extract

# Extract Codebase Knowledge Build or rebuild the `.gauntlet/knowledge.json` knowledge base. ## Steps 1. **Identify target directory**: use the current working directory or a user-specified path 2. **Run AST extraction**: invoke the extractor script ```bash python3 ${CLAUDE_PLUGIN_ROOT}/scripts/extractor.py <target-dir> ``` 3. **AI enrichment**: for each extracted entry, enhance the `detail` field with natural language explanation of business logic, data flow, architectural role, and rationale 4. **Cross-reference**: link related entries across modules by matching imports, shared types, and data flow paths 5. **Merge with annotations**: preserve existing curated entries in `.gauntlet/annotations/` 6. **Save**: write to `.gauntlet/knowledge.json` 7. **Report**: show summary by category, coverage gaps, difficulty distribution ## Category Priority 1. business_logic (weight 7) 2. architecture (weight 6) 3. data_flow (weight 5) 4. api_contract (weight 4) 5. pattern (weight 3) 6. dependency (weight 2) 7. error_handling (weight 1)