Search local Markdown notes using BM25 keyword and optional vector search.
npx skills add https://github.com/nirholas/agenti --skill qmd-nirholas
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/nirholas/agenti/tree/main/packages/protocols/x402-cloddsbot/src/skills/bundled/qmd Command: npx skills add https://github.com/nirholas/agenti --skill qmd-nirholas
Quickly locate and retrieve information from local Markdown notes and docs using fast keyword search and optional semantic ranking.
Ask QMD to search your notes with a query to begin.
High-intent search queries and answers about installing and using this skill.
You can search local Markdown notes by indexing your files and querying them to retrieve either full documents or specific sections using fast BM25 keyword search.
When exact keywords fail, you can use optional vector semantic search to rank indexed Markdown notes by content similarity, enabling deeper relevance retrieval from your knowledge base.
Yes, it supports BM25 keyword search by default for fast results and optional vector semantic search to capture content similarity for your project documentation.
Yes, it indexes local Markdown collections, allowing you to perform fast retrieval across personal knowledge bases, research notes, and team docs.
To start searching your research notes, simply index your local Markdown files and ask QMD to search your collection using a natural language query.
BM25 keyword search is fast but relies on exact term matching; it may miss contextually relevant documents if your query lacks specific keywords, where optional semantic search is needed.
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