git:20260622.c7c8f6f to git:20260713.f98719b

---

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.

## 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)