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 5.5 | $0.00036 | $0.01222 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (635 stars, last pushed 2mo ago), licensed MIT. It adds 91 tokens to every session and 3,055 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…
exam-forecast
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.