Index and search local Markdown notes with hybrid retrieval.
npx skills add https://github.com/wwwillott/jobnimbus --skill qmd-wwwillott
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/wwwillott/jobnimbus/tree/main/optional-skills/research/qmd Command: npx skills add https://github.com/wwwillott/jobnimbus --skill qmd-wwwillott
Locally index and search Markdown notes, documents, and transcripts without cloud dependencies.
Index a local folder with qmd and run a search across your collections.
High-intent search queries and answers about installing and using this skill.
Local on-device search solves this by indexing Markdown notes and documents directly on your machine. It uses hybrid retrieval combining BM25 keyword search, vector similarity, and LLM reranking, operating entirely offline without any cloud dependencies.
Hybrid retrieval combines BM25 keyword search, vector similarity, and LLM reranking to provide relevant results from personal knowledge bases. This mechanism ensures highly accurate search outcomes across locally indexed notes, documents, and transcripts.
Yes, you can run offline vector search across private transcripts on your machine. The system provides on-device indexing with vector similarity and operates entirely offline, ensuring your private transcripts remain secure without any cloud connections.
You integrate local search with an MCP workflow using the provided MCP interface. This supports HermesAgent MCP workflows alongside a CLI, allowing you to run queries across your locally indexed collections seamlessly within your agent architecture.
On-device BM25 search works for large local document collections by building fast, private indexes of local files. It is designed for developers, researchers, and knowledge workers needing efficient offline retrieval across extensive personal knowledge bases.
nelohenriqIndex local markdown notes and transcripts for hybrid on-device search via MCP.
devMoezIndex local markdown collections for hybrid BM25 and vector search.
ricardorocheStandardizes chunking, vector database integration, embedding management, and retrieval reranking for RAG pipelines.
sheawinklerSearch local markdown and text collections using hybrid BM25 and vector ranking.
