qmd
Local hybrid search for markdown notes and docs. Use when searching notes, finding related content, or retrieving documents from indexed collections.
Why use this skill?
Efficiently search your local markdown notes and documentation with qmd. A powerful, local-first search tool for organized, instant information retrieval.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/levineam/qmd-externalWhat This Skill Does
qmd (Quick Markdown Search) is a specialized local search engine designed specifically for Markdown-based knowledge bases, notes, and documentation. It allows users to index collections of files and perform lightning-fast searches using BM25 keyword matching or semantic vector search. By converting your local file directories into searchable collections, qmd bridges the gap between disorganized local notes and structured, retrieval-augmented knowledge management. It provides a robust command-line interface that integrates seamlessly with OpenClaw, enabling the AI to pull context directly from your personal or professional document archives.
Installation
To get started with qmd, ensure you have Bun version 1.0.0 or higher installed. On macOS, you must also have SQLite extensions installed via Homebrew (brew install sqlite). Once these prerequisites are met, install the tool globally using: bun install -g https://github.com/tobi/qmd. After installation, add your directories using qmd collection add /path/to/notes --name [collection_name]. Crucially, you must run qmd embed once to index your files and enable the vector search capabilities required for semantic queries. Ensure your PATH environment variable includes $HOME/.bun/bin for system-wide access.
Use Cases
qmd is ideal for individuals or developers who maintain large amounts of local documentation, research notes, or technical wikis in Markdown. It is perfectly suited for quickly finding buried information in a 'second brain' setup, such as retrieving a specific configuration snippet, recalling a project decision from past meeting notes, or surfacing related documentation across disparate folders. It is not intended for source code searching or managing large binary repositories, but rather for text-heavy, human-readable collections.
Example Prompts
- "Search my notes for the documentation on the new API authentication workflow."
- "Find related notes regarding the Q3 project roadmap and my recent task list."
- "Retrieve the markdown file about local vector database configurations from my tech-notes collection."
Tips & Limitations
Performance is the primary consideration when using qmd. Always prioritize the default qmd search command, as it relies on BM25 indexing and is typically instantaneous. Only reserve qmd vsearch for when keyword searches fail to produce relevant results, keeping in mind that the semantic vector engine may require significant compute time for a cold start. Avoid qmd query for standard interactions, as the added overhead of LLM reranking can lead to timeouts. For the best experience, organize your files into logical collections to narrow your search scope, and use the --json or --files flags if you are passing these results directly into further agent workflows to ensure structured and parseable output.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-levineam-qmd-external": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read
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