Borrowing it
Nothing to install: this file belongs to rigortype/rigor. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/rigortype/rigor/master/.claude/skills/rigor-docs-review/SKILL.mdgit clone --depth 1 https://github.com/rigortype/rigorWrote 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/rigortype/rigor/rigor-docs-review)<a href="https://agentmods.dev/skills/rigortype/rigor/rigor-docs-review"><img src="https://agentmods.dev/badge/skills/rigortype/rigor/rigor-docs-review/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.
<a href="https://agentmods.dev/skills/rigortype/rigor/rigor-docs-review"><img src="https://agentmods.dev/badge/skills/rigortype/rigor/rigor-docs-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00055 | $0.02891 |
| Opus 5.5 | $0.00022 | $0.01156 |
| Sonnet 5.5 | $0.00011 | $0.00578 |
| Haiku 4.5 | $0.00006 | $0.00289 |
Grade A, and why
rigor-docs-review 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 11d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
rigor-docs-review — user-facing docs review battery
Reviews Rigor's user-facing documentation — docs/manual/
(operational reference) and docs/handbook/ (type-model
walkthrough) — from multiple expert lenses run as independent-context subagents, records each to
a note, and applies only the necessary, axis-preserving fixes. This skill is the methodology
freeze; the personas are re-pointed at whatever chapter/scope each run targets.
The full design rationale (what ports from the chibirigor book battery, what drops, what inverts, why
L0 is new) is docs/notes/20260610-user-docs-review-battery-design.md.
Read it once; do not restate it here.
The one load-bearing constraint: this is software documentation, not a book. Prose depth is not a virtue. A book battery rewards narrative richness and woven-in background; here the reference job is correctness and necessary clarity, and background thinness is not a defect. This is why L3 is inverted (a bloat detector, not a depth booster) and why no lens is allowed to recommend padding.
When to use / not
- Use: a manual/handbook chapter reached a stopping point; pre-release doc pass; "does the doc still match the implementation?"; the user says "review the docs / 査読して / 校閲して".
- Do NOT use: a single-line fix or an obvious typo — the lens-launch overhead is not worth it.
The five layers
Run order is L0 → L1 → L2 → L3 → L4 (機械 → 真 → 伝 → 簡 → 整). A full cycle runs at milestones; day-to-day, run only the layer in question (see "Which layer to run"). Parallel within a layer, sequential across — a later layer reads the text the earlier layer's fixes already corrected, because polishing an incorrect claim, or tidying prose a reader can't follow, is wasted work.
| Layer | Question | Form |
|---|---|---|
| L0 機械 | Do the mechanically-checkable claims match reality? | spec/docs/ (make docs-check) — a permanent gate, not an LLM lens |
| L1 真 | Are the semantic claims true against the spec / implementation? | LLM lens (fidelity, + a type-theory sub-lens for the type-theory appendix) |
| L2 伝 | Does it reach its declared reader? | LLM lenses (Ruby-only reader · procedure reproduction · appendix reader) |
| L3 簡 | Is it fat? (thinness is L2's job, not this one) | LLM lens (bloat detector, inverted) |
| L4 整 | Is the English / terminology / cross-referencing clean? | LLM lens (copyedit — always last) |
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.
- 11d ago First seen · 172 lines · 55 tokens per session scan A 5791c0a0c9da
rigor-docs-review is a skill published in the GitHub repository rigortype/rigor (107 stars, last pushed today), licensed MPL-2.0. It adds 55 tokens to every session and 2,891 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 2026-10-26.
rigor-docs-review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| rigor-docs-reviewrigortype/rigor | Skill | 107 | today | MPL-2.0 | A | 55 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |