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.