Index and search private notes locally with hybrid retrieval.
npx skills add https://github.com/math-inc/OpenGauss --skill qmd-math-inc
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/math-inc/OpenGauss/tree/main/optional-skills/research/qmd Command: npx skills add https://github.com/math-inc/OpenGauss --skill qmd-math-inc
Locally indexes and searches private notes, docs, and transcripts to enable fast, private knowledge retrieval without cloud services.
Run a local search query across your notes with qmd to retrieve relevant results.
High-intent search queries and answers about installing and using this skill.
Local hybrid search indexes your private notes and documents using a retrieval engine that runs entirely on your machine, ensuring fast and private knowledge retrieval without cloud services.
Hybrid retrieval combines BM25 keyword matching, vector search, and LLM reranking to deliver highly relevant search results across your local knowledge base, optimizing both exact keyword matches and semantic context.
Yes, you can use vector search and BM25 for offline knowledge management. The system indexes your data locally, allowing you to query collections securely without an internet connection.
Optional embedding or vector models are required to enable vector search capabilities. Local data storage is necessary to hold your notes, docs, and transcripts for the indexing process.
The best way to find a specific meeting transcript is to use a local hybrid search query that scans indexed notes and documents, quickly locating summaries or keywords across large personal collections.
You access and query local search collections by using CLI or MCP tooling integrated with the hybrid retrieval engine, enabling direct commands to search your indexed notes and transcripts.
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