Agent skill

Nature-Style Mock Peer Review

by Yuan1z0825 in Yuan1z0825/nature-skills

Simulates a Nature-style referee assessment of a manuscript, producing three independent reviewer reports plus a synthesis, grounded in the supplied text.

Apache-2.0Auto-check passedResearch & Science

Install Nature-Style Mock Peer Review

skills CLI
$ npx skills add Yuan1z0825/nature-skills --skill nature-reviewer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Yuan1z0825/nature-skills nature-reviewer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Yuan1z0825/nature-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nature-reviewer .claude/skills/nature-reviewer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nature-reviewer
GitHub stars
47k
Token cost
~3.2k tokens
SKILL.md length
1,378 words
Files
21 (incl. references)
Skills in repo
23
Repo updated
First seen
Licence
Apache-2.0

At a glance

Simulates a Nature-style referee assessment of a manuscript, producing three independent reviewer reports plus a synthesis, grounded in the supplied text.

  • Works in 10 steps: Identify the input scope and whether the… → Build one immutable review packet… → Define the reviewer count and emphasis… → …
  • Self-reviewing a manuscript from the referee's side before submission
  • SKILL.md covers Default stance, Accepted inputs, Workflow and Output format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The skill evaluates a manuscript on five source-grounded axes: originality, scientific importance, interdisciplinary readership, technical soundness and readability for nonspecialists, with a 12-axis taxonomy of technical concerns used only as an internal checklist. By default it returns three mutually blind reviewer reports and one post-review synthesis. Each reviewer gets the same manuscript packet, the same journal criteria and a preassigned emphasis, and must run in a separate context, subagent or invocation, or the output must say that blindness cannot be guaranteed.

Reports are frozen before they are compared, and natural disagreement is kept as evidence of independence. A separate forensic consistency audit then checks arithmetic, metric bounds, aggregation levels, duplicate displays, provenance and reproducibility without feeding back into the reviewers. Every substantive concern gets a stable ID, a claim pointer and an evidence pointer. It does not draft author rebuttals. The folder has 23 files, including reference notes and tests.

When your agent uses it

  • Self-reviewing a manuscript from the referee's side before submission
  • Getting a structured list of major and minor concerns with evidence pointers
  • Stress-testing a paper's claims against independent reviewer perspectives

Example prompts

  • “Do a mock Nature peer review of the attached manuscript with three independent reviewers.”
  • “模拟审稿:从审稿人视角评估这篇论文的创新性和技术可靠性。”
  • “Review only the methods and results excerpt below and list major concerns with evidence pointers.”

Requirements

  • The manuscript or excerpt text supplied by you

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  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…
  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…
  5. Inside each isolated review, independently assess readiness and the source-grounded axes, then build that reviewer's own concern ledger…
  6. Finalize and freeze every reviewer report. Do not show a completed or partial report to another reviewer, and do not redistribute concerns…
  7. After all reports are frozen, run the forensic consistency audit in a separate editorial pass using…
  8. Compare the frozen reports in a separate synthesis pass. Reconcile independently created concerns to shared synthesis keys, and label…
  9. Generate Cross-review synthesis (post-review; not shown to reviewers) with consensus blocking concerns, other major concerns, forensic…
  10. Run QA for reviewer isolation, severity calibration, blocking calibration, evidence anchoring, groundedness, coverage, role boundaries…

What it can do on your machine

Read from SKILL.md and the folder at commit e605b35. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nature-Style Mock Peer Review loads about 3.2k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 54 tokens; SKILL.md has 1,378 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~19k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Yuan1z0825/nature-skills at commit e605b35, republished under its Apache-2.0 licence (© Yuan1z0825). 1,378 words, ~3,224 tokens.

Download SKILL.mdSave it as .claude/skills/nature-reviewer/SKILL.md (or your agent's skills folder). This skill also uses 20 other files; get the full folder from GitHub.
name
nature-reviewer
description
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.

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.
  • After all reports are frozen, run references/forensic-consistency-audit.md as a separate editorial pass. Audit arithmetic, metric bounds, aggregation levels, prose-table ordering, duplicate displays, dispersion anomalies, provenance and reproducibility. Never feed audit findings back into reviewer contexts or edit frozen reports after comparison.
  • 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. After all reports are frozen, run the forensic consistency audit in a separate editorial pass using references/forensic-consistency-audit.md. Classify findings as confirmed internal error, aggregation ambiguity, provenance gap, suspected duplication, unresolved input needed, not assessable, or passed. Do not feed audit findings back into reviewer contexts or edit frozen reports after comparison.
  8. Compare the frozen reports 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. Keep reviewer consensus and forensic audit findings separate.
  9. Generate Cross-review synthesis (post-review; not shown to reviewers) with consensus blocking concerns, other major concerns, forensic consistency findings, the minor-revision checklist, and genuine differences in emphasis or judgment.
  10. Run QA for reviewer isolation, severity calibration, blocking calibration, evidence anchoring, groundedness, coverage, role boundaries, non-invention, and forensic consistency. Overlap is measured only after freezing and must never trigger retroactive rewriting of individual reports.
Show full SKILL.md (496 more words)Show less

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]
- **Forensic consistency findings** [confirmed errors, aggregation ambiguities, provenance gaps, suspected duplication, unresolved author input]
- **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.
  • Do not label a numerical anomaly a confirmed error without arithmetic proof or source data.
  • Do not hide confirmed internal contradictions merely because no reviewer identified them.
  • Do not present an audit finding as reviewer consensus unless at least two frozen reports independently raised the same underlying concern.
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
references/forensic-consistency-audit.mdYou need the mandatory post-review audit of arithmetic, metric bounds, aggregation level, prose-table consistency, provenance, dispersion anomalies, or reproducibility gates
../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.

© Yuan1z0825, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 20 other files (references) in skills/nature-reviewer of Yuan1z0825/nature-skills.

  • SKILL.md
  • CHANGELOG.md
  • README.md
  • README_EN.md
  • agents/openai.yaml
  • manifest.yaml
  • references/domain-specific-review-gates.md
  • references/editorial criteria and processes.md
  • references/forensic-consistency-audit.md
  • references/qa-checklist.md
  • references/report-structure.md
  • references/review-axes.md
  • references/reviewer-workflow.md
  • references/role-boundaries.md
  • references/source-basis.md
  • references/technical-concern-taxonomy.md
  • tests/punctuation-style.md
  • tests/reviewer-independence.md
  • … and 3 more

Open the folder on GitHubat commit e605b35

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Questions about Nature-Style Mock Peer Review

What does Nature-Style Mock Peer Review do?

Simulates a Nature-style referee assessment of a manuscript, producing three independent reviewer reports plus a synthesis, grounded in the supplied text. The skill evaluates a manuscript on five source-grounded axes: originality, scientific importance, interdisciplinary readership, technical soundness and readability for nonspecialists, with a 12-axis taxonomy of technical concerns used only as an internal checklist. By default it returns three mutually blind reviewer reports and one post-review synthesis.

When should I use Nature-Style Mock Peer Review?

Nature-Style Mock Peer Review fits situations like: self-reviewing a manuscript from the referee's side before submission; getting a structured list of major and minor concerns with evidence pointers; stress-testing a paper's claims against independent reviewer perspectives.

How do I install Nature-Style Mock Peer Review in Claude Code?

Run `npx skills add Yuan1z0825/nature-skills --skill nature-reviewer -a claude-code`. Or copy the skill folder (skills/nature-reviewer in Yuan1z0825/nature-skills) into .claude/skills/nature-reviewer in your project. Claude Code loads it when a task matches its description.

How do I install Nature-Style Mock Peer Review in Codex?

Run `npx skills add Yuan1z0825/nature-skills --skill nature-reviewer -a codex`. Or copy the skill folder (skills/nature-reviewer in Yuan1z0825/nature-skills) into .agents/skills/nature-reviewer in your project. Codex loads it when a task matches its description.

Can I use Nature-Style Mock Peer Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Yuan1z0825/nature-skills --skill nature-reviewer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nature-reviewer, .gemini/skills/nature-reviewer, .github/skills/nature-reviewer and .opencode/skills/nature-reviewer in your project.

What does Nature-Style Mock Peer Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Nature-Style Mock Peer Review is instructions for the agent only. Our summary lists: The manuscript or excerpt text supplied by you.

Does Nature-Style Mock Peer Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nature-Style Mock Peer Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nature-Style Mock Peer Review use?

Nature-Style Mock Peer Review is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nature-Style Mock Peer Review use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16k tokens, read only when the agent opens those files.

What are the alternatives to Nature-Style Mock Peer Review?

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Who maintains Nature-Style Mock Peer Review?

Yuan1z0825 (a GitHub user) maintains it in Yuan1z0825/nature-skills, which has 47,222 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 11, 2026.

Source: Yuan1z0825/nature-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.