/extract

name: extract
description: Builds the gauntlet knowledge base from AST extraction and AI enrichment. Use when initializing or refreshing codebase knowledge for challenges.
model_hint: standard

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

1. **Identify target directory**: use the current working directory or a user-specified path

2. **Run AST extraction**: invoke the extractor script

   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

Exit Criteria

• [ ] `.gauntlet/knowledge.json` exists 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 `detail` field 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

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)