project-knowledge-extractionlisted

# Project Knowledge Extraction ## Coverage Extract durable, reusable project knowledge from local evidence. Covers source discovery, fact extraction, vocabulary capture, decision mining, failure-pattern capture, artifact routing, grounding, freshness, and deciding whether knowledge belongs in a skill, ADR, context doc, glossary, runbook, or memory. ## Philosophy Agents lose value when every session rediscovers the same project facts. The fix is not to dump everything into context. The fix is to extract durable knowledge, classify it, ground it in truth sources, and store it where future agents can find it. Durable knowledge must be evidence-backed. If it cannot be tied to code, docs, decisions, or observed behavior, mark it as a hypothesis. ## Method 1. Inventory evidence: code surfaces, docs, issues, reports, tests, scripts, screenshots, and prior decisions. 2. Extract stable facts, recurring failure modes, vocabulary, boundaries, and source-of-truth files. 3. Discard session noise, one-off logs, and facts likely to expire quickly. 4. Classify destination: skill, ADR, context doc, glossary, runbook, memory, or no artifact. 5. Add grounding: truth sources, last-verified date, and drift trigger. 6. Link the new artifact into the context graph. 7. Verify by asking what future task this knowledge should improve. ## Verification - [ ] Every retained fact has a truth source or is marked as a hypothesis - [ ] One-off session notes were excluded - [ ] Vocabulary distinction