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nature-paper-xray

精读一篇用户提供的论文,生成结构化Markdown讲解,重建研究逻辑与关键设计,用具体数值演算重要公式,并检查图表、结论及证据边界。

原文查看原文说明

Read one supplied paper closely enough to reconstruct how its authors actually got there, rather than restating what the abstract claims. Use for 精读论文, 讲透这篇论文, 把这篇读懂, 作者为什么这么设计, 这个公式怎么推出来的, paper x-ray, and deep single-paper comprehension before building on, reviewing, presenting, or citing that paper. Recovers the real starting point and the bet the authors placed, separates load-bearing design from decoration, grounds each key formula in a worked micro-example with concrete numbers, and checks how far the evidence actually reaches, including where the authors are confident and where the paper does not report something. Delivers a structured Markdown long-read by default. Not a summary, not a translation, not a peer review.

实测评分 未实测

查看安装方法

适用场景

  • 深入理解一篇论文再开展后续研究
  • 准备论文讲解或报告
  • 用具体数字理解数学公式
  • 找出支撑结果的设计选择
  • 引用或评审前核查证据边界

中文介绍

功能

nature-paper-xray会精读用户提供的一篇论文,并生成结构化的Markdown长篇讲解。它会梳理研究试图解决的问题、作者依赖的证据,以及影响结果的设计选择。提供了附录、补充材料、图注和表注时,也会一并阅读;它还会结合消融实验或受控比较,分析设计选择的作用,并用具体数字演算重要公式。技能会核对图、表和结论是否一致,并标出论文未报告的信息。

适用场景

在基于某篇论文开展后续研究之前,或准备报告、评审和引用时,如果需要深入理解论文,可以使用此技能。它能帮助梳理比较条件是否公平、结论成立所依赖的条件,以及证据能够支持到什么范围。

使用示例

  • “请解释这篇论文从什么研究问题出发,以及哪些设计选择支撑了结果。”
  • “请用具体数字演算论文的主要公式,并说明现有证据能支持哪些结论。”

注意事项

讲解会说明实际读取了哪些用户提供的材料。不会猜测缺失数值或作者动机;论文未报告的信息会明确标出。此技能不负责摘要或翻译论文,也不以同行评审者身份判断论文能否被接收或是否具有新颖性;它不检索文献、不核对引文,也不撰写稿件正文。

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。

文档检查

AI 根据 SKILL.md 的内容检查了需要注意的操作和使用前提。评分只依据实际运行的结果(实测对比)。

安全性发送数据、删除文件、执行命令等是否有说明
A良好资料要求说明阅读范围,不得猜测缺失数值或作者动机。
安装难易度所需工具和 API 密钥是否少且有说明
A良好前提是用户提供论文材料,未说明需要外部工具。

不计入评分。 由 AI(OpenAI Codex)于 2026年10月24日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(7)

  • SKILL.md6.0 KB
  • agents/openai.yaml395 B
  • manifest.yaml1.1 KB
  • README_EN.md3.7 KB
  • README.md3.2 KB
  • references/deep-reading-protocol.md3.4 KB
  • references/evidence-boundary.md3.3 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Nature Paper X-Ray

Read a paper the way a researcher explains it at a whiteboard to a colleague: what the authors saw, why they believed it, where they hesitated, what they gave up, and which numbers would change the conclusion.

The delivered artifact is a Markdown long-read. A section-by-section summary of the paper is a failure mode, not a fallback.

Default stance

  • The method section is a teaching order, not the order the work happened in. The introduction's story is arranged after the fact. The contribution list is written for reviewers.
  • The judgments that decided the paper — what the authors noticed, why they trusted it, what they abandoned — are mostly not written down. Recovering them is the job.
  • Every figure was chosen. Ask what the authors want a reader to believe from this one, then look for details inside it that do not support that reading.
  • A figure contains its own counter-evidence more often than the text does: a curve that crosses later, an error bar wider than the gap between compared methods, a log axis hiding a constant-factor difference, one seed standing in for a variance claim.
  • Name a mechanism or say the paper does not name one. Do not supply a motive the authors never stated.

Workflow

  1. Read the whole source first — body, appendix, footnotes, figure and table captions, and supplementary material if supplied. Appendices carry what the body declined to print: real hyperparameter search ranges, unflattering ablations, reviewer-response material. Record disagreements between the body and the appendix as findings.
  2. Reconstruct the research path. Name the concrete failure the work reacts to, and the bet the authors placed on their strongest evidence. The first figure usually carries that bet and deserves separate treatment.
  3. Separate load-bearing from decoration. For each distinctive design choice, judge whether removing it collapses the result or merely accompanies it. Cite the ablation, appendix table, or controlled comparison that settles it. When no such control exists, write that the paper does not run it.
  4. Ground the mathematics. Give every symbol its shape and meaning, state what a formula is for before stating the formula, and follow each key formula with a micro-example using concrete numbers carried to the end. See references/deep-reading-protocol.md.
  5. Check the evidence boundary. Agreement between body text, tables, and figures; whether baselines share data, steps, and tuning budget; whether hyperparameters were selected on the test set; preprocessing leakage; cost and the conditions under which the claim stops holding. See references/evidence-boundary.md.
  6. Preserve hedging strength. "May" is not "does", "suggests" is not "shows", "some settings" is not "all settings". Where the paper does not report something the conclusion needs, write that it is not reported. Never substitute a plausible number, an assumed default, or a value remembered from a similar paper.
  7. Deliver the long-read. Lead with what the paper is actually claiming, then the reconstructed path, the load-bearing judgment, the worked examples, and the evidence boundary. Close with what a reader should carry away and what remains open. State the scope read — body, appendix, supplementary, or a stated subset — and never imply fuller coverage than was achieved.

Paper-type adaptation

Reading emphasis shifts with the paper type. Load ../nature-shared/core/paper-type-taxonomy.md when the type is not obvious:

  • algorithmic — fair-comparison discipline dominates: are baselines tuned as hard as the proposal, and where does it fail?
  • methods — reproducibility dominates: would the protocol work in another lab, and what is left implicit?
  • hypothesis — the strength of the causal evidence dominates: does the evidence rule out the alternative explanation?
  • research — what was found, and how far the observation generalizes beyond the studied system.
  • review — how the field is organized, where sources disagree, and what remains open.

Boundaries

  • Reading sets no verdict. This skill reports what the paper does and how far its evidence reaches. It does not score, rank, or judge acceptance readiness, novelty, or significance — that is nature-reviewer.
  • It does not extract reusable writing patterns from a paper — that is nature-writing and its exemplar material.
  • It does not translate a paper or produce a bilingual reader — that is nature-reader.
  • It does not search the literature, verify citations, or cite sources on the paper's behalf — those are nature-academic-search, nature-citation, and nature-ref-verifier.
  • It does not draft manuscript prose. Use the reading as input to nature-writing when writing related work or positioning.

Output discipline

  • Write in the language the user is using. A Chinese request produces Chinese prose with English technical terms preserved exactly.
  • Quote the source only where an anchor requires it; do not reproduce the paper at length.
  • Formulas use $...$ inline and $$...$$ on their own line. Never place a formula in backticks or a code block.
  • A long paper is not a reason to degrade the output into a summary. If the reading is incomplete, say where it stopped.

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