peer-review — quality + safety report

---
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]
---

# 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

## 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:**
```bash
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 relevant results included, including negative results?
- **Reproducibility:** Are raw data or summary statistics provided?

**Common issues to identify:**
- Selective reporting of results
- Inappropriate statistical tests
- Missing error bars or measures of variability
- Over-fitting or circular analysis
- Batch effects or confounding variables
- Missing controls or validation experiments

#### Discussion
- **Interpretation:** Are conclusions supported by the data?
- **Limitations:** Are study limitations acknowledged and discussed?
- **Context:** Are findings placed appropriately within existing literature?
- **Speculation:** Is speculation clearly distinguished from data-supported conclusions?
- **Significance:** Are implications and importance clearly articulated?
- **Future directions:** Are next steps or unanswered questions discussed?

**Red flags:**
- Overstated conclusions
- Ignoring contradictory evidence
- Causal claims from correlational data
- Inadequate discussion of limitations
- Mechanistic claims without mechanistic evidence

#### References
- **Completeness:** Are key relevant papers cited?
- **Currency:** Are recent important studies included?
- **Balance:** Are contrary viewpoints appropriately cited?
- **Accuracy:** Are citations accurate and appropriate?
- **Self-citation:** Is there excessive or inappropriate self-citation?

### Stage 3: Methodological and Statistical Rigor

Evaluate the technical quality and rigor of the research with particular attention to common pitfalls.

**Statistical Assessment:**
- Are statistical assumptions met (normality, independence, homoscedasticity)?
- Are effect sizes reported alongside p-values?
- Is multiple testing correction applied appropriately?
- Are confidence intervals provided?
- Is sample size justified with power analysis?
- Are parametric vs. non-parametric tests chosen appropriately?
- Are missing data handled properly?
- Are exploratory vs. confirmatory analyses distinguished?

**Experimental Design:**
- Are controls appropriate and adequate?
- Is replication sufficient (biological and technical)?
- Are potential confounders identified and controlled?
- Is randomization properly implemented?
- Are blinding procedures adequate?
- Is the experimental design optimal for the research question?

**Computational/Bioinformatics:**
- Are computational methods clearly described and justified?
- Are software versions and parameters documented?
- Is code made available for reproducibility?
- Are algorithms and models validated appropriately?
- Are assumptions of computational methods met?
- Is batch correction applied appropriately?

### Stage 4: Reproducibility and Transparency

Assess whether the research meets modern standards for reproducibility and open science.

**Data Availability:**
- Are raw data deposited in appropriate repositories?
- Are accession numbers provided for public databases?
- Are data sharing restrictions justified (e.g., patient privacy)?
- Are data formats standard and accessible?

**Code and Materials:**
- Is analysis code made available (GitHub, Zenodo, etc.)?
- Are unique materials available or described sufficiently for recreation?
- Are protocols detailed in sufficient depth?

**Reporting Standards:**
- Does the manuscript follow discipline-specific reporting guidelines (CONSORT, PRISMA, ARRIVE, MIAME, MINSEQE, etc.)?
- See `references/reporting_standards.md` for common guidelines
- Are all elements of the appropriate checklist addressed?

### Stage 5: Figure and Data Presentation

Evaluate the quality, clarity, and integrity of data visualization.

**Quality Checks:**
- Are figures high resolution and clearly labeled?
- Are axes properly labeled with units?
- Are error bars defined (SD, SEM, CI)?
- Are statistical significance indicators explained?
- Are color schemes appropriate and accessible (colorblind-friendly)?
- Are scale bars included for images?
- Is data visualization appropriate for the data type?

**Integrity Checks:**
- Are there signs of image manipulation (duplications, splicing)?
- Are Western blots and gels appropriately presented?
- Are representative images truly representative?
- Are all conditions shown (no selective presentation)?

**Clarity:**
- Can figures stand alone with their legends?
- Is the message of each figure immediately clear?
- Are there redundant figures or panels?
- Would data be better presented as tables or figures?

### Stage 6: Ethical Considerations

Verify that the research meets ethical standards and guidelines.

**Human Subjects:**
- Is IRB/ethics approval documented?
- Is informed consent described?
- Are vulnerable populations appropriately protected?
- Is patient privacy adequately protected?
- Are potential conflicts of interest disclosed?

**Animal Research:**
- Is IACUC or equivalent approval documented?
- Are procedures humane and justified?
- Are the 3Rs (replacement, reduction, refinement) considered?
- Are euthanasia methods appropriate?

**Research Integrity:**
- Are there concerns about data fabrication or falsification?
- Is authorship appropriate and justified?
- Are competing interests disclosed?
- Is funding source disclosed?
- Are there concerns about plagiarism or duplicate publication?

### Stage 7: Writing Quality and Clarity

Assess the manuscript's clarity, organization, and accessibility.

**Structure and Organization:**
- Is the manuscript logically organized?
- Do sections flow coherently?
- Are transitions between ideas clear?
- Is the narrative compelling and clear?

**Writing Quality:**
- Is the language clear, precise, and concise?
- Are jargon and acronyms minimized and defined?
- Is grammar and spelling correct?
- Are sentences unnecessarily complex?
- Is the passive voice overused?

**Accessibility:**
- Can a non-specialist understand the main findings?
- Are technical terms explained?
- Is the significance clear to a broad audience?

## Structuring Peer Review Reports

Organize feedback in a hierarchical structure that prioritizes issues and provides actionable guidance.

### Summary Statement

Provide a concise overall assessment (1-2 paragraphs):
- Brief synopsis of the research
- Overall recommendation (accept, minor revisions, major revisions, reject)
- Key strengths (2-3 bullet points)
- Key weaknesses (2-3 bullet points)
- Bottom-line assessment of significance and soundness

### Major Comments

List critical issues that significantly impact the manuscript's validity, interpretability, or significance. Number these sequentially for easy reference.

**Major comments typically include:**
- Fundamental methodological flaws
- Inappropriate statistical analyses
- Unsupported or overstated conclusions
- Missing critical controls or experiments
- Serious re

… (truncated)