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Nature Reviewer

CommunityPopular
Yuan1z0825
nature-reviewer

Provide evidence-grounded mock peer review of scientific manuscripts or excerpts, covering significance, validity, and major/minor concerns. Use for 模拟审稿、投稿前自审、审稿人视角评估; not author rebuttal drafting.

Overview

PublisherYuan1z0825
Repositorynature-skills
Skill namenature-reviewer
Stars
42.8K
Forks
2.3K
Bundled files
17
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.

  • 17 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 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.
  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-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.
  • Use the 12-axis technical concern taxonomy only as an internal coverage checklist; it supplements but never replaces the five source-grounded axes.
  • Return exactly 3 mutually blind reviewer reports + 1 post-review synthesis unless the user explicitly asks for another structure.
  • Give every reviewer only the same immutable manuscript/source packet, the same journal criteria, and that reviewer's preassigned emphasis. Never provide another review, a shared concern ledger, a draft synthesis, or hints about what another reviewer noticed.
  • Run each reviewer in a genuinely separate context, subagent, process, or invocation. If the environment cannot isolate contexts, generate one reviewer report per invocation or explicitly state that mutual blindness cannot be guaranteed; never present shared-context drafting as independent peer review.
  • Define emphasis briefs before any report is generated. They are working lenses, not reviewer identities, specialties, institutions, or biographies.
  • Freeze each individual report before comparing them. Natural duplication or disagreement is valid evidence of independent review and must not be edited away to manufacture diversity.
  • Identify who would be interested in the results and why.
  • Identify technical failings that must be addressed before the authors' case is established.
  • Give every substantive concern a stable ID, a faithful claim_pointer, and a verifiable evidence_pointer; mark missing locations instead of inventing them.
  • Separate user-visible concerns into Major Concerns and Minor Comments. Mark a Major Concern Blocking Yes only when the current manuscript cannot establish its central case until that concern is resolved; Minor Comments are never blocking.
  • Do not impose a concern quota. If no grounded concern exists at a level, state that explicitly instead of inventing one.
  • Keep the critique intellectually sharp but professionally phrased; severity comes from impact on the manuscript's case, not from hostile wording.
  • Avoid em dashes, en dashes, and colons as routine prose punctuation throughout reviewer reports and synthesis. Prefer a new sentence, comma, semicolon, parentheses, or a short heading followed by a new line. Retain ordinary hyphens in standard compound terms and stable IDs such as R1-M1. Preserve punctuation in source-faithful titles, quotations, formulas, identifiers, URLs, times, and required machine-readable syntax when changing it would be inaccurate.
  • Distinguish clearly between what is supported, what is weak, and what is not assessable from the provided material.
  • When the manuscript has a clear technical domain, use claim-dependent domain gates as supporting checks, but keep the output inside the same 3-reviewer nature-reviewer structure.
  • 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. Build one immutable review packet containing only the supplied manuscript, verified source anchors, assessment boundary, and common journal criteria. Do not add analytical conclusions or suspected concerns to this packet.
  3. Define the reviewer count and emphasis briefs before launching any reviewer.
  4. Launch each reviewer in an isolated context. Pass only the immutable review packet, that reviewer's emphasis brief, the common report skeleton, and the same grounding rules.
  5. Inside each isolated review, independently assess readiness and the source-grounded axes, then build that reviewer's own concern ledger using references/technical-concern-taxonomy.md. If relevant, load only the applicable section of references/domain-specific-review-gates.md inside that same isolated context.
  6. Finalize and freeze every reviewer report. Do not show a completed or partial report to another reviewer, and do not redistribute concerns to control overlap.
  7. Only after all reports are frozen, compare them in a separate synthesis pass. Reconcile independently created concerns to shared synthesis keys, and label consensus only when at least two reports independently raise the same underlying concern.
  8. Generate Cross-review synthesis (post-review; not shown to reviewers) with consensus blocking concerns, other major concerns, the minor-revision checklist, and genuine differences in emphasis or judgment.
  9. Run QA for reviewer isolation, severity calibration, blocking calibration, evidence anchoring, groundedness, coverage, role boundaries, and non-invention. Overlap is measured only after freezing and must never trigger retroactive rewriting of individual reports.

Output format

Unless the user asks for another format, return:

text
Review setup
- **Input scope** [value]
- **Assessment boundary** [value]
- **Shared manuscript claim summary** [value]
- **Visible evidence base** [value]
- **Missing materials affecting confidence** [value]

Reviewer 1
- **Overall assessment** [text]
- **Who would be interested in the results, and why** [text]
- **Major strengths** [text]
- **Major Concerns** [items]
- **Minor Comments** [items]
- **Technical failings that need to be addressed before the case is established** [IDs or summary]
- **Assessment against Nature-style criteria** [text]
- **Recommendation posture** [text]

For each Major Concern
- **Concern ID** R1-M1
- **Severity** Major
- **Blocking** Yes / No
- **Axis** [value]
- **Claim pointer** [value]
- **Evidence pointer** [value]
- **Concern** [text]
- **Why it matters** [text]
- **Resolution test** [text]

For each Minor Comment
- **Concern ID** R1-m1
- **Severity** Minor
- **Axis** [value]
- **Affected element** [value]
- **Evidence pointer** [value]
- **Issue** [text]
- **Required correction** [text]

Reviewer 2
[Same structure]

Reviewer 3
[Same structure]

Cross-review synthesis (post-review; not shown to reviewers)
- **Consensus strengths** [text]
- **Consensus blocking concerns** [items]
- **Other consensus major concerns** [items]
- **Where emphasis differs across reviewers** [text]
- **Minor revision checklist** [items]
- **Broad-interest / significance readout** [text]
- **Most important issues to resolve before a strong Nature-style case is established** [items]

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

Red lines

  • Do not invent reviewer identities, specialty roles, or selection history.
  • Do not let one reviewer read, cite, anticipate, agree with, or respond to another review.
  • Do not build or distribute a shared concern ledger before individual reports are frozen.
  • Do not rewrite independent reports after comparison merely to reduce duplication or create artificial disagreement.
  • Do not call reports mutually blind when they were generated in a shared context without an explicit limitation notice.
  • Do not use dash punctuation or colons as habitual sentence connectors when clearer punctuation, headings, or sentence boundaries work.
  • 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.
  • Do not create Major or Minor concerns merely to fill a quota or make reviewer reports look balanced.
  • Do not downgrade a core evidence, validity, ethics, or integrity problem to Minor because it is easy to describe, and do not upgrade a local presentation issue merely to sound severe.

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/technical-concern-taxonomy.mdYou need the internal 12-axis coverage check, concern ledger, or claim/evidence-pointer rules
references/domain-specific-review-gates.mdThe manuscript has clear chemistry, engineering, materials, atmospheric, climate-ecology, hydrology, or remote-sensing evidence chains
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
../nature-shared/core/consistency-sweep.mdYou are checking the manuscript against itself: headline counts that do not reconcile with the Methods, one metric at two precisions, a superlative contradicted by the paper's own table, overlapping error bars presented as an advantage, or internal summaries that disagree
references/editorial criteria and processes.mdYou 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
  4. domain-specific supporting gates in references/domain-specific-review-gates.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?

Provide evidence-grounded mock peer review of scientific manuscripts or excerpts, covering significance, validity, and major/minor concerns. Use for 模拟审稿、投稿前自审、审稿人视角评估; not author rebuttal drafting.

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/Yuan1z0825/nature-skills/tree/main/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 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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