Borrowing it
Nothing to install: this file belongs to AlessandroCaforio/Academic-Writing. 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/AlessandroCaforio/Academic-Writing/main/.claude/skills/review-code/SKILL.mdgit clone --depth 1 https://github.com/AlessandroCaforio/Academic-WritingWrote 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/alessandrocaforio/academic-writing/review-code)<a href="https://agentmods.dev/skills/alessandrocaforio/academic-writing/review-code"><img src="https://agentmods.dev/badge/skills/alessandrocaforio/academic-writing/review-code.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 5.1 | $0.00017 | $0.00185 |
| Opus 5 | $0.00009 | $0.00093 |
| Sonnet 5 | $0.00003 | $0.00037 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
review-code 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 6d 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.
What it actually says
Review Code
Review a Python analysis notebook or script. The argument should be the notebook path.
Instructions
- Read the specified notebook/file at the given path
- Read
thesis/chapters/04_methodology.texfor formula reference - Read
.claude/rules/python-conventions.mdfor coding standards - Follow the code-reviewer agent instructions
Example Usage
/review-code analysis/02_task_displacement/labor_share.ipynb/review-code analysis/04_cces_analysis/CCES_automation_analysis.ipynb/review-code src/utils.py
If no argument is provided, review the most recently modified notebook in analysis/.
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.
- 6d ago First seen · 26 lines · 17 tokens per session scan A 149aaed62e71
review-code is a skill published in the GitHub repository AlessandroCaforio/Academic-Writing (17 stars, last pushed 5mo ago), licensed MIT. It adds 17 tokens to every session and 185 once invoked, about $0.0001 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.
Other skills, from other repositories
review-r
Read-only R code review protocol for .R scripts. Checks code quality, reproducibility, domain correctness, tidyverse idioms, and professional standards; produces a report without editing. Use when user says "review this R script", "check the R code", "audit the analysis code", "code review on the R", or when an R file…
proofcheck
Systematically verify mathematical proofs in statistics/ML theory paper appendices. Use when user says "proof check", "check proofs", "verify proofs", "audit paper", "检查证明", "证明验证", or wants to verify correctness of a paper's mathematical proofs.
theory-sharpen
Systematically assess whether a paper's theoretical results can be strengthened: relax assumptions, sharpen rates, align theory with model and experiments, and benchmark against state-of-the-art literature. Use when user says "sharpen theory", "strengthen results", "relax assumptions", "improve rates", "理论提升", "放宽假设"…
theory-simulation
Bridge between theoretical results and Monte Carlo simulation, built to top-stat-journal standards (AoS, JASA, JRSS-B, Biometrika, Bernoulli). Two modes: (1) DESIGN mode — for each theoretical claim, design new simulations that verify rates, coverage, stress-test assumptions, and reveal theory-improvement…
proof-repair
Generate self-consistent repair plans for mathematical proof issues found by /proofcheck, with literature-backed support. For each problematic assumption, model, proposition, or theorem, proposes fixes that preserve the full dependency chain and searches arXiv, Semantic Scholar, and Google Scholar for new references…
theory-design
Design a coherent theoretical framework for a new statistics / ML theory research topic, paper-type aware. Three modes — Theory paper (explain phenomena or provide new theoretical tools), Methodology paper (propose a new method with theoretical guarantees), Application paper (apply existing methods to scientific data…