Build a codebase knowledge base of business logic, architecture, data flow, and patterns.
Scanned 9/2/2026
Install to Claude Code
npx -y skills add majiayu000/claude-skill-registry --skill extract-athola-claude-night-market --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Extract Athola Claude Night Market?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/majiayu000-extract-athola-claude-night-market)More formats (shields.io, HTML) on the badges page.
---
name: extract
description: 'Build a codebase knowledge base of business logic, architecture, data flow, and patterns.'
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
## 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)
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Interview, source-challenge, verify, save, and ADR-gate fuzzy coding requests into Codex-ready implementation specs. Use when a feature, bugfix, refactor, migration, repo-wide change, or architecture task needs user-verified requirements, source-backed decisions, durable architecture decisions, acceptance criteria, validation commands, rollout notes, saved spec/ADR files, and a Codex execution prompt. Do not use when already fully specified or when the user wants direct implementation now.
Use when a repo needs CodeGraph plus ast-grep for Codex MCP setup, exploration, impact analysis, structural search, or safe refactor planning.