/peer-review

name: peer-review
description: "Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines."
allowed-tools: [Read, Write, Edit, Bash]

<!-- ╔══════════════════════════════════════════════════════════════╗ ║ 本文件为开源 Skill 原始文档，收录仅供学习与研究参考 ║ ║ CoPaper.AI 收集整理 | https://copaper.ai ║ ╚══════════════════════════════════════════════════════════════╝

来源仓库: https://github.com/K-Dense-AI/claude-scientific-writer 项目名称: claude-scientific-writer 开源协议: MIT License 收录日期: 2026-04-02

声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者 提供 AI Agent Skills 的集中参考。如有侵权，请联系删除。 -->

Scientific Critical Evaluation and Peer Review

Overview

Peer review is a systematic process for evaluating scientific manuscripts. Assess methodology, statistics, design, reproducibility, ethics, and reporting standards. Apply this skill for manuscript and grant review across disciplines with constructive, rigorous evaluation.

When to Use This Skill

This skill should be used when:

• Conducting peer review of scientific manuscripts for journals
• Evaluating grant proposals and research applications
• Assessing methodology and experimental design rigor
• Reviewing statistical analyses and reporting standards
• Evaluating reproducibility and data availability
• Checking compliance with reporting guidelines (CONSORT, STROBE, PRISMA)
• Providing constructive feedback on scientific writing

**Related Resource:** The **venue-templates** skill provides `reviewer_expectations.md` with detailed guidance on what reviewers look for at different venues (Nature/Science, Cell Press, medical journals, ML conferences). Use this to calibrate your review standards to the target venue.

Visual Enhancement with Scientific Schematics

**When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.**

If your document does not already contain schematics or diagrams:

• Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams
• Simply describe your desired diagram in natural language
• Nano Banana Pro will automatically generate, review, and refine the schematic

**For new documents:** Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.

**How to generate schematics:**

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

**When to add schematics:**

• Peer review workflow diagrams
• Evaluation criteria decision trees
• Review process flowcharts
• Methodology assessment frameworks
• Quality assessment visualizations
• Reporting guidelines compliance diagrams
• Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

---

Peer Review Workflow

Conduct peer review systematically through the following stages, adapting depth and focus based on the manuscript type and discipline.

Stage 1: Initial Assessment

Begin with a high-level evaluation to determine the manuscript's scope, novelty, and overall quality.

**Key Questions:**

• What is the central research question or hypothesis?
• What are the main findings and conclusions?
• Is the work scientifically sound and significant?
• Is the work appropriate for the intended venue?
• Are there any immediate major flaws that would preclude publication?

**Output:** Brief summary (2-3 sentences) capturing the manuscript's essence and initial impression.

Stage 2: Detailed Section-by-Section Review

Conduct a thorough evaluation of each manuscript section, documenting specific concerns and strengths.

Abstract and Title

• **Accuracy:** Does the abstract accurately reflect the study's content and conclusions?
• **Clarity:** Is the title specific, accurate, and informative?
• **Completeness:** Are key findings and methods summarized appropriately?
• **Accessibility:** Is the abstract comprehensible to a broad scientific audience?

Introduction

• **Context:** Is the background information adequate and current?
• **Rationale:** Is the research question clearly motivated and justified?
• **Novelty:** Is the work's originality and significance clearly articulated?
• **Literature:** Are relevant prior studies appropriately cited?
• **Objectives:** Are research aims/hypotheses clearly stated?

Methods

• **Reproducibility:** Can another researcher replicate the study from the description provided?
• **Rigor:** Are the methods appropriate for addressing the research questions?
• **Detail:** Are protocols, reagents, equipment, and parameters sufficiently described?
• **Ethics:** Are ethical approvals, consent, and data handling properly documented?
• **Statistics:** Are statistical methods appropriate, clearly described, and justified?
• **Validation:** Are controls, replicates, and validation approaches adequate?

**Critical elements to verify:**

• Sample sizes and power calculations
• Randomization and blinding procedures
• Inclusion/exclusion criteria
• Data collection protocols
• Computational methods and software versions
• Statistical tests and correction for multiple comparisons

Results

• **Presentation:** Are results presented logically and clearly?
• **Figures/Tables:** Are visualizations appropriate, clear, and properly labeled?
• **Statistics:** Are statistical results properly reported (effect sizes, confidence intervals, p-values)?
• **Objectivity:** Are results presented without over-interpretation?
• **Completeness:** Are all re