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Nature Reviewer

Community
YuanyuanMa03
nature-reviewer

Simulate a Nature-style reviewer assessment from the referee perspective rather than an author rebuttal. Use when the user wants a pre-submission review, reviewer report, peer-review style critique, novelty/significance/technical soundness assessment, reviewer-style manuscript evaluation, 审稿人视角评估, 预审稿意见, or Nature reviewer report. Return 3 reviewer reports plus a cross-review synthesis, grounded only in the local Nature reviewer source basis. Also trigger on general pre-submission review requests during academic writing even without the word "Nature", such as getting a mock peer review for any journal, critiquing a draft as a reviewer would, assessing novelty/rigor before submission, and Chinese phrasings like 审稿人视角、模拟审稿、预审、帮我审一下论文、投稿前自审、审稿意见模拟、找论文问题.

Overview

PublisherYuanyuanMa03
Repositoryacademic-research-skills
Skill namenature-reviewer
Stars
76
Forks
7
Bundled files
7
LicenseMIT
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.

  • 7 bundled files

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

  • Open source

    Published by YuanyuanMa03 on GitHub. Read the source before you install it.

Installation

Install the Nature Reviewer 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/YuanyuanMa03/academic-research-skills/tree/main/plugins/nature-reviewer/skills/nature-reviewer
  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/YuanyuanMa03/academic-research-skills.git /tmp/academic-research-skills
mkdir -p .claude/skills
cp -r /tmp/academic-research-skills/plugins/nature-reviewer/skills/nature-reviewer .claude/skills/nature-reviewer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nature Reviewer 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 Reviewer 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 Reviewer 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 Reviewer Assessment Skill

Use this skill to simulate a Nature-style reviewer assessment package from the referee side.

This skill is for reviewer-style manuscript evaluation, not for drafting the authors' response. If the user wants rebuttal writing, route to nature-response.

Default stance

  • Ground the review only in the local source basis plus manuscript facts supplied by the user.
  • Evaluate the manuscript against source-grounded axes: originality, scientific importance, interdisciplinary readership, technical soundness, and readability for nonspecialists.
  • Return exactly 3 reviewer reports + 1 cross-review synthesis unless the user explicitly asks for another structure.
  • The three reviewers may differ only in emphasis; do not invent reviewer identities, specialties, institutions, or biographies.
  • Identify who would be interested in the results and why.
  • Identify technical failings that must be addressed before the authors' case is established.
  • Distinguish clearly between what is supported, what is weak, and what is not assessable from the provided material.
  • Do not claim the editor's final decision or certainty about fit to Nature.

Accepted inputs

The skill may receive:

  • full manuscript draft
  • abstract, summary paragraph, or cover-summary style text
  • introduction, results, discussion, or methods excerpts
  • figure legends, selected figures, or result notes
  • author notes in Chinese or English describing the claimed contribution
  • pre-submission positioning notes

If the provided material is partial, perform a bounded review and mark the assessment boundary explicitly.

Workflow

  1. Identify the input scope and whether the job is a reviewer-style assessment rather than rebuttal drafting.
  2. Extract a shared manuscript fact base: main claim, visible evidence, claimed significance, likely readership, and visible limitations.
  3. Check readiness and label missing evidence or missing sections instead of inventing them.
  4. Assess the manuscript using the source-grounded axes.
  5. Generate Reviewer 1, Reviewer 2, and Reviewer 3 using shared facts but different emphasis.
  6. Generate a Cross-review synthesis that captures consensus and weighting differences.
  7. Run QA for groundedness, coverage, role boundaries, and non-invention.

Output format

Unless the user asks for another format, return:

text
Review setup
- Input scope:
- Assessment boundary:
- Shared manuscript claim summary:
- Visible evidence base:
- Missing materials affecting confidence:

Reviewer 1
- Overall assessment:
- Who would be interested in the results, and why:
- Major strengths:
- Major concerns:
- Technical failings that need to be addressed before the case is established:
- Assessment against Nature-style criteria:
- Recommendation posture:

Reviewer 2
[Same structure]

Reviewer 3
[Same structure]

Cross-review synthesis
- Consensus strengths:
- Consensus technical risks:
- Where emphasis differs across reviewers:
- Broad-interest / significance readout:
- Most important issues to resolve before a strong Nature-style case is established:

Risk / unsupported claims
- [specific unsupported or not-assessable items]

Red lines

  • Do not invent reviewer identities, specialty roles, or selection history.
  • Do not invent experiments, validations, controls, citations, figure details, line numbers, or prior-work distinctions not present in the input.
  • Do not silently turn reviewer assessment into author rebuttal drafting.
  • Do not present the review as an editorial decision letter.
  • Do not state that the manuscript belongs in Nature as a settled fact.
  • Do not omit technical failings when the provided evidence does not establish the authors' case.

Related files

FileOpen when
references/source-basis.mdYou need source provenance, local rule summaries, or source-vs-implementation boundaries
references/reviewer-workflow.mdYou need the invocation order, fact-base extraction flow, or synthesis rules
references/review-axes.mdYou need the evaluation axes or reviewer weighting logic
references/report-structure.mdYou need the default output contract or section anatomy
references/role-boundaries.mdYou need constraints on reviewer differences and editor-versus-reviewer boundaries
references/qa-checklist.mdYou are finalizing an output and need groundedness / non-invention checks
[references/editorial criteria and processes.md](references/editorial criteria and processes.md)You need the primary local Nature source text

Source hierarchy

Use sources in this order:

  1. references/editorial criteria and processes.md
  2. manuscript facts supplied by the user
  3. conservative local implementation rules documented in references/source-basis.md

If a user asks for policy-level certainty beyond this local source, state the limit instead of improvising broader journal policy.

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 Reviewer AI skill do?

Simulate a Nature-style reviewer assessment from the referee perspective rather than an author rebuttal. Use when the user wants a pre-submission review, reviewer report, peer-review style critique, novelty/significance/technical soundness assessment, reviewer-style manuscript evaluation, 审稿人视角评估, 预审稿意见, or Nature reviewer report. Return 3 reviewer reports plus a cross-review synthesis, grounded only in the local Nature reviewer source basis. Also trigger on general pre-submission review requests during academic writing even without the word "Nature", such as getting a mock peer review for...

Why use Nature Reviewer on TypingMind?

Because you install it once and use it with any model. Nature Reviewer 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 Reviewer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/YuanyuanMa03/academic-research-skills/tree/main/plugins/nature-reviewer/skills/nature-reviewer. 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 Reviewer?

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 Reviewer?

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

Is the Nature Reviewer AI skill free?

Yes. It is published on GitHub by YuanyuanMa03 under the MIT 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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