Aggregate technical documentation from llms.txt, GitHub repositories, and websites.
npx skills add https://github.com/vumichien/bi-agent --skill docs-seeker
Please help me install this Agent Skill. Skill: docs-seeker Source: https://github.com/vumichien/bi-agent/tree/main/.claude/skills/docs-seeker Command: npx skills add https://github.com/vumichien/bi-agent --skill docs-seeker
Developers often waste hours searching for up-to-date, relevant technical documentation across fragmented sources. This Skill automates that complex search, providing consolidated, AI-friendly documentation from official sources, GitHub repositories, and the web, saving immense time and ensuring accuracy.
llms.txt standards and context7.com for structured, AI-friendly documentation.llms.txt is unavailable.Find the latest documentation for the 'Astro' framework, focusing on its data fetching capabilities.
High-intent search queries and answers about installing and using this skill.
Technical documentation discovery aggregates docs from llms.txt standards, GitHub repositories, and the web simultaneously. This Skill searches official sources first, then uses Repomix to analyze repositories and parallel web exploration, returning consolidated results from all sources in one query.
Yes. This Skill prioritizes llms.txt-based documentation and context7.com structured sources for AI-ready retrieval. When llms.txt is unavailable, it falls back to GitHub repository analysis and web research to ensure comprehensive coverage.
Parallel documentation exploration deploys multiple agents to simultaneously process numerous documentation URLs and repositories. This approach delivers faster, more complete results than manual searches, handling version filtering and multi-source coverage automatically.
Yes. The Skill analyzes GitHub repositories via Repomix and executes parallel exploration across websites, repos, and package registries. It handles versioning, topic filtering, and multi-source verification with clear fallbacks for missing sources.
Multi-source aggregation combines llms.txt standards, GitHub analysis, and web research into consolidated, AI-friendly documentation. This approach satisfies multi-source retrieval, version handling, and comprehensive reporting while automating the search across fragmented platforms.
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