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 NeuroDong/Ai-Review --skill ai-review-skillsgit clone --depth 1 https://github.com/NeuroDong/Ai-ReviewWrote 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/neurodong/ai-review/ai-review-skills)<a href="https://agentmods.dev/skills/neurodong/ai-review/ai-review-skills"><img src="https://agentmods.dev/badge/skills/neurodong/ai-review/ai-review-skills/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/neurodong/ai-review/ai-review-skills"><img src="https://agentmods.dev/badge/skills/neurodong/ai-review/ai-review-skills.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 | $0.00091 | $0.03055 |
| Opus 5 | $0.00046 | $0.01528 |
| Sonnet 5 | $0.00018 | $0.00611 |
| Haiku 4.5 | $0.00009 | $0.00305 |
Grade A, and why
ai-review-skill 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 12d 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ai-Review Skill (SoT)
Produces structured, evidence-anchored paper reviews with no scores or accept/reject. Supports LaTeX, PDF, and Word. For English manuscripts use the SoT Prompt (English) section below; for Chinese manuscripts use the SoT Prompt (Chinese) section below.
When to Use
User says "review my paper", "审稿", "论文审稿", "review this manuscript", or provides a path to a manuscript file (.tex, .pdf, .docx, .doc).
Workflow
Step 1: Obtain manuscript content
- LaTeX (
.tex): Read the file(s). For multi-file projects, read the main file and any\input/\includefiles to assemble full text. Strip or ignore\bibliography/\citeonly if needed for length. - PDF (
.pdf): Extract text accurately. Prefer in order: (1) any skill in this project’s.cursor/skills/or~/.cursor/skills/that extracts PDF text; (2)pdftotext -layout "file.pdf" -(poppler-utils); (3) Python with PyMuPDF (fitz),pdfplumber, orpypdf(e.g.page.get_text()or equivalent). Preserve section order. - Word (
.docx/.doc): Extract text. Preferpython-docxfor.docx(paragraphs + tables); ormammothfor.docxto markdown. For.doc, use mammoth or suggest converting to.docxfirst.
If no file is given, ask for the manuscript path.
Step 2: Detect manuscript language
From the extracted or read text, decide if the paper is mainly English or mainly Chinese (title, abstract, headings, body). English paper → follow SoT Prompt (English) below. Chinese paper → follow SoT Prompt (Chinese) below.
Step 3: Generate the review
Use the manuscript text as the [Input] to the chosen SoT prompt. Follow that prompt’s multi-stage process and output exactly the six sections in order. No scores, ratings, or accept/reject. Every claim must have an evidence anchor or "No direct evidence found in the manuscript."
SoT Prompt (English) — use for English manuscripts
Apply the following prompt in full when the manuscript is in English.
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.
- 12d ago First seen · 225 lines · 91 tokens per session scan A b230ef41bb12
ai-review-skill is a skill published in the GitHub repository NeuroDong/Ai-Review (612 stars, last pushed 18d ago), licensed MIT. It adds 91 tokens to every session and 3,055 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 2026-08-30.
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