Skip to content
Agentmart
Submit a tool

nature-reviewer

Simulate a Nature-style reviewer assessment from the referee perspective rather than an author rebuttal.

Skill by ypan50918-ops

IndexedApache-2.0
Write a review

Chalk Labs

Need help deploying nature-reviewer?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/ypan50918-ops-nature-reviewer

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

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.

Use cases: documents, writing, research

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/nature-reviewer

What's inside

Skill name
nature-reviewer
Description
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.
License
Apache-2.0
Instructions
127 lines in SKILL.md · view on GitHub

Files (14)

  • README.md
  • README_EN.md
  • SKILL.md
  • manifest.yaml
  • domain-specific-review-gates.md · references/
  • editorial criteria and processes.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 · references/
  • traceable-review.md · tests/

SKILL.md (first lines)

# 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 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.
- Give every substantive concern a stable ID, a faithful `claim_pointer`, and a verifiable `evidence_pointer`; mark missing locations instead of inventing them.
- 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. 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. Build an internal concern ledger using `references/technical-concern-taxonomy.md`; record applicability, claim/evidence pointers, severity, and the resolution test for each supported concern.
6. If the manuscript clearly falls into a technical domain covered by `references/domain-specific-review-gates.md`, load only the relevant domain section and use it to sharpen the technical-soundness critique.
7. Generate `Reviewer 1`, `Reviewer 2`, and `Reviewer 3` using shared facts but different emphasis. Reuse ledger issue keys internally so repeated concerns can be measured and cross-referenced.
8. Generate a `Cross-review synthesis` that captures consensus and weighting differences. Label an issue as consensus only when at least two reviewer reports raise the same underlying concern.
9. Run QA for evidence anchoring, overlap, 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:

For each substantive concern:
- Concern ID: R1-M1
- Axis:
- Claim pointer:
- Evidence pointer:
- Concern and resolution test:

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

| File | Open when |
|---|---|
| [references/source-basis.md](references/source-basis.md) | You need source provenance, local rule summaries, or source-vs-implementation boundaries |
| [references/reviewer-workflow.md](references/reviewer-workflow.md) | You need the invocation order, fact-base extraction flow, or synthesis rules |
| [references/review-axes.md](references/review-axes.md) | You need the evaluation axes or reviewer weighting logic |
| [references/technical-concern-taxonomy.md](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](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](references/report-structure.md) | You need the default output contract or section anatomy |
| [references/role-boundaries.md](references/role-boundaries.md) | You need constraints on reviewer differences and editor-versus-reviewer boundaries |
| [references/qa-checklist.md](references/qa-checklist.md) | You 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:

From ypan50918-ops/Yuan1z0825-nature-skills, Apache-2.0.

More from ypan50918-ops/Yuan1z0825-nature-skills

Signals

Stars
0
Forks
1
Open issues
0
Last commit (this folder)
Jul 19, 2026 (3 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from ypan50918-ops

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get nature-reviewer running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying nature-reviewer?

CallGet help
Cite this page

nature-reviewer skill. Agent Mart. https://agentmart.dev/skills/ypan50918-ops-nature-reviewer (last updated 2026-10-24)