qmd

Search local knowledge bases with hybrid keyword, vector, and reranked retrieval.

Updated Apr 2, 2026
One-click install
npx skills add https://github.com/JKhyro/HERMES-AGENT --skill qmd-jkhyro
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qmd
Source: https://github.com/JKhyro/HERMES-AGENT/tree/main/optional-skills/research/qmd
Command: npx skills add https://github.com/JKhyro/HERMES-AGENT --skill qmd-jkhyro

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

qmd removes the friction of searching large local knowledge bases by combining keyword search, semantic retrieval, and reranking so you can find the right note, transcript, or document quickly.

Core Features & Use Cases

  • Hybrid Local Search: Search markdown notes, meeting transcripts, documentation, and other text files with BM25, vector search, and LLM reranking.
  • MCP and CLI Access: Use qmd directly from the terminal or connect it to Hermes Agent through MCP for seamless agentic retrieval.
  • Knowledge Base Workflows: Ideal for personal notes, project documentation, research archives, and meeting history when exact wording is unknown but the concept is clear.

Quick Start

Ask qmd to search your local notes for the topic, then review the best result and open the source document if needed.

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 when I only remember the concept?

To search local markdown notes by concept, use hybrid retrieval that combines BM25 keyword search with vector embeddings and LLM reranking to find relevant documents when exact wording is unknown.

What is BM25 search combined with vector embeddings for local knowledge bases?

Hybrid retrieval combining BM25 search with vector embeddings is a mechanism that matches exact keywords while performing semantic discovery to find conceptually relevant notes in local knowledge archives.

Can I use semantic retrieval for project documentation through MCP integration?

Yes, you can use semantic retrieval for project documentation through MCP integration, connecting the local search index directly to Hermes Agent for seamless agentic retrieval workflows.

Do I need local indexing to run reranked searches on my personal notes?

Yes, local indexing is required to run reranked searches on personal notes, because the system must generate vector embeddings and build BM25 indexes on-device before executing fast retrieval.

What is the best way to retrieve specific terms from large local text files?

The best way to retrieve specific terms from large local text files is using a hybrid search approach that runs BM25 keyword matching alongside vector search and applies LLM reranking for precise results.