
+                row += " | ".join(
+                    f"{acc.get(s, 0.0):.0%}" if s in acc else "—"
+                    for s in sorted(all_skills)
+                )
+                row += " |"
+                lines.append(row)
+        else:
+            lines.append("*No skill accuracy data yet — run smoke tests after fine-tuning.*")
+
+        lines.append("")
+        if metrics.notes:
+            lines.append(f"## Latest Notes\n\n{metrics.notes}\n")
+
+        self._summary_path.write_text("\n".join(lines))

timmy_automations/retrain/trajectory_exporter.py

@@ -0,0 +1,255 @@
+"""Trajectory exporter — reads session JSONL logs and extracts conversation trajectories.
+
+A trajectory is a coherent sequence of messages + tool calls that form
+a single task attempt.  Each trajectory becomes one training example.
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass, field
+from datetime import UTC, datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_LOGS_DIR_DEFAULT = "logs"
+_SESSION_GLOB = "session_*.jsonl"
+
+
+@dataclass
+class Trajectory:
+    """A single conversation trajectory extracted from session logs."""
+
+    session_date: str
+    started_at: str
+    ended_at: str
+    messages: list[dict[str, Any]] = field(default_factory=list)
+    tool_calls: list[dict[str, Any]] = field(default_factory=list)
+    errors: list[dict[str, Any]] = field(default_factory=list)
+    decisions: list[dict[str, Any]] = field(default_factory=list)
+
+    @property
+    def message_count(self) -> int:
+        return len(self.messages)
+
+    @property
+    def tool_call_count(self) -> int:
+        return len(self.tool_calls)
+
+    @property
+    def error_count(self) -> int:
+        return len(self.errors)
+
+    @property
+    def has_successful_tool_call(self) -> bool:
+        """True if any tool call succeeded (no error entry follows it)."""
+        return self.tool_call_count > 0 and self.error_count == 0
+
+    @property
+    def is_multi_step(self) -> bool:
+        """True if this trajectory involved multiple turns with tool use."""
+        return self.message_count >= 2 and self.tool_call_count >= 1
+
+    def to_chat_format(self) -> list[dict[str, str]]:
+        """Convert trajectory to chat-format messages for training.
+
+        Interleaves messages and tool-call results as assistant/tool turns.
+        """
+        chat: list[dict[str, str]] = []
+        # Merge all entries by timestamp and emit in order
+        all_entries = sorted(
+            self.messages + self.tool_calls + self.decisions,
+            key=lambda e: e.get("timestamp", ""),
+        )
+        for entry in all_entries:
+            etype = entry.get("type")
+            if etype == "message":
+                role = "user" if entry.get("role") == "user" else "assistant"
+                content = entry.get("content", "")
+                if content:
+                    chat.append({"role": role, "content": content})
+            elif etype == "tool_call":
+                tool = entry.get("tool", "unknown")
+                result = entry.get("result", "")
+                chat.append(
+                    {
+                        "role": "assistant",
+                        "content": f"[tool:{tool}] {result}",
+                    }
+                )
+            elif etype == "decision":
+                decision = entry.get("decision", "")
+                if decision:
+                    chat.append({"role": "assistant", "content": f"[decided] {decision}"})
+        return chat
+
+
+class TrajectoryExporter:
+    """Reads session JSONL logs and yields Trajectory objects for a date range."""
+
+    def __init__(self, logs_dir: str | Path | None = None, repo_root: str | Path | None = None):
+        if repo_root is None:
+            repo_root = Path(__file__).resolve().parent.parent.parent
+        self._repo_root = Path(repo_root)
+
+        if logs_dir is None:
+            self._logs_dir = self._repo_root / _LOGS_DIR_DEFAULT
+        else:
+            self._logs_dir = Path(logs_dir)
+
+    def export_week(self, weeks_ago: int = 0) -> list[Trajectory]:
+        """Export all trajectories from the specified week.
+
+        Args:
+            weeks_ago: 0 = current week, 1 = last week, etc.
+
+        Returns:
+            List of Trajectory objects extracted from session logs.
+        """
+        now = datetime.now(tz=UTC)
+        # Week boundaries: Mon–Sun
+        days_since_monday = now.weekday()
+        week_start = (now - timedelta(days=days_since_monday + 7 * weeks_ago)).replace(
+            hour=0, minute=0, second=0, microsecond=0
+        )
+        week_end = week_start + timedelta(days=7)
+
+        logger.info(
+            "Exporting trajectories for week %s–%s",
+            week_start.date().isoformat(),
+            week_end.date().isoformat(),
+        )
+
+        trajectories: list[Trajectory] = []
+        log_files = sorted(self._logs_dir.glob(_SESSION_GLOB))
+
+        for log_file in log_files:
+            # Parse date from filename: session_YYYY-MM-DD.jsonl
+            try:
+                date_str = log_file.stem.removeprefix("session_")
+                file_date = datetime.strptime(date_str, "%Y-%m-%d").replace(tzinfo=UTC)
+            except ValueError:
+                logger.debug("Skipping non-date session file: %s", log_file.name)
+                continue
+
+            if not (week_start <= file_date < week_end):
+                continue
+
+            file_trajectories = self._extract_from_file(log_file)
+            trajectories.extend(file_trajectories)
+            logger.info(
+                "Extracted %d trajectories from %s", len(file_trajectories), log_file.name
+            )
+
+        logger.info("Total trajectories exported: %d", len(trajectories))
+        return trajectories
+
+    def _extract_from_file(self, log_file: Path) -> list[Trajectory]:
+        """Parse a single session JSONL file into trajectories.
+
+        Groups entries into trajectories by finding natural conversation
+        boundaries (gaps of inactivity or topic shifts in the message stream).
+        """
+        entries: list[dict[str, Any]] = []
+        try:
+            with open(log_file) as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        entries.append(json.loads(line))
+                    except json.JSONDecodeError:
+                        logger.debug("Skipping malformed JSON line in %s", log_file.name)
+        except OSError as exc:
+            logger.warning("Could not read %s: %s", log_file, exc)
+            return []
+
+        if not entries:
+            return []
+
+        date_str = log_file.stem.removeprefix("session_")
+        return self._segment_trajectories(entries, date_str)
+
+    def _segment_trajectories(
+        self, entries: list[dict[str, Any]], session_date: str
+    ) -> list[Trajectory]:
+        """Split a flat list of session entries into discrete trajectories.
+
+        Segmentation rule: start a new trajectory when:
+        - A user message follows a Timmy message (new conversation turn)
+        - More than 5 minutes have elapsed between entries
+
+        This produces training examples that are coherent task attempts.
+        """
+        if not entries:
+            return []
+
+        trajectories: list[Trajectory] = []
+        current_entries: list[dict[str, Any]] = []
+        prev_ts: datetime | None = None
+        _SEGMENT_GAP_MINUTES = 5
+
+        def _flush() -> None:
+            if current_entries:
+                traj = _build_trajectory(current_entries, session_date)
+                if traj.message_count > 0:
+                    trajectories.append(traj)
+
+        for entry in entries:
+            ts_raw = entry.get("timestamp", "")
+            try:
+                ts = datetime.fromisoformat(ts_raw.replace("Z", "+00:00"))
+            except (ValueError, AttributeError):
+                ts = None
+
+            # Time-gap segmentation
+            if ts and prev_ts and (ts - prev_ts).total_seconds() > _SEGMENT_GAP_MINUTES * 60:
+                _flush()
+                current_entries = []
+
+            # New-turn segmentation: user message after assistant turn
+            etype = entry.get("type")
+            erole = entry.get("role")
+            if etype == "message" and erole == "user" and current_entries:
+                # Check if previous non-error entry was a Timmy message
+                for prev in reversed(current_entries):
+                    if prev.get("type") == "message":
+                        if prev.get("role") == "timmy":
+                            _flush()
+                            current_entries = []
+                        break
+
+            current_entries.append(entry)
+            if ts:
+                prev_ts = ts
+
+        _flush()
+        return trajectories
+
+
+def _build_trajectory(entries: list[dict[str, Any]], session_date: str) -> Trajectory:
+    """Build a Trajectory from a flat list of entries."""
+    messages = [e for e in entries if e.get("type") == "message"]
+    tool_calls = [e for e in entries if e.get("type") == "tool_call"]
+    errors = [e for e in entries if e.get("type") == "error"]
+    decisions = [e for e in entries if e.get("type") == "decision"]
+
+    timestamps = [e.get("timestamp", "") for e in entries if e.get("timestamp")]
+    started_at = min(timestamps) if timestamps else ""
+    ended_at = max(timestamps) if timestamps else ""
+
+    return Trajectory(
+        session_date=session_date,
+        started_at=started_at,
+        ended_at=ended_at,
+        messages=messages,
+        tool_calls=tool_calls,
+        errors=errors,
+        decisions=decisions,
+    )