qmd

Enable local hybrid search across markdown notes and documents.

Updated Apr 27, 2026
One-click install
npx skills add https://github.com/AissenLiu/EasyHermes --skill qmd-aissenliu
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qmd
Source: https://github.com/AissenLiu/EasyHermes/tree/main/hermes-agent/optional-skills/research/qmd
Command: npx skills add https://github.com/AissenLiu/EasyHermes --skill qmd-aissenliu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Private, local search over your notes, docs, and transcripts, enabling fast retrieval without cloud dependencies.

Core Features & Use Cases

  • Hybrid search across local content using BM25, vector search, and LLM reranking.
  • CLI and MCP integration for seamless Hermes Agent workflows.
  • Index and query markdown notes, meeting transcripts, and documents for quick discovery.

Quick Start

Run a local search across your markdown notes and transcripts to retrieve the most relevant documents.

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?

You can search local markdown notes and transcripts offline by utilizing a hybrid retrieval system. It uses BM25, vector search, and LLM reranking to index and query your private documents without cloud dependencies.

What is hybrid retrieval for local knowledge bases?

Hybrid retrieval for local knowledge bases combines BM25 keyword matching, vector search, and LLM reranking. This multi-layered approach ensures fast and highly relevant document discovery directly on your device.

Does local knowledge search work with MCP and CLI integrations?

Yes, local knowledge search supports seamless MCP and CLI integrations. This allows you to index and query your local documents directly within Hermes Agent workflows for quick retrieval.

Do I need Node.js to run local vector search and indexing?

Yes, you need Node.js version 22 or higher to run local vector search and indexing. The system operates offline using a local model cache and your MCP configurations.

Can I retrieve documents from meeting transcripts without cloud dependencies?

You can retrieve documents from meeting transcripts without cloud dependencies by running local on-device search. It indexes your transcripts and documents locally, ensuring private and fast retrieval.

What's the best way to index local documents for private search?

The best way to index local documents for private search is using a local on-device tool that supports hybrid ranking. This enables indexing markdown notes and transcripts for quick discovery while keeping data offline.