Norman-bury/research-writing-skill is an agent skill that turns academic paper writing into a tracked, reusable workflow with planning, drafting, reviews, figures, literature work, and LaTeX outputs. It is intended for undergraduate students, graduate students, and early-career researchers working on theses, coursework papers, or initial submissions. Its catalogue entries are the skills, instructions, plugin, and hook that implement the workflow across coding-agent platforms.
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 Norman-bury/research-writing-skill --skill peer-reviewgit clone --depth 1 https://github.com/Norman-bury/research-writing-skillWrote 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/norman-bury/research-writing-skill/peer-review)<a href="https://agentmods.dev/skills/norman-bury/research-writing-skill/peer-review"><img src="https://agentmods.dev/badge/skills/norman-bury/research-writing-skill/peer-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/norman-bury/research-writing-skill/peer-review"><img src="https://agentmods.dev/badge/skills/norman-bury/research-writing-skill/peer-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 | $0.00023 | $0.01418 |
| Opus 5 | $0.00012 | $0.00709 |
| Sonnet 5 | $0.00005 | $0.00284 |
| Haiku 4.5 | $0.00002 | $0.00142 |
Grade A, and why
peer-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 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
同行评审与自审
本技能提供论文评审、自我审视和批判性分析的完整指南。
Checklist
- 初步评估：把握论文核心
- 逐节详细审查
- 方法论和统计严谨性评估
- 可重复性和透明度检查
- 撰写审稿报告
- 提供具体改进建议
一、同行评审工作流程
阶段1：初步评估
关键问题：
- 核心研究问题或假设是什么？
- 主要发现和结论是什么？
- 工作是否科学合理且有意义？
- 是否适合目标期刊/会议？
- 是否存在明显的重大缺陷？
输出：2-3句话的简要总结。
阶段2：逐节详细审查
摘要与标题
- 摘要是否准确反映研究内容和结论？
- 标题是否具体、准确、信息丰富？
引言
- 背景信息是否充分且最新？
- 研究问题是否有明确的动机和理由？
- 相关先前研究是否被适当引用？
方法
- 其他研究者能否根据描述复现研究？
- 方法是否适合解决研究问题？
- 统计方法是否适当？
结果
- 结果是否逻辑清晰地呈现？
- 图表是否适当、清晰且正确标注？
- 是否包含所有相关结果？
讨论
- 结论是否有数据支持？
- 研究局限性是否被承认和讨论？
- 推测是否与数据支持的结论明确区分？
阶段3：方法论和统计严谨性
统计评估：
- 统计假设是否满足？
- 是否报告效应量和p值？
- 是否适当应用多重检验校正？
- 样本量是否有功效分析支持？
实验设计：
- 对照是否适当和充分？
- 重复是否足够？
- 潜在混杂因素是否被控制？
二、审稿报告结构
总结陈述
## 总体评估
**研究概述**：[1-2句话概括]
**总体建议**：[接受/小修/大修/拒稿]
**关键优点**：
1. [优点1]
2. [优点2]
**关键缺点**：
1. [缺点1]
2. [缺点2]
主要意见
显著影响论文有效性的关键问题：
- 基本方法论缺陷
- 不适当的统计分析
- 不支持或过度陈述的结论
- 缺少关键对照或实验
次要意见
改善清晰度和完整性的问题：
- 图表标签或图例不清晰
- 缺少方法细节
- 排版或语法错误
三、批判性思维框架
偏倚检测
| 偏倚类型 | 检查要点 |
|---|---|
| 确认偏倚 | 是否只强调支持性发现？ |
| 选择偏倚 | 样本是否代表目标人群？ |
| 发表偏倚 | 是否缺少阴性结果？ |
| P-hacking | 是否多次分析直到显著？ |
逻辑谬误识别
| 谬误类型 | 表现 |
|---|---|
| 事后归因 | "B跟在A后面，所以A导致了B" |
| 相关=因果 | 混淆关联与因果 |
| 草率泛化 | 从小样本得出广泛结论 |
| 挑选数据 | 只选择支持性证据 |
四、自我审稿提示词
投稿前自查
# Role
你是一位严苛的资深学术审稿人。
# Task
请深入阅读并分析我的论文，撰写严厉但建设性的审稿报告。
# 审查维度
1. **原创性**：实质性突破还是边际增量？
2. **严谨性**：推导是否有跳跃？实验对比是否公平？
3. **一致性**：声称的贡献是否得到验证？
# Output
- Part 1 [Review Report]：Summary, Strengths, Weaknesses, Rating
- Part 2 [Strategic Advice]：具体改进建议
# Input
投稿目标：[期刊/会议名]
论文内容：[粘贴]
快速质量检查
# Task
快速检查论文是否存在以下问题：
1. 逻辑一致性：引言的声明是否在实验中得到验证？
2. 术语一致性：核心概念是否保持一致命名？
3. 数据支持：所有结论是否有数据支持？
4. 对照完整性：是否与足够的baseline比较？
5. 消融充分性：是否验证了每个关键模块？
# Output
- 无问题：[检测通过]
- 有问题：分点列出位置和具体问题
五、审稿语气
最佳实践
- 建设性：将批评框架为改进机会
- 具体：提供具体例子和可操作建议
- 平衡：承认优点和缺点
- 尊重：记住作者投入了大量努力
- 客观：关注科学，而非科学家
避免事项
- 人身攻击或轻蔑语言
- 没有具体例子的模糊批评
- 要求超出范围的不必要实验
- 在双盲评审中暴露身份
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 · 194 lines · 23 tokens per session scan A b540c455b8e1
peer-review is a skill published in the GitHub repository Norman-bury/research-writing-skill (3,195 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 1,418 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.
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