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/mr-tbot/auto-everything/auto-docnpx skills add mr-tbot/Auto-Everything --skill auto-docgit clone --depth 1 https://github.com/mr-tbot/Auto-EverythingWrote 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/mr-tbot/auto-everything/auto-doc)<a href="https://agentmods.dev/skills/mr-tbot/auto-everything/auto-doc"><img src="https://agentmods.dev/badge/skills/mr-tbot/auto-everything/auto-doc.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 | $0.00096 | $0.03461 |
| Opus 5 | $0.00048 | $0.01731 |
| Sonnet 5 | $0.00019 | $0.00692 |
| Haiku 4.5 | $0.00010 | $0.00346 |
Grade A, and why
auto-doc 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 3d 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/auto-doc
Find every place this project describes itself, check each against what the code actually does now, and fix what has drifted.
The standing law: a project's documentation is part of its surface area. When the product changes, the docs that teach it are stale until proven otherwise. A stale tutorial is not cosmetic — someone follows it, it does not match, and that becomes a support ticket, a bad review, or a refund.
Step 1 — Enumerate The Doc Surface
Four categories. Miss a category and the audit reports clean on documentation nobody checked.
In-repo: README, CHANGELOG, docs/ trees, doc-site sources (Docusaurus, MkDocs, Sphinx,
VitePress, mdBook, Starlight, Jekyll), ADRs, CONTRIBUTING, SECURITY, code comments and docstrings,
--help output, man pages, and generated API docs (TypeDoc, Sphinx autodoc, rustdoc, Dokka, DocC,
godoc, Doxygen, OpenAPI).
Repo host: description, topics, homepage, the wiki, Pages, Releases and their notes, issue/PR
templates, CODEOWNERS, community-health files, social preview, labels, milestones, Projects.
External: Notion, Confluence, Jira, Linear, GitBook, Outline, BookStack, and friends. Detect which
from inside the repo — URLs in README/CONTRIBUTING/templates, ticket-ID patterns (PROJ-123) in
commit messages and branch names, CI integrations, .env keys, MCP server config — rather than
asking cold.
In-product prose, which is the surface everyone forgets: onboarding copy, empty-state text, error
messages, menu labels, and their translations. strings.xml, Localizable.strings, .arb, i18n
JSON. A removed feature leaves its menu label behind, and when the English string finally gets fixed,
forty locales keep the old promise.
Store listings count too, and they are the highest-severity claim source — a false claim on a store
page outranks a wrong line in a tutorial. Pull them into diffable files rather than eyeballing the
console: fastlane deliver download_metadata for App Store Connect, fastlane supply init for Play.
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.
- 3d ago First seen · 260 lines · 96 tokens per session scan A c44cd892159d
auto-doc is a skill published in the GitHub repository mr-tbot/Auto-Everything (5 stars, last pushed 9d ago), licensed MIT. It adds 96 tokens to every session and 3,461 once invoked, about $0.0005 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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