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