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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.

Install / Use

npx skills add Yuan1z0825/nature-skills --skill nature-reviewer

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

95/100

Supported Platforms

Universal

Our assessment of nature-reviewer

nature-reviewer scores 95/100 on our quality scale, 114th of 1,186 Content & Media skills we index (top 10%).

Its SKILL.md is 11 KB long, well organised into 8 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

With 44,346 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
17/20
Description
15/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated about 2 months ago, so nature-reviewer is actively maintained.
  • Our last check on 2026-10-24 found the source still online.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-28. Automated pattern scan on 2026-09-28. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

nature-reviewer compared with similar skills

All 4 of these similar skills score higher than nature-reviewer; compare them before choosing.

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Frequently asked questions

How do I install nature-reviewer?
Run npx skills add Yuan1z0825/nature-skills --skill nature-reviewer. The install tabs above show the steps for each supported agent.
Which AI agents does nature-reviewer work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is nature-reviewer safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is nature-reviewer still maintained?
The repository was last updated about 2 months ago, so nature-reviewer is actively maintained.

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.
  • 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:

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

| File | Open when | |---|---| | references/source-basis.md | You need source provenance, local rule summaries, or source-vs-implementation boundaries | | references/reviewer-workflow.md | You need the invocation order, fact-base extraction flow, or synthesis rules | | references/review-axes.md | You need the evaluation axes or reviewer weighting logic | | references/technical-concern-taxonomy.md | You need the internal 12-axis coverage check, concern ledger, or claim/evidence-pointer rules | | references/domain-specific-review-gates.md | The manuscript has clear chemistry, engineering, materials, atmospheric, climate-ecology, hydrology, or remote-sensing evidence chains | | references/report-structure.md | You need the default output contract or section anatomy | | references/role-boundaries.md | You need constraints on reviewer differences and editor-versus-reviewer boundaries | | references/qa-checklist.md | You are finalizing an output and need groundedness / non-invention checks | | ../nature-shared/core/consistency-sweep.md | You 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.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
  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.

Related Skills

View on GitHub
GitHub Stars44.3k
CategoryContent
Updated1mo ago
Forks2.3k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions