Extract Codebase Knowledge
Build or rebuild the .gauntlet/knowledge.json knowledge base.
When NOT To Use
- Tribal knowledge no parser can see (use
gauntlet:curate) - Building the code graph (use
gauntlet:graph-build)
Steps
Identify target directory: use the current working directory or a user-specified path
Run AST extraction: invoke the extractor script
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/extractor.py <target-dir>AI enrichment: for each extracted entry, enhance the
detailfield with natural language explanation of business logic, data flow, architectural role, and rationaleCross-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.jsonReport: show summary by category, coverage gaps, difficulty distribution
Exit Criteria
-
.gauntlet/knowledge.jsonexists and is valid JSON after the skill completes; entries from.gauntlet/annotations/are merged and not overwritten - Report shows entry counts broken down by all 7 categories (business_logic, architecture, data_flow, api_contract, pattern, dependency, error_handling) with coverage gaps identified
- Each extracted entry has a
detailfield containing a natural language explanation (not just the raw AST node name) - Cross-reference links between related entries are present for modules sharing imports, shared types, or data flow paths
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)