nature-reviewer

nature-reviewer is a skill for Codex from Yuan1z0825/nature-skills. It costs 54 tokens per session (2,583 once invoked), scanned A, original, Apache-2.0.

A tool for simulating peer review of a research paper from the reviewer’s point of view. Peer review is the process in which experts assess a paper before publication.

In plain words
What is it for?
It produces three independent reviewer reports and a combined summary based on the supplied paper and sources.
Why use it?
It reveals possible problems with originality, importance, technical reliability, audience fit, and clarity before submission.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions subagents.

Good fit It produces three independent reviewer reports and a combined summary based on the supplied paper and sources.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yuan1z0825/nature-skills/nature-reviewer
About the project

Nature Skills is a collection of reusable skills that help AI agents handle academic writing and scientific visualization. Researchers and AI-assisted scholars use it to turn research tasks into repeatable workflows and usable outputs. The catalogue entries are skills from this collection.

Yuan1z0825/nature-skills · 50,803 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Yuan1z0825/nature-skills --skill nature-reviewer
Clone the repo
git clone --depth 1 https://github.com/Yuan1z0825/nature-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for nature-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-reviewer/github.svg)](https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-reviewer)
Your own site
<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-reviewer"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for nature-reviewer

Your own site · 80×15
<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-reviewer"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,583 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. ✓ AI security review Sonnet 5 · 6 Sept 2026 📄 Read the review Third-party audits
  • Socket pass 28 Sept 2026
  • Snyk pass 28 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.02583
Opus 5.5 $0.00022 $0.01033
Sonnet 5.5 $0.00011 $0.00517
Haiku 4.5 $0.00005 $0.00258

Measured 28d ago against content hash 8ca28cc8e8bd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

nature-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 28d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (tests/test_reviewer_instruction_contracts.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/nature-reviewer/SKILL.md · 174 lines

How it starts

The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 174 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 28d ago Changed · +7 lines 8ca28cc8e8bd
  2. 1mo ago Changed · -7 lines · -81 tokens per session 977d5b3e4528
  3. 1mo ago First seen · 174 lines · 135 tokens per session scan A 88aab215096a

Subscribe to this mod's changes

nature-reviewer is a skill published in the GitHub repository Yuan1z0825/nature-skills (50,803 stars, last pushed 2d ago), licensed Apache-2.0. It adds 54 tokens to every session and 2,583 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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