Qmd Skill Main

Local hybrid search for markdown notes and docs. Use when searching notes, finding related content, or retrieving documents from indexed collections.

Security Scan

The skill is internally consistent with a local markdown search tool: it only requires the qmd binary and its instructions describe local indexing and searches; minor notes about undeclared prerequisites and optional installs are worth checking before install.

Purpose & Capability

The skill's name/description (local hybrid search of markdown notes) matches what the SKILL.md instructs (qmd search/vsearch/query against indexed local collections). Required binaries list only 'qmd', which is appropriate, but the docs also call out prerequisites (Bun >=1.0.0, sqlite on macOS) and an install method that uses Bun to pull the project — those prerequisites are mentioned in the instructions but not declared in the registry 'required binaries' list, a minor inconsistency to be aware of.

Instruction Scope

SKILL.md only instructs use of the qmd CLI to index and search user-specified local Markdown paths, manage indexes, and schedule updates. It does not direct the agent to read unrelated system files, exfiltrate data, or access unrelated credentials. It does mention automatic model downloads and a default cache path (~/.cache/qmd/models/) which are expected for local embedding workflows.

Install Mechanism

The registry lists no formal install spec, but SKILL.md and embedded metadata recommend 'bun install -g https://github.com/tobi/qmd'. Installing from a GitHub repo via Bun is common but executes unreviewed code during installation — a moderate-risk install pattern compared with a vetted package/release. Also the tool may auto-download GGUF models from the network on first run (large files, remote sources).

Credentials

The skill declares no required environment variables or credentials, and SKILL.md only references expected env items like PATH and XDG_CACHE_HOME for cache override. No sensitive credentials are requested, and the scope of environment access (local files for indexing, cache for models) is proportionate to the stated purpose.

Persistence & Privilege

The skill does not request always: true or other elevated persistent privileges in the registry. It is an instruction-only skill that relies on the qmd binary; it does not attempt to force permanent inclusion or autonomous invocation beyond normal agent behavior.

Assessment

This skill appears to do what it says: index and search local Markdown using the qmd CLI. Before installing, verify you trust the qmd GitHub source (the SKILL suggests installing via Bun from the repo), and prefer installing through your package manager or official releases if available. Be aware qmd may download local GGUF models automatically (large disk and network usage) and will read any files you add to its collections — only index directories you intend to expose to the tool. Finally, install Bun/SQLite if you plan to follow the SKILL.md instructions, and confirm the qmd binary on PATH is the expected one.

qmd - Quick Markdown Search

Local search engine for Markdown notes, docs, and knowledge bases. Index once, search fast.

When to use (trigger phrases)

• "search my notes / docs / knowledge base"
• "find related notes"
• "retrieve a markdown document from my collection"
• "search local markdown files"

Default behavior (important)

• Prefer qmd search (BM25). It's typically instant and should be the default.
• Use qmd vsearch only when keyword search fails and you need semantic similarity (can be very slow on a cold start).
• Avoid qmd query unless the user explicitly wants the highest quality hybrid results and can tolerate long runtimes/timeouts.

Prerequisites

• Bun >= 1.0.0
• macOS: brew install sqlite (SQLite extensions)
• Ensure PATH includes: $HOME/.bun/bin

Install Bun (macOS): brew install oven-sh/bun/bun

Install

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

Setup

qmd collection add /path/to/notes --name notes --mask "**/*.md"
qmd context add qmd://notes "Description of this collection"  # optional
qmd embed  # one-time to enable vector + hybrid search

What it indexes

• Intended for Markdown collections (commonly **/*.md).
• In our testing, "messy" Markdown is fine: chunking is content-based (roughly a few hundred tokens per chunk), not strict heading/structure based.
• Not a replacement for code search; use code search tools for repositories/source trees.

Search modes

• qmd search (default): fast keyword match (BM25)
• qmd vsearch (last resort): semantic similarity (vector). Often slow due to local LLM work before the vector lookup.
• qmd query (generally skip): hybrid search + LLM reranking. Often slower than vsearch and may timeout.

Performance notes

• qmd search is typically instant.
• qmd vsearch can be ~1 minute on some machines because query expansion may load a local model (e.g., Qwen3-1.7B) into memory per run; the vector lookup itself is usually fast.
• qmd query adds LLM reranking on top of vsearch, so it can be even slower and less reliable for interactive use.
• If you need repeated semantic searches, consider keeping the process/model warm (e.g., a long-lived qmd/MCP server mode if available in your setup) rather than invoking a cold-start LLM each time.

Common commands

qmd search "query"             # default
qmd vsearch "query"
qmd query "query"
qmd search "query" -c notes     # Search specific collection
qmd search "query" -n 10        # More results
qmd search "query" --json       # JSON output
qmd search "query" --all --files --min-score 0.3

Useful options

• -n <num>: number of results
• -c, --collection <name>: restrict to a collection
• --all --min-score <num>: return all matches above a threshold
• --json / --files: agent-friendly output formats
• --full: return full document content

Retrieve

qmd get "path/to/file.md"       # Full document
qmd get "#docid"                # By ID from search results
qmd multi-get "journals/2025-05*.md"
qmd multi-get "doc1.md, doc2.md, #abc123" --json

Maintenance

qmd status                      # Index health
qmd update                      # Re-index changed files
qmd embed                       # Update embeddings

Keeping the index fresh

Automate indexing so results stay current as you add/edit notes.

• For keyword search (qmd search), qmd update is usually enough (fast).
• If you rely on semantic/hybrid search (vsearch/query), you may also want qmd embed, but it can be slow.

Example schedules (cron):

# Hourly incremental updates (keeps BM25 fresh):
0 * * * * export PATH="$HOME/.bun/bin:$PATH" && qmd update

# Optional: nightly embedding refresh (can be slow):
0 5 * * * export PATH="$HOME/.bun/bin:$PATH" && qmd embed

If your Clawdbot/agent environment supports a built-in scheduler, you can run the same commands there instead of system cron.

Models and cache

• Uses local GGUF models; first run auto-downloads them.
• Default cache: ~/.cache/qmd/models/ (override with XDG_CACHE_HOME).

Relationship to Clawdbot memory search

• qmd searches your local files (notes/docs) that you explicitly index into collections.
• Clawdbot's memory_search searches agent memory (saved facts/context from prior interactions).
• Use both: memory_search for "what did we decide/learn before?", qmd for "what's in my notes/docs on disk?".