qmd

qmd is a skill for Claude Code from openclaw/nix-openclaw-tools. It costs 32 tokens per session (1,104 once invoked), scanned A, a copy of qmd, MIT.

A local search tool for finding words, phrases, and related ideas in Markdown notes, documentation, and other text files.

In plain words
What is it for?
Finding documents, searching exact terms or code names, and looking up answers described in natural language.
Why use it?
It helps locate useful information in a collection of notes without opening documents one by one.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: built for openclaw.

Good fit Finding documents, searching exact terms or code names, and looking up answers…

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/openclaw/nix-openclaw-tools/qmd
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add openclaw/nix-openclaw-tools --skill qmd
Clone the repo
git clone --depth 1 https://github.com/openclaw/nix-openclaw-tools

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for qmd

README.md
[![agentmods](https://agentmods.dev/badge/skills/openclaw/nix-openclaw-tools/qmd.svg)](https://agentmods.dev/skills/openclaw/nix-openclaw-tools/qmd)
Your own site
<a href="https://agentmods.dev/skills/openclaw/nix-openclaw-tools/qmd"><img src="https://agentmods.dev/badge/skills/openclaw/nix-openclaw-tools/qmd.svg" alt="Measured on agentmods" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,104 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin 92% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00032 $0.01104
Opus 5 $0.00016 $0.00552
Sonnet 5 $0.00006 $0.00221
Haiku 4.5 $0.00003 $0.00110

Measured 7d ago against content hash 5e505e951452, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

qmd scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -X POST http://localhost:8181/query \
Origin

This is a copy

92% identical to qmd — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

tools/qmd/skills/qmd/SKILL.md · 144 lines

How it starts

The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Local search engine for markdown content.

Status

!qmd status 2>/dev/null || echo "qmd CLI not found on this runtime PATH"

MCP: query

{
  "searches": [
    { "type": "lex", "query": "CAP theorem consistency" },
    { "type": "vec", "query": "tradeoff between consistency and availability" }
  ],
  "collections": ["docs"],
  "limit": 10
}

Query Types

Type Method Input
lex BM25 Keywords — exact terms, names, code
vec Vector Question — natural language
hyde Vector Answer — hypothetical result (50-100 words)

Writing Good Queries

lex (keyword)

  • 2-5 terms, no filler words
  • Exact phrase: "connection pool" (quoted)
  • Exclude terms: performance -sports (minus prefix)
  • Code identifiers work: handleError async

vec (semantic)

  • Full natural language question
  • Be specific: "how does the rate limiter handle burst traffic"
  • Include context: "in the payment service, how are refunds processed"

hyde (hypothetical document)

  • Write 50-100 words of what the answer looks like
  • Use the vocabulary you expect in the result

expand (auto-expand)

  • Use a single-line query (implicit) or expand: question on its own line
  • Lets the local LLM generate lex/vec/hyde variations
  • Do not mix expand: with other typed lines — it's either a standalone expand query or a full query document

Intent (Disambiguation)

When a query term is ambiguous, add intent to steer results:

{
  "searches": [
    { "type": "lex", "query": "performance" }
  ],
  "intent": "web page load times and Core Web Vitals"
}

Intent affects expansion, reranking, chunk selection, and snippet extraction. It does not search on its own — it's a steering signal that disambiguates queries like "performance" (web-perf vs team health vs fitness).

Combining Types

Goal Approach
Know exact terms lex only
Don't know vocabulary Use a single-line query (implicit expand:) or vec
Best recall lex + vec
Complex topic lex + vec + hyde
Ambiguous query Add intent to any combination above

Read the full file on GitHub · 144 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 144 lines · 32 tokens per session scan A 5e505e951452

Subscribe to this mod's changes

qmd is a skill published in the GitHub repository openclaw/nix-openclaw-tools (53 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 1,104 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 92% identical to qmd, differing in 5 lines, and is treated as a copy.

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