qmd

Index local notes and search with BM25, vector search, and LLM reranking.

Updated Apr 24, 2026
One-click install
npx skills add https://github.com/Harries/hermes-agent --skill qmd-harries
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qmd
Source: https://github.com/Harries/hermes-agent/tree/main/optional-skills/research/qmd
Command: npx skills add https://github.com/Harries/hermes-agent --skill qmd-harries

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Local knowledge collections sit idle without fast, privacy-preserving search. QMD enables on-device indexing and retrieval over notes, docs, transcripts, and files using a hybrid approach that blends traditional keyword search with semantic vectors and LLM reranking.

Core Features & Use Cases

  • On-device indexing of markdown notes, documents, and transcripts
  • Hybrid search combining BM25, vector search, and LLM reranking
  • MCP integration for seamless tool availability
  • Offline operation with private data
  • Use Case: Personal knowledge management, project research, or meeting transcripts search

Quick Start

Index your local notes and run a hybrid bm25+vector search to locate relevant information across your knowledge base.

Frequently Asked Questions about qmd

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

FAQPage Schema
How do I search local markdown notes and transcripts offline?

Search local notes and transcripts offline using a hybrid retrieval engine that combines BM25 keyword search, vector search, and LLM reranking directly on your device.

What is hybrid retrieval for personal knowledge management?

Hybrid retrieval for personal knowledge management blends traditional BM25 keyword matching with semantic vector search and LLM reranking to locate relevant information across local documents.

Can I index project documentation on macOS without cloud dependencies?

Yes, you can index project documentation on macOS without cloud dependencies because the system operates fully offline, relying on local storage for the index and locally cached models.

Does local knowledge base search work with MCP integration?

Yes, local knowledge base search supports MCP integration, allowing seamless tool availability and connectivity within your existing offline workflows and private data environments.

What are the limitations of offline search for local knowledge bases?

Limitations of offline search include requiring local storage capacity for indices and embeddings, and needing locally cached models and tooling, which may consume significant disk space on your device.