Search local markdown notes using hybrid BM25 and semantic retrieval.
npx skills add https://github.com/Signmanal/VIGIL --skill qmd-signmanal
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/Signmanal/VIGIL/tree/main/optional-skills/research/qmd Command: npx skills add https://github.com/Signmanal/VIGIL --skill qmd-signmanal
Manually searching through large collections of local markdown notes, meeting transcripts, and documentation to find specific information is time-consuming and often inaccurate with basic keyword-only search tools.
Use the qmd skill to search your local notes and project docs for all mentions of the database migration plan from last quarter's team syncs.
High-intent search queries and answers about installing and using this skill.
Hybrid search for local markdown notes and documentation combines BM25 keyword matching, semantic vector similarity, and LLM-powered reranking. This approach ensures highly relevant retrieval from large collections of text files without sending any data to cloud services.
The best way to search meeting transcripts for specific past decisions is using a local hybrid search skill. It applies BM25 keyword matching and semantic vector similarity to quickly locate relevant content across thousands of stored text files.
Yes, you can perform semantic search on a local knowledge base without uploading data to the cloud. This skill processes all text files locally, ensuring data privacy while delivering conceptual search results for vague queries.
Yes, local document retrieval supports optional MCP integration for AI assistant workflows. This allows AI assistants to leverage hybrid search capabilities to access specific content across your local documentation efficiently.
To search thousands of local text files for a vague concept instead of an exact keyword, use semantic vector search mode. This mode interprets conceptual meaning rather than strict keyword matching, finding relevant notes even without exact terms.
davpatel605-beepSearch local markdown notes and transcripts using hybrid keyword and vector retrieval.
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robertbr123Index and search local documents using BM25, vector search, and AI reranking.
InverterNetworkCreate serverless vector indexes and run hybrid search queries.
