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
Script-first documentation discovery using llms.txt standard.
Execute scripts to handle entire workflow - no manual URL construction needed.
ALWAYS execute scripts in this order:
# 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.
detect-topic.js - Classify query type
{topic, library, isTopicSpecific}fetch-docs.js - Retrieve documentation
analyze-llms-txt.js - Process llms.txt
Topic-Specific Search - Fastest path (10-15s)
General Library Search - Comprehensive coverage (30-60s)
Repository Analysis - Fallback strategy
context7-patterns.md - URL patterns, known repositories
errors.md - Error handling, fallback strategies
advanced.md - Edge cases, versioning, multi-language
Topic query: "How do I use date picker in shadcn?"
node scripts/detect-topic.js "<query>" # → {topic, library, isTopicSpecific}
node scripts/fetch-docs.js "<query>" # → 2-3 URLs
# Read URLs with WebFetch
General query: "Documentation for Next.js"
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 recommendation
Scripts load .env: process.env > .claude/skills/docs-seeker/.env > .claude/skills/.env > .claude/.env
See .env.example for configuration options.