Qmd

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's requirements and instructions match its stated purpose (local markdown search); it indexes local files and may download local models, which is expected behavior for this tool.

Purpose & Capability

Name/description, required binary (qmd), and instructions align: the skill is a wrapper for the qmd CLI and only needs that binary (and Bun for installation). Nothing requested is unrelated to local markdown search.

Instruction Scope

SKILL.md instructs the agent to run qmd commands, add collections, and (optionally) schedule qmd update/embed. It explicitly operates on user-provided file paths and local caches. The only broader behavior is that qmd may auto-download GGUF models and will read/index files you point it at — this is coherent with its purpose but worth noting.

Install Mechanism

The skill is instruction-only (no automatic install), but the README metadata recommends installing via Bun from the project's GitHub repo (bun install -g https://github.com/tobi/qmd) and suggests brew install sqlite for macOS. Installing via Bun from the repo and qmd's auto-download of GGUF models is a moderate-risk action (network downloads, large model files) but is expected for a local LLM-based search tool.

Credentials

No credentials or sensitive env vars are requested. The instructions reference PATH adjustments (e.g., $HOME/.bun/bin) and cache paths (~/.cache/qmd/models) which are proportional to running qmd. Note: indexing a directory will read local files you point it at — index contents should be chosen carefully.

Persistence & Privilege

The skill does not request always: true or other elevated platform privileges, nor does it instruct modifying other skills. It may be used to run long-lived processes or scheduled jobs (user-configured), which is normal for this use case.

Scan Findings in Context

[no_regex_findings] expected: The package is instruction-only with no code files, so the regex scanner had nothing to analyze. This is expected; runtime behavior is driven by the qmd CLI invoked by the agent.

Assessment

This skill is internally consistent for local markdown search, but consider the following before installing: 1) qmd will index whatever directories you add — avoid indexing sensitive directories or configure masks to exclude secrets. 2) The recommended install uses Bun to fetch code from the project's GitHub repo and qmd may auto-download GGUF models into ~/.cache/qmd/models; ensure you trust the upstream project and have disk/network capacity. 3) If you schedule automatic updates/embeddings (cron or agent scheduler), those jobs will run qmd commands with the PATH you provide — be careful what PATH and environment you export in scheduled contexts. If you want extra assurance, install qmd manually from the official repo, verify checksums/releases, and test indexing on a small, non-sensitive subset of files first.

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?".