docs-seeker intelligently discovers and analyzes technical documentation using multiple complementary strategies. It prioritizes llms.txt-formatted documen…
docs-seeker intelligently discovers and analyzes technical documentation using multiple complementary strategies. It prioritizes llms.txt-formatted documentation (with built-in context7.com URL patterns for GitHub repositories and websites), performs repository analysis via Repomix, launches parallel Explorer agents for broad coverage, and falls back to Researcher agents when structured sources are missing. Typical use cases include retrieving the latest library or framework docs, extracting documentation for specific features or topics, auditing repository documentation, and aggregating multi-source references for onboarding or integration. Core features include target identification (name, version, scope), llms.txt pattern probing (including topic query parameters), Repomix code inspection, parallelized exploration, and robust fallback research. The main advantages are standardized AI-friendly outputs, faster and repeatable discovery, and comprehensive coverage across repos and websites to reduce manual search effort and improve agent grounding.
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