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feat(dependency-graph): add transitive closure and deep chain analysis
- Implement transitive_closure(): computes full dependency tree for each node
- Implement find_deep_chains(): identifies longest paths in dependency graph
- JSON output now includes `transitive` and `deep_chains` fields
- Added comprehensive unit tests in scripts/test_dependency_graph.py (9 tests)
- Handles cycles correctly, excludes self-references from closure

Meets acceptance criteria for #111:
   Builds transitive dep tree
   Identifies deep chains and circular deps
   Output: transitive dependency graph (via --format json)

Closes #111
2026-04-26 05:08:23 -04:00
2026-04-15 11:29:23 -04:00

Compounding Intelligence

Turn 1B+ daily tokens into durable, compounding fleet intelligence.

The Problem

20,991 sessions on disk. Each one starts at zero. Every agent rediscover the same HTTP 405 is a branch protection issue. The intelligence from a million tokens of work evaporates when the session ends.

The Solution

Three pipelines that form a compounding loop:

SESSION ENDS → HARVESTER → KNOWLEDGE STORE → BOOTSTRAPPER → NEW SESSION STARTS SMARTER
                              ↓
                         MEASURER → Prove it's working

Architecture

Pipeline 1: Harvester

Reads finished session transcripts. Extracts durable knowledge: facts, pitfalls, patterns, tool quirks. Stores in knowledge/.

Pipeline 2: Bootstrap

Before a session starts, queries knowledge store for relevant facts. Assembles compact 2k-token context. Injects into session so it starts with full situational awareness.

Pipeline 3: Measure

Tracks whether compounding is happening. Knowledge velocity, error reduction, hit rate, task completion. Daily report proves the loop works.

Directory Structure

├── knowledge/
│   ├── index.json          # Machine-readable fact index
│   ├── global/             # Cross-repo knowledge
│   ├── repos/{repo}.md     # Per-repo knowledge
│   └── agents/{agent}.md   # Agent-type notes
├── scripts/
│   ├── harvester.py        # Post-session knowledge extractor
│   ├── bootstrapper.py     # Pre-session context loader
│   ├── measurer.py         # Compounding metrics
│   └── session_reader.py   # JSONL parser
├── metrics/
│   └── dashboard.md        # Human-readable status
└── templates/
    ├── bootstrap-context.md
    └── harvest-prompt.md

The 100x Path

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.

Issues

See all issues for the full roadmap.

Epics:

  • EPIC 1: Session Harvester (#2)
  • EPIC 2: Knowledge Store & Bootstrap (#3)
  • EPIC 3: Compounding Measurement (#4)
  • EPIC 4: Retroactive Harvest (#5)
Description
System for turning 1B+ daily tokens into durable, compounding fleet intelligence
Readme 4.8 MiB
Languages
Python 100%