Search project documents and return relevance-ranked results with contextual excerpts.
npx skills add https://github.com/doancan/mags --skill mags-docs-search
Please help me install this Agent Skill. Skill: mags-docs-search Source: https://github.com/doancan/mags/tree/main/skills/mags-docs-search Command: npx skills add https://github.com/doancan/mags --skill mags-docs-search
Locates and ranks relevant project documents across your codebase, reducing time spent searching.
Invoke mags-docs-search with your query to locate matching documents.
High-intent search queries and answers about installing and using this skill.
Full-text search across project documents locates architecture notes, backend rules, and API references by indexing repository docs and notes, then ranking matches based on your query to return relevant results with contextual excerpts.
Snippet extraction from full-text indexing finds relevant documentation sections by analyzing query relevance, returning concise contextual excerpts from your project notes so you can quickly locate matching documents without manual scanning.
Yes, relevance ranking evaluates backend rules within your repository docs, applying full-text indexing to sort results by query match quality and present the most relevant architecture overviews and API references first.
Invoke a document search query to locate matching API references across your codebase, which uses full-text indexing and relevance ranking to return concise snippets, reducing time spent manually searching through notes.
Project docs search covers architecture overviews, backend rules, and API references in typical software projects, operating across your repository's notes and documentation to return relevance-ranked results with contextual excerpts.
Yes, full-text search operates across your codebase notes to locate architecture overviews, using relevance ranking and snippet extraction to present concise, matching results from your project documents based on your query.
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