---
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
   ```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

## 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)