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AI/MLathola

extract

Builds the gauntlet knowledge base from AST extraction and AI enrichment. Use when initializing or refreshing codebase knowledge for challenges.

Stars
294
Source
athola/claude-night-market
Updated
2026-05-30
Slug
athola--claude-night-market--extract
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/athola/claude-night-market/HEAD/plugins/gauntlet/skills/extract/SKILL.md -o .claude/skills/extract.md

Drops the SKILL.md into .claude/skills/extract.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

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

    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

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)