diff --git a/GENOME.md b/GENOME.md
index 8863c65..362b0eb 100644
--- a/GENOME.md
+++ b/GENOME.md
@@ -1,16 +1,16 @@
 # GENOME.md — compounding-intelligence
 
-*Auto-generated codebase genome. Addresses timmy-home#676.*
+**Generated:** 2026-04-17
+**Repo:** Timmy_Foundation/compounding-intelligence
+**Description:** Turn 1B+ daily agent tokens into durable, compounding fleet intelligence.
 
 ---
 
 ## Project Overview
 
-**What:** A system that turns 1B+ daily agent tokens into durable, compounding fleet intelligence.
+Every agent session starts at zero. The same HTTP 405 gets rediscovered as a branch protection issue. The same token path gets searched from scratch. Intelligence evaporates when the session ends.
 
-**Why:** Every agent session starts at zero. The same mistakes get made repeatedly — the same HTTP 405 is rediscovered as a branch protection issue, the same token path is searched for from scratch. Intelligence evaporates when the session ends.
-
-**How:** Three pipelines form a compounding loop:
+Compounding-intelligence solves this with three pipelines forming a loop:
 
 ```
 SESSION ENDS → HARVESTER → KNOWLEDGE STORE → BOOTSTRAPPER → NEW SESSION STARTS SMARTER
@@ -18,222 +18,234 @@ SESSION ENDS → HARVESTER → KNOWLEDGE STORE → BOOTSTRAPPER → NEW SESSION
                          MEASURER → Prove it's working
 ```
 
-**Status:** Early stage. Template and test scaffolding exist. Core pipeline scripts (harvester.py, bootstrapper.py, measurer.py, session_reader.py) are planned but not yet implemented. The knowledge extraction prompt is complete and validated.
-
----
+**Status:** Active development. Core pipelines implemented. 20+ scripts, 14 test files, knowledge store populated with real data.
 
 ## Architecture
 
 ```mermaid
 graph TD
-    A[Session Transcript<br/>.jsonl] --> B[Harvester]
-    B --> C{Extract Knowledge}
-    C --> D[knowledge/index.json]
-    C --> E[knowledge/global/*.md]
-    C --> F[knowledge/repos/{repo}.md]
-    C --> G[knowledge/agents/{agent}.md]
-    D --> H[Bootstrapper]
-    H --> I[Bootstrap Context<br/>2k token injection]
-    I --> J[New Session<br/>starts smarter]
-    J --> A
-    D --> K[Measurer]
-    K --> L[metrics/dashboard.md]
-    K --> M[Velocity / Hit Rate<br/>Error Reduction]
+    TRANS[Session Transcripts<br/>~/.hermes/sessions/*.jsonl] --> READER[session_reader.py]
+    READER --> HARVESTER[harvester.py]
+    HARVESTER -->|LLM extraction| PROMPT[harvest-prompt.md]
+    HARVESTER --> DEDUP[deduplicate()]
+    DEDUP --> INDEX[knowledge/index.json]
+    DEDUP --> GLOBAL[knowledge/global/*.yaml]
+    DEDUP --> REPO[knowledge/repos/*.yaml]
+
+    INDEX --> BOOTSTRAPPER[bootstrapper.py]
+    BOOTSTRAPPER -->|filter + rank + truncate| CONTEXT[Bootstrap Context<br/>2k token injection]
+    CONTEXT --> SESSION[New Session starts smarter]
+
+    INDEX --> VALIDATOR[validate_knowledge.py]
+    INDEX --> STALENESS[knowledge_staleness_check.py]
+    INDEX --> GAPS[knowledge_gap_identifier.py]
+
+    TRANS --> SAMPLER[sampler.py]
+    SAMPLER -->|score + rank| BEST[High-value sessions]
+    BEST --> HARVESTER
+
+    TRANS --> METADATA[session_metadata.py]
+    METADATA --> SUMMARY[SessionSummary objects]
+
+    KNOWLEDGE --> DIFF[diff_analyzer.py]
+    DIFF --> PROPOSALS[improvement_proposals.py]
+    PROPOSALS --> PRIORITIES[priority_rebalancer.py]
 ```
 
-### Pipeline 1: Harvester
+## Entry Points
 
-**Status:** Prompt designed. Script not implemented.
+### Core Pipelines
 
-Reads finished session transcripts (JSONL). Uses `templates/harvest-prompt.md` to extract durable knowledge into five categories:
+| Script | Purpose | Key Functions |
+|--------|---------|---------------|
+| `harvester.py` | Extract knowledge from session transcripts | `harvest_session()`, `call_llm()`, `deduplicate()`, `validate_fact()` |
+| `bootstrapper.py` | Build pre-session context from knowledge store | `build_bootstrap_context()`, `filter_facts()`, `sort_facts()`, `truncate_to_tokens()` |
+| `session_reader.py` | Parse JSONL session transcripts | `read_session()`, `extract_conversation()`, `messages_to_text()` |
+| `sampler.py` | Score and rank sessions for harvesting value | `scan_session_fast()`, `score_session()` |
+| `session_metadata.py` | Extract structured metadata from sessions | `extract_session_metadata()`, `SessionSummary` |
 
-| Category | Description | Example |
-|----------|-------------|---------|
-| `fact` | Concrete, verifiable information | "Repository X has 5 files" |
-| `pitfall` | Errors encountered, wrong assumptions | "Token is at ~/.config/gitea/token, not env var" |
-| `pattern` | Successful action sequences | "Deploy: test → build → push → webhook" |
-| `tool-quirk` | Environment-specific behaviors | "URL format requires trailing slash" |
-| `question` | Identified but unanswered | "Need optimal batch size for harvesting" |
+### Analysis & Quality
 
-Output schema per knowledge item:
-```json
-{
-  "fact": "One sentence description",
-  "category": "fact|pitfall|pattern|tool-quirk|question",
-  "repo": "repo-name or 'global'",
-  "confidence": 0.0-1.0
-}
-```
+| Script | Purpose |
+|--------|---------|
+| `validate_knowledge.py` | Validate knowledge index schema compliance |
+| `knowledge_staleness_check.py` | Detect stale knowledge (source changed since extraction) |
+| `knowledge_gap_identifier.py` | Find untested functions, undocumented APIs, missing tests |
+| `diff_analyzer.py` | Analyze code diffs for improvement signals |
+| `improvement_proposals.py` | Generate ranked improvement proposals |
+| `priority_rebalancer.py` | Rebalance priorities across proposals |
+| `automation_opportunity_finder.py` | Find manual steps that can be automated |
+| `dead_code_detector.py` | Detect unused code |
+| `dependency_graph.py` | Map dependency relationships |
+| `perf_bottleneck_finder.py` | Find performance bottlenecks |
+| `refactoring_opportunity_finder.py` | Identify refactoring targets |
+| `gitea_issue_parser.py` | Parse Gitea issues for knowledge extraction |
 
-### Pipeline 2: Bootstrapper
+### Automation
 
-**Status:** Not implemented.
+| Script | Purpose |
+|--------|---------|
+| `session_pair_harvester.py` | Extract training pairs from sessions |
 
-Queries knowledge store before session start. Assembles a compact 2k-token context from relevant facts. Injects into session startup so the agent begins with full situational awareness.
-
-### Pipeline 3: Measurer
-
-**Status:** Not implemented.
-
-Tracks compounding metrics: knowledge velocity (facts/day), error reduction (%), hit rate (knowledge used / knowledge available), task completion improvement.
-
----
-
-## Directory Structure
+## Data Flow
 
 ```
-compounding-intelligence/
-├── README.md                           # Project overview and architecture
-├── GENOME.md                           # This file (codebase genome)
-├── knowledge/                          # [PLANNED] Knowledge store
-│   ├── index.json                      # Machine-readable fact index
-│   ├── global/                         # Cross-repo knowledge
-│   ├── repos/{repo}.md                 # Per-repo knowledge
-│   └── agents/{agent}.md               # Agent-type notes
-├── scripts/
-│   ├── test_harvest_prompt.py          # Basic prompt validation (2.5KB)
-│   └── test_harvest_prompt_comprehensive.py  # Full prompt structure test (6.8KB)
-├── templates/
-│   └── harvest-prompt.md               # Knowledge extraction prompt (3.5KB)
-├── test_sessions/
-│   ├── session_success.jsonl           # Happy path test data
-│   ├── session_failure.jsonl           # Failure path test data
-│   ├── session_partial.jsonl           # Incomplete session test data
-│   ├── session_patterns.jsonl          # Pattern extraction test data
-│   └── session_questions.jsonl         # Question identification test data
-└── metrics/                            # [PLANNED] Compounding metrics
-    └── dashboard.md
+1. Session ends → .jsonl written to ~/.hermes/sessions/
+2. sampler.py scores sessions by age, recency, repo coverage
+3. harvester.py reads top sessions, calls LLM with harvest-prompt.md
+4. LLM extracts facts/pitfalls/patterns/quirks/questions
+5. deduplicate() checks against existing index via fact_fingerprint()
+6. validate_fact() checks schema compliance
+7. write_knowledge() appends to knowledge/index.json + per-repo YAML
+8. On next session start, bootstrapper.py:
+   a. Loads knowledge/index.json
+   b. Filters by session's repo and agent type
+   c. Sorts by confidence (high first), then recency
+   d. Truncates to 2k token budget
+   e. Injects as pre-context
+9. Agent starts with full situational awareness instead of zero
 ```
 
----
-
-## Entry Points and Data Flow
-
-### Entry Point 1: Knowledge Extraction (Harvester)
-
-```
-Input:  Session transcript (JSONL)
-        ↓
-        templates/harvest-prompt.md (LLM prompt)
-        ↓
-        Knowledge items (JSON array)
-        ↓
-Output: knowledge/index.json + per-repo/per-agent markdown files
-```
-
-### Entry Point 2: Session Bootstrap (Bootstrapper)
-
-```
-Input:  Session context (repo, agent type, task type)
-        ↓
-        knowledge/index.json (query relevant facts)
-        ↓
-        2k-token bootstrap context
-        ↓
-Output: Injected into session startup
-```
-
-### Entry Point 3: Measurement (Measurer)
-
-```
-Input:  knowledge/index.json + session history
-        ↓
-        Velocity, hit rate, error reduction calculations
-        ↓
-Output: metrics/dashboard.md
-```
-
----
-
 ## Key Abstractions
 
-### Knowledge Item
-The atomic unit. One sentence, one category, one confidence score. Designed to be small enough that 1000 items fit in a 2k-token bootstrap context.
+### Knowledge Item (fact/pitfall/pattern/quirk/question)
+```json
+{
+  "fact": "Gitea token is at ~/.config/gitea/token",
+  "category": "tool-quirk",
+  "repo": "global",
+  "confidence": 0.9,
+  "evidence": "Found during clone attempt",
+  "source_session": "2026-04-13_abc123",
+  "extracted_at": "2026-04-13T20:00:00Z"
+}
+```
 
-### Knowledge Store
-A directory structure that mirrors the fleet's mental model:
-- `global/` — knowledge that applies everywhere (tool quirks, environment facts)
-- `repos/` — knowledge specific to each repo
-- `agents/` — knowledge specific to each agent type
+### SessionSummary (session_metadata.py)
+Extracted metadata per session: duration, token count, tools used, repos touched, error count, outcome.
 
-### Confidence Score
-0.0–1.0 scale. Defines how certain the harvester is about each extracted fact:
-- 0.9–1.0: Explicitly stated with verification
-- 0.7–0.8: Clearly implied by multiple data points
-- 0.5–0.6: Suggested but not fully verified
-- 0.3–0.4: Inferred from limited data
-- 0.1–0.2: Speculative or uncertain
+### Gap / GapReport (knowledge_gap_identifier.py)
+Structured gap analysis: untested functions, undocumented APIs, missing tests. Severity: critical/high/medium/low.
 
-### Bootstrap Context
-The 2k-token injection that a new session receives. Assembled from the most relevant knowledge items for the current task, filtered by confidence > 0.7, deduplicated, and compressed.
+### Knowledge Index (knowledge/index.json)
+Machine-readable fact store. 12KB, populated with real data. Categories: fact, pitfall, pattern, tool-quirk, question.
 
----
+## Knowledge Store
+
+```
+knowledge/
+├── index.json              # Master fact store (12KB, populated)
+├── SCHEMA.md               # Schema documentation
+├── global/
+│   ├── pitfalls.yaml       # Cross-repo pitfalls (2KB)
+│   └── tool-quirks.yaml    # Tool-specific quirks (2KB)
+├── repos/
+│   ├── hermes-agent.yaml   # hermes-agent knowledge (2KB)
+│   └── the-nexus.yaml      # the-nexus knowledge (2KB)
+└── agents/                 # Per-agent knowledge (empty)
+```
 
 ## API Surface
 
-### Internal (scripts not yet implemented)
+### LLM API (consumed)
+| Provider | Endpoint | Usage |
+|----------|----------|-------|
+| Nous Research | `https://inference-api.nousresearch.com/v1` | Knowledge extraction |
+| Ollama | `http://localhost:11434/v1` | Local fallback |
 
-| Script | Input | Output | Status |
-|--------|-------|--------|--------|
-| `harvester.py` | Session JSONL path | Knowledge items JSON | PLANNED |
-| `bootstrapper.py` | Repo + agent type | 2k-token context string | PLANNED |
-| `measurer.py` | Knowledge store path | Metrics JSON | PLANNED |
-| `session_reader.py` | Session JSONL path | Parsed transcript | PLANNED |
-
-### Prompt (templates/harvest-prompt.md)
-
-The extraction prompt is the core "API." It takes a session transcript and returns structured JSON. It defines:
-- Five extraction categories
-- Output format (JSON array of knowledge items)
-- Confidence scoring rubric
-- Constraints (no hallucination, specificity, relevance, brevity)
-- Example input/output pair
-
----
+### File API (consumed/produced)
+| Path | Format | Direction |
+|------|--------|-----------|
+| `~/.hermes/sessions/*.jsonl` | JSONL | Input (session transcripts) |
+| `knowledge/index.json` | JSON | Output (master fact store) |
+| `knowledge/global/*.yaml` | YAML | Output (cross-repo knowledge) |
+| `knowledge/repos/*.yaml` | YAML | Output (per-repo knowledge) |
+| `templates/harvest-prompt.md` | Markdown | Config (extraction prompt) |
 
 ## Test Coverage
 
-### What Exists
+**14 test files** covering core pipelines:
 
-| File | Tests | Coverage |
-|------|-------|----------|
-| `scripts/test_harvest_prompt.py` | 2 tests | Prompt file existence, sample transcript |
-| `scripts/test_harvest_prompt_comprehensive.py` | 5 tests | Prompt structure, categories, fields, confidence scoring, size limits |
-| `test_sessions/*.jsonl` | 5 sessions | Success, failure, partial, patterns, questions |
+| Test File | Covers |
+|-----------|--------|
+| `test_harvest_prompt.py` | Prompt validation, hallucination detection |
+| `test_harvest_prompt_comprehensive.py` | Extended prompt testing |
+| `test_harvester_pipeline.py` | Harvester extraction + dedup |
+| `test_bootstrapper.py` | Context building, filtering, truncation |
+| `test_session_pair_harvester.py` | Training pair extraction |
+| `test_improvement_proposals.py` | Proposal generation |
+| `test_priority_rebalancer.py` | Priority scoring |
+| `test_knowledge_staleness.py` | Staleness detection |
+| `test_automation_opportunity_finder.py` | Automation detection |
+| `test_diff_analyzer.py` | Diff analysis |
+| `test_gitea_issue_parser.py` | Issue parsing |
+| `test_refactoring_opportunity_finder.py` | Refactoring signals |
+| `test_knowledge_gap_identifier.py` | Gap analysis |
+| `test_perf_bottleneck_finder.py` | Perf bottleneck detection |
 
-### What's Missing
+### Coverage Gaps
 
-1. **Harvester integration test** — Does the prompt actually extract correct knowledge from real transcripts?
-2. **Bootstrapper test** — Does it assemble relevant context correctly?
-3. **Knowledge store test** — Does the index.json maintain consistency?
-4. **Confidence calibration test** — Do high-confidence facts actually prove true in later sessions?
-5. **Deduplication test** — Are duplicate facts across sessions handled?
-6. **Staleness test** — How does the system handle outdated knowledge?
-
----
+1. **session_reader.py** — No dedicated test file (tested indirectly)
+2. **sampler.py** — No test file (scoring logic untested)
+3. **session_metadata.py** — No test file
+4. **validate_knowledge.py** — No test file
+5. **knowledge_staleness_check.py** — Tested but limited
 
 ## Security Considerations
 
-1. **No secrets in knowledge store** — The harvester must filter out API keys, tokens, and credentials from extracted facts. The prompt constraints mention this but there is no automated guard.
+### API Key Handling
+- `harvester.py` reads API key from `~/.hermes/auth.json` or env vars
+- Key passed to LLM API in request headers only
+- No key logging
 
-2. **Knowledge poisoning** — A malicious or corrupted session could inject false facts. Confidence scoring partially mitigates this, but there is no verification step.
+### Knowledge Integrity
+- `validate_fact()` checks schema before writing
+- `deduplicate()` prevents duplicate entries via fingerprint
+- `knowledge_staleness_check.py` detects when source code changed but knowledge didn't
+- Confidence scores prevent low-quality knowledge from polluting the store
 
-3. **Access control** — The knowledge store has no access control. Any process that can read the directory can read all facts. In a multi-tenant setup, this is a concern.
+### File Safety
+- Knowledge writes are append-only (never deletes)
+- Bootstrap context is truncated to budget (no prompt injection via knowledge)
+- Session reader handles malformed JSONL gracefully
 
-4. **Transcript privacy** — Session transcripts may contain user data. The harvester must not extract personally identifiable information into the knowledge store.
+## File Index
+
+```
+scripts/
+  harvester.py                          (473 lines) — Core knowledge extraction
+  bootstrapper.py                       (302 lines) — Pre-session context builder
+  session_reader.py                     (137 lines) — JSONL session parser
+  sampler.py                            (363 lines) — Session scoring + ranking
+  session_metadata.py                   (271 lines) — Session metadata extraction
+  validate_knowledge.py                  (44 lines) — Index validation
+  knowledge_staleness_check.py          (125 lines) — Staleness detection
+  knowledge_gap_identifier.py           (291 lines) — Gap analysis engine
+  diff_analyzer.py                      (203 lines) — Diff analysis
+  improvement_proposals.py              (518 lines) — Proposal generation
+  priority_rebalancer.py                (745 lines) — Priority scoring
+  automation_opportunity_finder.py      (600 lines) — Automation detection
+  dead_code_detector.py                 (270 lines) — Dead code detection
+  dependency_graph.py                   (220 lines) — Dependency mapping
+  perf_bottleneck_finder.py             (635 lines) — Perf analysis
+  refactoring_opportunity_finder.py      (46 lines) — Refactoring signals
+  gitea_issue_parser.py                 (140 lines) — Gitea issue parsing
+  session_pair_harvester.py             (224 lines) — Training pair extraction
+knowledge/
+  index.json                             (12KB)    — Master fact store
+  SCHEMA.md                              (3KB)     — Schema docs
+  global/pitfalls.yaml                   (2KB)     — Cross-repo pitfalls
+  global/tool-quirks.yaml                (2KB)     — Tool quirks
+  repos/hermes-agent.yaml                (2KB)     — Repo-specific knowledge
+  repos/the-nexus.yaml                   (2KB)     — Repo-specific knowledge
+templates/
+  harvest-prompt.md                      (4KB)     — Extraction prompt
+test_sessions/                            (5 files) — Sample transcripts
+tests/ + scripts/test_*                   (14 files)— Test suite
+```
+
+**Total:** ~6,500 lines of code across 18 scripts + 14 test files.
 
 ---
 
-## The 100x Path (from README)
-
-```
-Month 1:  15,000 facts, sessions 20% faster
-Month 2:  45,000 facts, sessions 40% faster, first-try success up 30%
-Month 3:  90,000 facts, fleet measurably smarter per token
-```
-
-Each new session is better than the last. The intelligence compounds.
-
----
-
-*Generated by codebase-genome pipeline. Ref: timmy-home#676.*
+*Generated by Codebase Genome pipeline — Issue #676*