检测可能阻塞Python异步处理的输入输出调用,并推动修复和补充测试。验证问题重现时测试确实失败、恢复修复后通过,以监测问题再次出现。
- 检查异步Python代码变更
- 排查整个代码库的输入输出阻塞风险
- 补充清理与错误分支的测试
阅读学术论文,评估主要论点、证据、研究方法、创新性及其与相关研究的关系,具体指出优点与不足,并整理成包含改进建议的评审。
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
从PDF或URL读取学术论文,梳理主要论点及其证据,形成有依据的评审。评估研究方法的合理性、创新性、实验设计、统计支持和可复现性,并结合正文中的章节、图表说明优点与不足。还会查阅相关文献,将论文与已有研究的关系、向作者提出的问题及具体改进建议整理为Markdown文稿。
适合既想了解论文要点,又想判断结果可信程度的读者。可用于起草会议或期刊的同行评审意见,也可用于投稿前的检查。针对实验论文、理论论文和综述调整评估重点,深度可从简短评价调整到详细评审。
核查相关研究时使用网页搜索和web_fetch。付费论文仅评估摘要或公开版本等可访问内容。评审会说明无法核实的内容等局限,并遵循注重未发表研究保密性的处理方针。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月21日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
This skill produces structured, peer-review-quality analyses of academic papers and research publications. It follows established academic review standards used by top-tier venues (NeurIPS, ICML, ACL, Nature, IEEE) to provide rigorous, constructive, and balanced assessments.
The review covers summary, strengths, weaknesses, methodology assessment, contribution evaluation, literature positioning, and actionable recommendations — all grounded in evidence from the paper itself.
Always load this skill when:
Thoroughly read and understand the paper before forming any judgments.
Extract and record:
| Field | Description |
|---|---|
| Title | Full paper title |
| Authors | Author list and affiliations |
| Venue / Status | Publication venue, preprint server, or submission status |
| Year | Publication or submission year |
| Domain | Research field and subfield |
| Paper Type | Empirical, theoretical, survey, position paper, systems paper, etc. |
Read the paper systematically:
List the paper's main claims explicitly:
Claim 1: [Specific claim about contribution or finding]
Evidence: [What evidence supports this claim in the paper]
Strength: [Strong / Moderate / Weak]
Claim 2: [...]
...
Use web search to understand the research landscape:
Search queries:
- "[paper topic] state of the art [current year]"
- "[key method name] comparison benchmark"
- "[authors] previous work [topic]"
- "[specific technique] limitations criticism"
- "survey [research area] recent advances"
Use web_fetch on key related papers or surveys to understand where this work fits.
Evaluate the methodology using the following framework:
| Criterion | Questions to Ask | Rating |
|---|---|---|
| Soundness | Is the approach technically correct? Are there logical flaws? | 1-5 |
| Novelty | What is genuinely new vs. incremental improvement? | 1-5 |
| Reproducibility | Are details sufficient to reproduce? Code/data available? | 1-5 |
| Experimental Design | Are baselines fair? Are ablations adequate? Are datasets appropriate? | 1-5 |
| Statistical Rigor | Are results statistically significant? Error bars reported? Multiple runs? | 1-5 |
| Scalability | Does the approach scale? Are computational costs discussed? | 1-5 |
Evaluate the significance level:
| Level | Description | Criteria |
|---|---|---|
| Landmark | Fundamentally changes the field | New paradigm, widely applicable breakthrough |
| Significant | Strong contribution advancing the state of the art | Clear improvement with solid evidence |
| Moderate | Useful contribution with some limitations | Incremental but valid improvement |
| Marginal | Minimal advance over existing work | Small gains, narrow applicability |
| Below threshold | Does not meet publication standards | Fundamental flaws, insufficient evidence |
For each strength or weakness, provide:
Produce the final review using the template below.
# Paper Review: [Paper Title]
## Paper Metadata
- **Authors**: [Author list]
- **Venue**: [Publication venue or preprint server]
- **Year**: [Year]
- **Domain**: [Research field]
- **Paper Type**: [Empirical / Theoretical / Survey / Systems / Position]
## Executive Summary
[2-3 paragraph summary of the paper's core contribution, approach, and main findings.
State your overall assessment upfront: what the paper does well, where it falls short,
and whether the contribution is sufficient for the claimed venue/impact level.]
## Summary of Contributions
1. [First claimed contribution — one sentence]
2. [Second claimed contribution — one sentence]
3. [Additional contributions if any]
## Strengths
### S1: [Concise strength title]
[Detailed explanation with specific references to sections, figures, or tables in the paper.
Explain WHY this is a strength and its significance.]
### S2: [Concise strength title]
[...]
### S3: [Concise strength title]
[...]
## Weaknesses
### W1: [Concise weakness title]
[Detailed explanation with specific references. Explain the impact of this weakness on
the paper's claims. Suggest how it could be addressed.]
### W2: [Concise weakness title]
[...]
### W3: [Concise weakness title]
[...]
## Methodology Assessment
| Criterion | Rating (1-5) | Assessment |
|-----------|:---:|------------|
| Soundness | X | [Brief justification] |
| Novelty | X | [Brief justification] |
| Reproducibility | X | [Brief justification] |
| Experimental Design | X | [Brief justification] |
| Statistical Rigor | X | [Brief justification] |
| Scalability | X | [Brief justification] |
## Questions for the Authors
1. [Specific question that would clarify a concern or ambiguity]
2. [Question about methodology choices or alternative approaches]
3. [Question about generalizability or practical applicability]
## Minor Issues
- [Typos, formatting issues, unclear figures, notation inconsistencies]
- [Missing references that should be cited]
- [Suggestions for improved clarity]
## Literature Positioning
[How does this work relate to the current state of the art?
Are key related works cited? Are comparisons fair and comprehensive?
What important related work is missing?]
## Recommendations
**Overall Assessment**: [Accept / Weak Accept / Borderline / Weak Reject / Reject]
**Confidence**: [High / Medium / Low] — [Justification for confidence level]
**Contribution Level**: [Landmark / Significant / Moderate / Marginal / Below threshold]
### Actionable Suggestions for Improvement
1. [Specific, constructive suggestion]
2. [Specific, constructive suggestion]
3. [Specific, constructive suggestion]
| Paper Type | Focus Areas |
|---|---|
| Empirical | Experimental design, baselines, statistical significance, ablations, reproducibility |
| Theoretical | Proof correctness, assumption reasonableness, tightness of bounds, connection to practice |
| Survey | Comprehensiveness, taxonomy quality, coverage of recent work, synthesis insights |
| Systems | Architecture decisions, scalability evidence, real-world deployment, engineering contributions |
| Position | Argument coherence, evidence for claims, impact potential, fairness of characterizations |
Before finalizing the review, verify:
/mnt/user-data/outputs/review-{paper-topic}.md when working in sandboxpresent_files tooldeep-research skill — load both when the user wants the paper reviewed in the context of the broader field还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
检测可能阻塞Python异步处理的输入输出调用,并推动修复和补充测试。验证问题重现时测试确实失败、恢复修复后通过,以监测问题再次出现。
通过对话梳理工作、偏好及希望交由AI判断的范围,创建或更新规定AI伙伴行为的SOUL.md,并明确说话方式和表达异议的规则。
根据数据特点,从26种图表和示意图中选择合适的形式并生成图片,可展示时间变化、比较、分布和关联,并返回图片链接及生成时使用的完整配置。
通过HTTP API向运行中的DeerFlow发送调研和分析请求,获取回答、继续对话并上传资料。还可查看模型、技能、智能体列表及服务运行状态。
阅读实际源代码,按项目规模编写或完善包含安装步骤与用法示例的README、API文档、架构图、开发者指南及变更日志。
为市场、竞争对手、消费者或财务调研设计章节、所需数据和图表计划,再将已收集的信息整理为包含对比、来源及战略启示的报告,并明确标注缺失数据。