Search local markdown files with hybrid keyword and semantic retrieval.
npx skills add https://github.com/LynxLabVN/office-agent --skill qmd-lynxlabvn
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/LynxLabVN/office-agent/tree/main/agent-core/optional-skills/research/qmd Command: npx skills add https://github.com/LynxLabVN/office-agent --skill qmd-lynxlabvn
This Skill empowers you to efficiently search your local knowledge bases, notes, and documents using a hybrid retrieval engine with advanced features.
Run 'qmd search "search term"' to start searching your indexed notes and documents.
High-intent search queries and answers about installing and using this skill.
You can search your local markdown knowledge base without cloud services by using a hybrid retrieval engine that runs entirely on your device. It combines keyword matching, semantic vector search, and LLM reranking to deliver accurate results from locally stored files.
The best way to find specific information in large markdown collections is using a hybrid search engine that combines keyword matching, semantic understanding, and LLM reranking. This approach delivers comprehensive and accurate search results across your local documents.
You can integrate local knowledge base search into your MCP client workflow using the built-in MCP integration support. The Skill operates locally and supports both MCP clients and command-line interfaces for flexible querying of your indexed notes.
Yes, qmd works with markdown files and supports command-line usage. You can run 'qmd search "search term"' from the CLI to quickly search your indexed notes and documentation files stored locally.
Semantic vector search combined with LLM reranking improves local search results by first understanding the contextual meaning of your query and then intelligently reordering the matches. This hybrid approach ensures both relevance and accuracy when retrieving information from your knowledge base.
The local AI search is specifically designed to retrieve information from locally stored markdown, note, and documentation files. It leverages keyword and semantic vector search optimized for these text-based formats to deliver accurate search results.
mahmoudimusSearch local Markdown notes with hybrid keyword and semantic retrieval.
cpfifferQuery the network.comind semantic index for cognition records via /xrpc/network.comind.search.query endpoint.
Yangel-hideSearch local knowledge stores with hybrid BM25, vector, and LLM reranking.
overviewlabsSearch local Markdown collections with hybrid keyword and vector retrieval.
