Extract
- 74 installs
- 325 repo stars
- Updated August 2, 2026
- athola/claude-night-market
extract is an agent skill that compiles AST-derived codebase knowledge into .gauntlet/knowledge.json with AI-enriched gauntlet entries.
About
extract is a Claude Night Market gauntlet skill that builds or rebuilds `.gauntlet/knowledge.json` from a target codebase. The agent identifies the working or user-specified directory, invokes the bundled AST extractor script, then enriches each record with natural-language business logic and architectural context. Cross-linking ties related symbols across modules; existing annotation files are preserved on merge. Solo builders and small teams use it when standing up coding challenges, onboarding agents to unfamiliar repos, or refreshing knowledge after large refactors. Category priority weights business_logic highest through error_handling lowest, shaping challenge difficulty. It is procedural infrastructure—not a one-shot lint—meant for repeatable knowledge-base generation inside the gauntlet workflow.
- Runs python3 extractor.py on target directory for AST-based entry capture
- AI enrichment expands each entry detail with business logic, data flow, architecture, and rationale
- Cross-references modules via imports, shared types, and data-flow paths
- Merges with curated .gauntlet/annotations/ without overwriting hand-tuned entries
- Reports coverage gaps and difficulty distribution across 7 weighted categories
Extract by the numbers
- 74 all-time installs (skills.sh)
- Ranked #5,508 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 74 |
|---|---|
| repo stars | ★ 325 |
| Security audit | 3 / 3 scanners passed |
| Last updated | August 2, 2026 |
| Repository | athola/claude-night-market ↗ |
What it does
Initialize or refresh a gauntlet knowledge base from AST extraction plus AI-enriched explanations for codebase challenge workflows.
Who is it for?
Best when you're maintaining athola/claude-night-market gauntlets and need reproducible codebase intelligence from AST plus narrative enrichment.
Skip if: Quick ad-hoc code search without gauntlet artifacts, or repos where running a Python extractor against the tree is disallowed.
When should I use this skill?
Initializing or refreshing codebase knowledge for gauntlet challenges—when .gauntlet/knowledge.json must be built or updated.
What you get
A merged knowledge.json with enriched details, annotation preservation, and a category or coverage summary ready for gauntlet challenge flows.
- .gauntlet/knowledge.json
- Coverage and difficulty summary report
By the numbers
- 7 knowledge categories with explicit priority weights (business_logic weight 7 through error_handling weight 1)
Files
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
Exit Criteria
- [ ]
.gauntlet/knowledge.jsonexists 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
detailfield 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)
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Structured gauntlet knowledge pipeline with weighted categories—not a generic codebase RAG indexer or static doc generator alone.
FAQ
Who is extract for?
Developers using Claude Night Market gauntlets who want agent-ready codebase knowledge extracted from real project trees.
When should I use extract?
Use when initializing.gauntlet/knowledge.json, after major refactors in build, before ship review drills that need architecture context, or during operate iterate when refreshing challenge coverage.
Is extract safe to install?
It runs local Python extraction over your source tree; review the Security Audits panel on this page and audit scripts/extractor.py before execution.