Index local markdown notes and transcripts for hybrid semantic search.
npx skills add https://github.com/InverterNetwork/hermes-agent --skill qmd-inverternetwork
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/InverterNetwork/hermes-agent/tree/main/optional-skills/research/qmd Command: npx skills add https://github.com/InverterNetwork/hermes-agent --skill qmd-inverternetwork
This skill solves the challenge of fragmented personal knowledge by providing a unified, local search engine that indexes your notes, transcripts, and documentation without relying on cloud services.
Ask the agent to search your notes for a specific concept or document by providing a natural language query.
High-intent search queries and answers about installing and using this skill.
You can perform local semantic search across markdown notes and meeting transcripts by installing a CLI tool that indexes files entirely on-device. This approach combines BM25 keyword matching with vector embeddings to retrieve information while ensuring your data never leaves your machine.
Hybrid semantic search improves knowledge base retrieval by combining traditional BM25 keyword matching, vector embeddings, and LLM-based reranking. This multi-layered approach ensures high-precision results when finding specific decisions or technical documentation across thousands of files.
Yes, Node.js and SQLite are required dependencies to run this local AI search index for markdown files. These components support the CLI tool needed to index your notes and execute hybrid semantic search queries on your device.
Yes, you can use Model Context Protocol (MCP) for persistent agent access to your personal knowledge base. MCP integration allows AI agents to continuously query your locally indexed markdown notes and documentation through natural language requests.
The best way to find specific decisions in past meeting transcripts using natural language is to use a local search engine with LLM-powered reranking. It processes your natural language query, matches it against indexed transcripts via vector embeddings, and surfaces the most relevant decisions.
Relying on BM25 keyword matching alongside vector search ensures high-precision retrieval for your local markdown files. BM25 catches exact term matches that vector search might miss, while LLM-based reranking further refines the final results.
JuanMS20Index Markdown and text files for local hybrid search with BM25, vectors, and reranking.
h4vzzOptimize raw context chunks for AI models within a token budget.
zerwizSearch local knowledge bases with hybrid BM25 and vector retrieval.
chronista-clubEnable semantic retrieval over high-dimensional embeddings using Qdrant.
