paper-review by evoscientist/evoskills
npx skills add https://github.com/evoscientist/evoskills --skill paper-review一种在提交前系统性地自我审阅学术论文的方法。涵盖5个方面的审阅清单、用于结构清晰度的反向提纲、图表质量检查以及反驳准备。
如果用户已经收到审稿人意见并需要撰写反驳信,请改用
paper-rebuttal技能。
在开始审阅之前,请确认 paper-writing 交接清单已满足:所有章节已起草完毕,论点均有证据支撑,局限性章节已存在,图表已定稿,且没有未解决的 \todo{} 标记。如果有任何项目未完成,请在审阅前完成写作。
力求完美:审阅你自己的论文,考虑审稿人可能提出的每一个问题,并逐一解决。
抵御负面评审的最佳方法是进行彻底的自我审阅:
在最终润色前运行此协议:
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论文没有为读者提供新知识。
通过以下问题评估贡献是否充分:
危险信号:如果对以上任何一项回答"是",请加强贡献叙述或增加更多技术深度。
缺少技术细节,不可复现;方法模块缺乏动机说明。
危险信号:如果可复现性存疑,请添加实现细节或补充材料。
仅比先前方法略好;或者比先前方法好但仍然不够好。
危险信号:如果改进幅度微小,请强调其他优势(速度、泛化性、简洁性)或添加更具挑战性的测试案例。
缺少消融实验;缺少重要基线;缺少重要评估指标;数据过于简单。
危险信号:缺少消融实验或基线是导致拒稿的最常见原因之一。
实验设置不切实际;方法存在技术缺陷;方法不鲁棒;新方法的成本超过其收益。
危险信号:如果方法需要针对每个场景进行大量调优,请添加鲁棒性实验或承认并说明该局限性。
论文中的每一个论点(尤其是在摘要和引言中)都必须正确且有实验支撑。一些审稿人会因为无支撑的论点而直接拒稿。
仔细检查摘要和引言中的每一个论点。对于每个论点:
一个无支撑的论点——尤其是在摘要或引言中——可能成为拒稿的理由。
从已完成的段落中提取写作计划,并检查流程是否流畅。
在写完一个章节(或整篇论文)后:
将此技术应用于:
\toprule, \midrule, \bottomrule)规则:"如果我们的方法没有低于SOTA指标,那就不是技术缺陷"
当收到审稿意见后,使用 paper-rebuttal 技能进行:
你的自我审阅成果(先模拟拒稿、论点-证据审核、来自反直觉协议的预反驳草稿)可以直接用于反驳过程。
参见 references/review-checklist.md 获取包含更详细子问题的5方面清单扩展版。
关于对抗性压力测试和拒稿风险阈值,请参见 references/counterintuitive-review.md。
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A systematic approach to self-reviewing academic papers before submission. Covers a 5-aspect review checklist, reverse-outlining for structural clarity, figure/table quality checks, and rebuttal preparation.
If the user has already received reviewer comments and needs to write a rebuttal, use the
paper-rebuttalskill instead.
Before starting review, confirm the paper-writing handoff checklist is satisfied: all sections drafted, claims anchored to evidence, limitation section present, figures finalized, and no unresolved \todo{} markers. If any item is incomplete, finish writing before reviewing.
Strive for perfection: review your own paper, consider every question a reviewer might ask, and address them one by one.
The best defense against negative reviews is a thorough self-review:
Run this protocol before final polishing:
See references/counterintuitive-review.md
The paper does not provide readers with new knowledge.
Ask these questions to evaluate whether the contribution is sufficient:
Red flag : If "yes" to any of these, strengthen the contribution narrative or add more technical depth.
Missing technical details, not reproducible; a method module lacks motivation.
Red flag : If reproducibility is in doubt, add implementation details or supplementary material.
Only slightly better than previous methods; or better than previous methods but still not good enough.
Red flag : If improvements are marginal, emphasize other advantages (speed, generalizability, simplicity) or add more challenging test cases.
Missing ablation studies; missing important baselines; missing important evaluation metrics; data too simple.
Red flag : Missing ablations or baselines is one of the most common reasons for rejection.
Experimental setting is impractical; method has technical flaws; method is not robust; new method's costs outweigh its benefits.
Red flag : If the method requires significant tuning per scenario, add robustness experiments or acknowledge and address the limitation.
Every claim in the paper (especially in the Abstract and Introduction) must be correct and supported by experiments. Some reviewers will reject a paper directly for unsupported claims.
Go through every claim in the Abstract and Introduction. For each claim:
An unsupported claim — especially in the Abstract or Introduction — can be grounds for rejection.
Extract the writing plan from finished paragraphs and check whether the flow is smooth.
After writing a section (or the entire paper):
Apply this to:
\toprule, \midrule, \bottomrule)Rule: "If our method does not fall below SOTA metrics, it is not a technical defect"
When reviews come back, use the paper-rebuttal skill for:
Your self-review artifacts (reject-first simulation, claim-evidence audit, prebuttal drafts from the counterintuitive protocol) feed directly into the rebuttal process.
See references/review-checklist.md for an expanded version of the 5-aspect checklist with more detailed sub-questions.
For adversarial stress testing and reject-risk thresholds, see references/counterintuitive-review.md.
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AI论文代码复现工具 - 自动化复现AI论文仓库,实现最小化可信运行
9,100 周安装