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Nature Paper Xray

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Yuan1z0825
nature-paper-xray

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.

Overview

PublisherYuan1z0825
Repositorynature-skills
Skill namenature-paper-xray
Stars
47.4K
Forks
2.5K
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Yuan1z0825 on GitHub. Read the source before you install it.

Installation

Install the Nature Paper Xray AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-paper-xray
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Yuan1z0825/nature-skills.git /tmp/nature-skills
mkdir -p .claude/skills
cp -r /tmp/nature-skills/skills/nature-paper-xray .claude/skills/nature-paper-xray
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nature Paper Xray in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nature Paper Xray on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nature Paper Xray is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Nature Paper Xray AI skill do?

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 p...

Why use Nature Paper Xray on TypingMind?

Because you install it once and use it with any model. Nature Paper Xray is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nature Paper Xray in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-paper-xray. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Nature Paper Xray?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nature Paper Xray?

As many as you like. As long as a model supports skills, you can use Nature Paper Xray with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nature Paper Xray AI skill free?

Yes. It is published on GitHub by Yuan1z0825 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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