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Builds the gauntlet knowledge base from AST extraction and AI enrichment.
Passed 11 of 17 test cases with the skill, and 3 of 17 without it.
and 9 more in the full report
11 fixed3 passed either way7 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which numeric weight should be assigned to data_flow when ordering category importance in the report?
| Checked | Without skill | With skill |
|---|---|---|
| Assigns weight 5 to data_flow | Missed | Passed |
The instructions your model reads when the skill loads.
Build or rebuild the .gauntlet/knowledge.json knowledge base.
gauntlet:curate)gauntlet:graph-build)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 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
.gauntlet/knowledge.json exists and is valid JSON after the
skill completes; entries from .gauntlet/annotations/ are merged
and not overwrittentest cases passed, vs 3 / 17 without
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