qmd

Search local text files with hybrid BM25, vector, and LLM reranking.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/kotakbiasa/hermes-agent --skill qmd-kotakbiasa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qmd
Source: https://github.com/kotakbiasa/hermes-agent/tree/main/optional-skills/research/qmd
Command: npx skills add https://github.com/kotakbiasa/hermes-agent --skill qmd-kotakbiasa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

BM25 keyword search, semantic vector search, and LLM reranking are combined to enable fast, private local search across notes, transcripts, and documents.

Core Features & Use Cases

  • Hybrid retrieval: keyword + vector + LLM reranking for high-quality results.
  • Local-first: runs entirely on-device without cloud dependencies.
  • MCP integration: exposes tools for Hermes Agent workflow automation and external MCP clients.

Quick Start

Add your collections and run qmd embed to build the index.

Frequently Asked Questions about qmd

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does hybrid retrieval combine BM25 and vector search for local documents?▼

Hybrid retrieval merges BM25 keyword matching with semantic vector search and LLM-powered reranking to deliver high-quality local search results across markdown notes and transcripts without cloud dependencies.

Can I run local search across personal markdown notes without sending data to the cloud?▼

Yes, local search runs entirely on-device without cloud dependencies, ensuring private queries across personal knowledge bases by processing text-based files stored locally on your machine.

How do I build a search index for meeting transcripts using local search?▼

To build a search index, add your collections of meeting transcripts and documentation, then run the embed CLI tool to generate the vector and BM25 indices required for hybrid retrieval.

Do I need Node.js to perform hybrid retrieval and LLM reranking on local files?▼

Yes, Node.js is required to execute the CLI tools for collection management, context tagging, and MCP-enabled workflows that facilitate hybrid retrieval and LLM reranking on local files.

Does this local search engine support MCP integration for agent workflows?▼

Yes, it supports MCP integration by exposing tools for Hermes Agent workflow automation and external MCP clients, enabling automated hybrid retrieval within agent-driven pipelines.

What are the limitations of using LLM reranking for local knowledge base search?▼

LLM reranking for local knowledge base search requires sufficient on-device compute resources, and the hybrid retrieval engine is limited to querying text-based files like markdown notes and documentation.