docs: GENOME.md — full codebase analysis #676

GENOME.md

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+# GENOME.md — compounding-intelligence
+
+*Auto-generated codebase genome. See timmy-home#676.*
+
+---
+
+## Project Overview
+
+**What:** A system that turns 1B+ daily agent tokens into durable, compounding fleet intelligence.
+
+**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:
+
+```
+SESSION ENDS → HARVESTER → KNOWLEDGE STORE → BOOTSTRAPPER → NEW SESSION STARTS SMARTER
+                              ↓
+                         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.
+
+---
+
+## 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]
+```
+
+### Pipeline 1: Harvester
+
+**Status:** Prompt designed. Script not implemented.
+
+Reads finished session transcripts (JSONL). Uses `templates/harvest-prompt.md` to extract durable knowledge into five categories:
+
+| 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" |
+
+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
+}
+```
+
+### Pipeline 2: Bootstrapper
+
+**Status:** Not implemented.
+
+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
+
+```
+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
+```
+
+---
+
+## 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 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
+
+### 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
+
+### 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.
+
+---
+
+## API Surface
+
+### Internal (scripts not yet implemented)
+
+| 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
+
+---
+
+## Test Coverage
+
+### What Exists
+
+| 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 |
+
+### What's Missing
+
+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?
+
+---
+
+## 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.
+
+2. **Knowledge poisoning** — A malicious or corrupted session could inject false facts. Confidence scoring partially mitigates this, but there is no verification step.
+
+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.
+
+4. **Transcript privacy** — Session transcripts may contain user data. The harvester must not extract personally identifiable information into the knowledge store.
+
+---
+
+## 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.*