Qmd 4

Documents

qmd-4

Qmd 4 is an Agent Skill for documents published in the sundial-org/awesome-openclaw-skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Search and retrieve markdown documents from local knowledge bases using qmd. Supports BM25 keyword search, vector semantic search, and hybrid search with LLM re-ranking. Use for querying indexed notes, documentation, meeting transcripts, and any markdown-based knowledge. Requires qmd CLI installed (bun install -g https://github.com/tobi/qmd).

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QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

Open your project in Codex.
Copy and paste the prompt below.
Review files and risks before approving any action.

Prompt to paste

Inspect the Agent Skill at https://github.com/sundial-org/awesome-openclaw-skills/blob/HEAD/skills/qmd-4/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/qmd-4, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

QMD - Local Markdown Search

Search and retrieve documents from locally indexed markdown knowledge bases.

Installation

bun install -g https://github.com/tobi/qmd

Setup

# Add a collection
qmd collection add ~/notes --name notes --mask "**/*.md"

# Generate embeddings (required for vsearch/query)
qmd embed

Usage Rules

Always use --json flag for structured output when invoking qmd commands.

Search Commands

search (BM25 keyword search - fast)

qmd search "authentication flow" --json
qmd search "error handling" --json -n 10
qmd search "config" --json -c notes

vsearch (vector semantic search)

qmd vsearch "how does login work" --json
qmd vsearch "authentication best practices" --json -n 20

query (hybrid with LLM re-ranking - best quality)

qmd query "implementing user auth" --json
qmd query "deployment process" --json --min-score 0.5

Search Options

Option	Description
`-n NUM`	Number of results (default: 5, or 20 with --json)
`-c, --collection NAME`	Restrict to specific collection
`--min-score NUM`	Minimum score threshold
`--full`	Return complete document content in results
`--all`	Return all matches

Retrieval Commands

get (single document)

qmd get docs/guide.md --json
qmd get "#a1b2c3" --json
qmd get notes/meeting.md:50 -l 100 --json

multi-get (multiple documents)

qmd multi-get "docs/*.md" --json
qmd multi-get "api.md, guide.md, #abc123" --json
qmd multi-get "notes/**/*.md" --json --max-bytes 20480

Maintenance Commands

qmd update              # Re-index changed files
qmd status              # Check index health
qmd collection list     # List all collections

Search Mode Selection

Mode	Speed	Quality	Best For
search	Fast	Good	Exact keywords, known terms
vsearch	Medium	Better	Conceptual queries, synonyms
query	Slow	Best	Complex questions, uncertain terms

Performance note: vsearch and query have ~1 minute cold start latency for vector initialization. Prefer search for interactive use.

MCP Server

qmd can run as an MCP server for direct integration:

qmd mcp

Exposes tools: qmd_search, qmd_vsearch, qmd_query, qmd_get, qmd_multi_get, qmd_status