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.
npx agentmods add skills/tom5610/llm-wiki/extractnpx skills add tom5610/llm-wiki --skill extractgit clone --depth 1 https://github.com/tom5610/llm-wikiWrote 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.
[](https://agentmods.dev/skills/tom5610/llm-wiki/extract)<a href="https://agentmods.dev/skills/tom5610/llm-wiki/extract"><img src="https://agentmods.dev/badge/skills/tom5610/llm-wiki/extract.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00062 | $0.02488 |
| Opus 5 | $0.00031 | $0.01244 |
| Sonnet 5 | $0.00012 | $0.00498 |
| Haiku 4.5 | $0.00006 | $0.00249 |
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 5d 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.
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.mdexists and the wiki has content. If the wiki is empty, suggest running/ingeston a source first — it will bootstrap the wiki automatically.
Phase 1: Parse intent
From $ARGUMENTS, determine three things:
- What knowledge the user wants (definitions, procedures, taxonomies, frameworks, templates, examples, rules/heuristics, named patterns, or a combination)
- What output format they want, if stated — otherwise ask in Phase 4
- 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).
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.
- 5d ago First seen · 142 lines · 62 tokens per session scan A 5301356cdb8e
extract is a skill published in the GitHub repository tom5610/llm-wiki (2 stars, last pushed 4mo ago), licensed MIT. It adds 62 tokens to every session and 2,488 once invoked, about $0.0003 per session on Opus 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.
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