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 runtime instructions align with its stated purpose (local markdown search); it asks to run the qmd binary, index local files, and may download local models — all expected for this functionality.

Purpose & Capability

Name/description (local hybrid search for markdown) match the declared requirement (qmd binary) and the SKILL.md commands (qmd search, vsearch, query, embed, update, etc.). There are no unrelated credentials, binaries, or config paths requested.

Instruction Scope

Instructions direct the agent to run qmd commands and to index and read user-specified markdown files (e.g., qmd collection add /path/to/notes). This is expected for a local search tool. SKILL.md also states that qmd will auto-download GGUF models to ~/.cache/qmd/models/ and may load local LLMs for semantic search (vsearch/query), which implies network downloads, substantial disk usage, and high memory when models are loaded — an expected but important side effect to be aware of.

Install Mechanism

The skill is instruction-only (no packaged install spec in the registry), but SKILL.md suggests installing qmd via Bun (bun install -g https://github.com/tobi/qmd). Installing from a public GitHub repo via Bun is a common pattern but carries the usual moderate risk of executing third-party code from upstream; the instruction is not using obscure hosts or shorteners. Registry/metadata mismatch: registry listed no install spec while SKILL.md contains install instructions — benign but slightly inconsistent.

Credentials

The skill requests no credentials or special env vars. It mentions PATH and XDG_CACHE_HOME only for practical setup and cache override. No disproportionate secret access is requested.

Persistence & Privilege

always is false and the skill does not request persistent/global agent privileges. SKILL.md suggests optionally running recurring index/embed jobs (cron) — this is user-controlled and not enforced by the skill. The skill does not modify other skills or agent-wide config.

Assessment

This skill appears coherent and implements a local markdown search: it will read and index whatever files you point it at (so avoid indexing secrets), and it may download and store local model files (GGUF) to ~/.cache/qmd/models/ and load them into memory for semantic search which can be slow and resource-heavy. Installing qmd as suggested (bun install -g from GitHub) will execute third-party code — review the upstream repository before installing. If you plan to automate indexing (cron or agent scheduler), restrict the collection paths and run with a user account with limited privileges. If you have sensitive notes, either exclude those paths or run qmd in a sandboxed environment. Overall the skill is internally consistent with its stated purpose, but verify the upstream qmd project and its model-download/network behavior before installing.

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?".