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extract

extract is a skill for Claude Code from tom5610/llm-wiki. It costs 62 tokens per session (2,488 once invoked), scanned A, original, MIT.

A workflow for turning wiki pages into structured notes, checklists, summaries, templates, or other compact reference material.

In plain words
What is it for?
Use it to extract definitions, procedures, rules, examples, prompt templates, or other organized knowledge from a populated wiki.
Why use it?
It turns existing wiki knowledge into a format that can be reused for a specific downstream task.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution. Also seen: names the AskUserQuestion tool.

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

not rated 3repo 6mo ago A scan 62 tokens original MIT

Good fit Use it to extract definitions, procedures, rules, examples, prompt templates, or other organized knowledge from a populated wiki.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tom5610/llm-wiki/extract
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 extract
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 extract

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/tom5610/llm-wiki/extract"><img src="https://agentmods.dev/badge/skills/tom5610/llm-wiki/extract.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,488 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 $0.00062 $0.02488
Opus 5.5 $0.00025 $0.00995
Sonnet 5.5 $0.00012 $0.00498
Haiku 4.5 $0.00006 $0.00249

Measured 2mo ago against content hash 5301356cdb8e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

extract 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/extract/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Extract workflow

Extract structured knowledge from the wiki for downstream use. Use $ARGUMENTS as the intent description and optional destination (e.g., /extract a checklist for editing LinkedIn posts, /extract all prompt templates as JSON to ./prompts.json, /extract a compact summary of synthetic voice tells).

Prerequisites

  • Verify wiki/index.md exists and the wiki has content. If the wiki is empty, suggest running /ingest on a source first — it will bootstrap the wiki automatically.

Phase 1: Parse intent

From $ARGUMENTS, determine three things:

  1. What knowledge the user wants (definitions, procedures, taxonomies, frameworks, templates, examples, rules/heuristics, named patterns, or a combination)
  2. What output format they want, if stated — otherwise ask in Phase 4
  3. Where to write the output, if stated — otherwise ask in Phase 5

Fast path: If $ARGUMENTS specifies all three (what, format, destination), skip Phase 4 and Phase 5 — proceed directly from Phase 3 to Phase 6. Present a brief confirmation of what you understood before producing output.

If $ARGUMENTS is empty or too vague to determine what knowledge is needed, use the AskUserQuestion tool to clarify the output type. Options: "Structured data (JSON/YAML)" (description: "Machine-readable for apps and APIs"), "Derivative document (checklist, guide)" (description: "Human-readable reference docs"), "Compact knowledge pack" (description: "Dense summary for LLM prompts or handoff"), "Visual format (diagram/slides)" (description: "Mermaid, Marp, or matplotlib output"). If the user picks an option, follow up with a separate AskUserQuestion call to narrow scope (what topic or pages to extract from) — ask one question at a time.

Phase 2: Find relevant pages

Read wiki/index.md. Match the user's intent against the page titles and one-line summaries to identify which wiki pages contain the knowledge needed.

Lens scoping (optional): If the user specifies a lens or domain (e.g., "extract all techniques from the ML lens"), filter candidates to pages with the matching lens frontmatter field. If no lens is specified, include pages from all lenses (default).

Read the full file on GitHub · 142 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 · 142 lines · 62 tokens per session scan A 5301356cdb8e

Subscribe to this mod's changes

extract is a skill published in the GitHub repository tom5610/llm-wiki (3 stars, last pushed 6mo ago), licensed MIT. It adds 62 tokens to every session and 2,488 once invoked, about $0.0002 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.

ResearchWriting and docs
Related

extract compared with skills from other repositories

extract next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
extracttom5610/llm-wikiSkill36mo agoMITA62Claude 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