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 skills add yha9806/academic-writing-toolkit --skill self-reviewgit clone --depth 1 https://github.com/yha9806/academic-writing-toolkitWrote 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/yha9806/academic-writing-toolkit/self-review)<a href="https://agentmods.dev/skills/yha9806/academic-writing-toolkit/self-review"><img src="https://agentmods.dev/badge/skills/yha9806/academic-writing-toolkit/self-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/yha9806/academic-writing-toolkit/self-review"><img src="https://agentmods.dev/badge/skills/yha9806/academic-writing-toolkit/self-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.01259 |
| Opus 5 | $0.00034 | $0.00629 |
| Sonnet 5 | $0.00014 | $0.00252 |
| Haiku 4.5 | $0.00007 | $0.00126 |
Grade A, and why
self-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 7d 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/self-review - Clean-Room Manuscript Self-Review
Purpose
Audit the user's own work without letting memory, prior chats, unstated project knowledge, or the model's background knowledge become evidence.
The governing rule is:
self-review truth = explicit review packet + source anchor
Use /argument-governance first when the manuscript needs a formal intent, contribution, claim, and evidence map.
Codex-Only Baseline
Complete self-review with Codex, the review manifest, allowed sources, and the bundled packet checker. Do not require Gemini, gemini-agent, a second model, or a subagent. If an external review is available, keep it in Reviewer-risk inference or advisory notes and never use it as source support.
If /argument-governance is unavailable, manually extract the same clean-room argument spine from manifest-listed sources only.
Enhanced Advisory Mode
If the manifest and the user explicitly allow an API-key-backed advisory review, Codex may run or incorporate a second-model pass after the clean-room self-review packet is valid.
Rules:
- the base clean-room review must be possible without the external call
- API keys must be read from environment variables only
- the manifest may name
api_key_env_var, but must never store the key value - only manifest-approved source subsets may be sent externally
- external findings must be placed under
Reviewer-risk inferenceor advisory notes - external findings must be re-grounded against allowed sources before becoming revision actions
- unsupported external comments stay unsupported
Core Rules
- Use only files listed in the review manifest.
- Treat prior chat memory, unstated project assumptions, model background knowledge, and unlisted notes as forbidden evidence.
- Split every finding into
Supported by packet,Not supported by packet, orReviewer-risk inference. - Every supported finding must include a source anchor.
- Do not repair missing evidence by remembering earlier conversations.
- Do not treat generated reviews, agent drafts, or reviewer simulations as final evidence.
- Do not edit the manuscript until the user approves specific revision actions.
- Do not treat an unavailable external review tool as a blocker.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago Changed · +23 lines · -15 tokens per session 463ceec99918
- 12d ago First seen · 152 lines · 84 tokens per session scan A dca04da26ce4
self-review is a skill published in the GitHub repository yha9806/academic-writing-toolkit (38 stars, last pushed 3d ago), licensed MIT. It adds 69 tokens to every session and 1,259 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 2026-08-30.
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