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query

query is a skill for Claude Code from tom5610/llm-wiki. It costs 91 tokens per session (1,715 once invoked), scanned A, original, MIT.

A question-and-search workflow for a markdown wiki. It either lists relevant pages for keywords or writes a cited answer by combining information from the wiki.

In plain words
What is it for?
Looking up topics, locating pages, explaining wiki content, summarizing information, and answering questions with references to the source pages.
Why use it?
It helps users find the right documentation and understand it without manually opening and comparing pages.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution. Also seen: mentions CLAUDE.md; names the AskUserQuestion tool.

Part of the llm-wiki plugin — 5 skills, 1 hook shipped together

not rated 3repo 6mo ago A scan 91 tokens original MIT

Good fit Looking up topics, locating pages, explaining wiki content, summarizing information, and answering questions with references to the source pages.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tom5610/llm-wiki/query
View source ↗ tom5610/llm-wiki
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 tom5610/llm-wiki --skill query
Clone the repo
git clone --depth 1 https://github.com/tom5610/llm-wiki

Made for: Claude Code.

Or install llm-wiki, the plugin that ships this one along with the rest of its 5 skills, 1 hook.

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 query

README.md
[![agentmods](https://agentmods.dev/badge/skills/tom5610/llm-wiki/query/github.svg)](https://agentmods.dev/skills/tom5610/llm-wiki/query)
Your own site
<a href="https://agentmods.dev/skills/tom5610/llm-wiki/query"><img src="https://agentmods.dev/badge/skills/tom5610/llm-wiki/query/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for query

Your own site · 80×15
<a href="https://agentmods.dev/skills/tom5610/llm-wiki/query"><img src="https://agentmods.dev/badge/skills/tom5610/llm-wiki/query.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 91 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,715 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 $0.00091 $0.01715
Opus 5.5 $0.00036 $0.00686
Sonnet 5.5 $0.00018 $0.00343
Haiku 4.5 $0.00009 $0.00171

Measured 2mo ago against content hash cc739705a823, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

query scanned grade A with 0 findings 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 2mo 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/query/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Query workflow

The user asks a question. Use $ARGUMENTS as the question if provided (e.g., /query How do the main concepts in the wiki relate to each other?).

Prerequisites

  • Verify wiki/index.md exists. If missing, suggest running /ingest on a source first — it will bootstrap the wiki automatically.

  1. Detect mode. Determine whether the user wants navigation or understanding:

    • Navigational — the input is keywords, a lookup, or asks "where is X?" / "which pages mention X?" / "find X". The user wants a results list, not a composed answer.
    • Synthesis — the input is a natural language question, asks for explanation, comparison, or summary. The user wants an answer.

    Mode override: If the user explicitly says "just search" or "just list pages", use navigational mode regardless of input structure. If the user says "explain" or "synthesize", use synthesis mode. When in doubt, default to synthesis — it's strictly more useful than a list of links.

  2. Find relevant pages.

    • Read wiki/index.md to identify candidate pages.
    • Lens filtering (optional): If the user's question specifies a domain or explicitly requests results from a particular lens (e.g., "in the ML lens" or "within compliance"), filter candidates to pages with the matching lens frontmatter field. If no lens is specified, search across all pages (default).
    • If the index is insufficient, use a tiered search:
      • Tier 1 — Filenames: Scan filenames in wiki/ for query term matches.
      • Tier 2 — Headings and frontmatter: Grep for the query in page titles, headings (#), and frontmatter tags/title fields.
      • Tier 3 — Body text: Grep the full body text of remaining pages.
      • For wikis with >50 pages, stop after Tier 2 if 5+ matches are found.

  3. Respond according to mode.

    If navigational: Present a concise results list. For each matching page, show:

    • Page title as a [[wikilink]]
    • Page type and tags
    • Most relevant excerpt (1-3 lines of context)
    • If no results: suggest related terms, alternative spellings, or rephrasing as a synthesis question.
    • Do not read full pages or synthesize — keep it cheap and fast.

Read the full file on GitHub · 83 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. 2mo ago First seen · 83 lines · 91 tokens per session scan A cc739705a823

Subscribe to this mod's changes

query is a skill published in the GitHub repository tom5610/llm-wiki (3 stars, last pushed 6mo ago), licensed MIT. It adds 91 tokens to every session and 1,715 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Research
Related

query compared with skills from other repositories

query next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
querytom5610/llm-wikiSkill36mo agoMITA91Claude Code
imagegenopenai/codexSkill134ktodayApache-2.0A113Codex
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79kyesterdayMITA200Claude Code, Codex
playwright-climicrosoft/playwrightSkill100kyesterdayApache-2.0A19Claude Code
browser-automationopenclaw/openclawSkill394ktodayMITA36Claude Code
systematic-debuggingobra/superpowersSkill314k4d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143kyesterdayMITA95Claude Code