docs-seeker
Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy
npx skills add majiayu000/claude-skill-registry --skill docs-seeker-binjuhor-shadcn-lar --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Documentation Discovery via Scripts ## Overview **Script-first** documentation discovery using llms.txt standard. Execute scripts to handle entire workflow - no manual URL construction needed. ## Primary Workflow **ALWAYS execute scripts in this order:** ```bash # 1. DETECT query type (topic-specific vs general) node scripts/detect-topic.js "<user query>" # 2. FETCH documentation using script output node scripts/fetch-docs.js "<user query>" # 3. ANALYZE results (if multiple URLs returned) cat llms.txt | node scripts/analyze-llms-txt.js - ``` Scripts handle URL construction, fallback chains, and error handling automatically. ## Scripts **`detect-topic.js`** - Classify query type - Identifies topic-specific vs general queries - Extracts library name + topic keyword - Returns JSON: `{topic, library, isTopicSpecific}` - Zero-token execution **`fetch-docs.js`** - Retrieve documentation - Constructs context7.com URLs automatically - Handles fallback: topic → general → error - Outputs llms.txt content or error message - Zero-token execution **`analyze-llms-txt.js`** - Process llms.txt - Categorizes URLs (critical/important/supplementary) - Recommends agent distribution (1 agent, 3 agent
- Overview
- Primary Workflow
- Scripts
- Workflow References
- References
- Execution Principles
- Quick Start
- Environment
node scripts/detect-topic.js "<user query>"
node scripts/fetch-docs.js "<user query>"
cat llms.txt | node scripts/analyze-llms-txt.js -
node scripts/detect-topic.js "<query>" # → {topic, library, isTopicSpecific}
node scripts/fetch-docs.js "<query>" # → 2-3 URLs
Read URLs with WebFetch
node scripts/detect-topic.js "<query>" # → {isTopicSpecific: false}
node scripts/fetch-docs.js "<query>" # → 8+ URLs
cat llms.txt | node scripts/analyze-llms-txt.js - # → {totalUrls, distribution}
Deploy agents per recommendationWhat does the docs-seeker skill do?
Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill docs-seeker-binjuhor-shadcn-lar --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.