Brycewang Stanford Awesome Agent Skills For Empirical Research Skills 04 K Dense AI Claude Scientific Writer Peer Review

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  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

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---
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

**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:**
```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 reproducibility concerns
- Major gaps in literature coverage
- Ethical concerns

**For each major comment:**
1. Clearly state the issue
2. Explain why it's problematic
3. Suggest specific solutions or additional experiments
4. Indicate if addressing it is essential for publication

### Minor Comments

List less critical issues that would improve clarity, completeness, or presentation. Number these sequentially.

**Minor comments typically include:**
- Unclear figure labels or legends
- Missing methodological details
- Typographical or grammatical errors
- Suggestions for improved data presentation
- Minor statistical reporting issues
- Supplementary analyses that would strengthen conclusions
- Requests for clarification

**For each minor comment:**
1. Identify the specific location (section, paragraph, figure)
2. State the issue clearly
3. Suggest how to address it

### Specific Line-by-Line Comments (Optional)

For manuscripts requiring detailed feedback, provide section-specific or line-by-line comments:
- Reference specific page/line numbers or sections
- Note factual errors, unclear statements, or missing citations
- Suggest specific edits for clarity

### Questions for Authors

List specific questions that need clarification:
- Methodological details that are unclear
- Seemingly contradictory results
- Missing information needed to evaluate the work
- Requests for additional data or analyses

## Tone and Approach

Maintain a constructive, professional, and collegial tone throughout the review.

**Best Practices:**
- **Be constructive:** Frame criticism as opportunities for improvement
- **Be specific:** Provide concrete examples and actionable suggestions
- **Be balanced:** Acknowledge strengths as well as weaknesses
- **Be respectful:** Remember that authors have invested significant effort
- **Be objective:** Focus on the science, not the scientists
- **Be thorough:** Don't overlook issues, but prioritize appropriately
- **Be clear:** Avoid ambiguous or vague criticism

**Avoid:**
- Personal attacks or dismissive language
- Sarcasm or condescension
- Vague criticism without specific examples
- Requesting unnecessary experiments beyond the scope
- Demanding adherence to personal preferences vs. best practices
- Revealing your identity if reviewing is double-blind

## Special Considerations by Manuscript Type

### Original Research Articles
- Emphasize rigor, reproducibility, and novelty
- Assess significance and impact
- Verify that conclusions are data-driven
- Check for complete methods and appropriate controls

### Reviews and Meta-Analyses
- Evaluate comprehensiveness of literature coverage
- Assess search strategy and inclusion/exclusion criteria
- Verify systematic approach and lack of bias
- Check for critical analysis vs. mere summarization
- For meta-analyses, evaluate statistical approach and heterogeneity

### Methods Papers
- Emphasize validation and comparison to existing methods
- Assess reproducibility and availability of protocols/code
- Evaluate improvements over existing approaches
- Check for sufficient detail for implementation

### Short Reports/Letters
- Adapt expectations for brevity
- Ensure core findings are still rigorous and significant
- Verify that format is appropriate for findings

### Preprints
- Recognize that these have not undergone formal peer review
- May be less polished than journal submissions
- Still apply rigorous standards for scientific validity
- Consider providing constructive feedback to help authors improve before journal submission

### Presentations and Slide Decks

**⚠️ CRITICAL: For presentations, NEVER read the PDF directly. ALWAYS convert to images first.**

When reviewing scientific presentations (PowerPoint, Beamer, slide decks):

#### Mandatory Image-Based Review Workflow

**NEVER attempt to read presentation PDFs directly** - this causes buffer overflow errors and doesn't show visual formatting issues.

**Required Process:**
1. Convert PDF to images using Python:
   ```bash
   python skills/scientific-slides/scripts/pdf_to_images.py presentation.pdf review/slide --dpi 150
   # Creates: review/slide-001.jpg, review/slide-002.jpg, etc.
   ```
2. Read and inspect EACH slide image file sequentially
3. Document issues with specific slide numbers
4. Provide feedback on visual formatting and content

**Print when starting review:**
```
[HH:MM:SS] PEER REVIEW: Presentation detected - converting to images for review
[HH:MM:SS] PDF REVIEW: NEVER reading PDF directly - using image-based inspection
```

#### Presentation-Specific Evaluation Criteria

**Visual Design and Readability:**
- [ ] Text is large enough (minimum 18pt, ideally 24pt+ for body text)
- [ ] High contrast between text and background (4.5:1 minimum, 7:1 preferred)
- [ ] Color scheme is professional and colorblind-accessible
- [ ] Consistent visual design across all slides
- [ ] White space is adequate (not cramped)
- [ ] Fonts are clear and professional

**Layout and Formatting (Check EVERY Slide Image):**
- [ ] No text overflow or truncation at slide edges
- [ ] No element overlaps (text over images, overlapping shapes)
- [ ] Titles are consistently positioned
- [ ] Content is properly aligned
- [ ] Bullets and text are not cut off
- [ ] Figures fit within slide boundaries
- [ ] Captions and labels are visible and readable

**Content Quality:**
- [ ] One main idea per slide (not overloaded)
- [ ] Minimal text (3-6 bullets per slide maximum)
- [ ] Bullet points are concise (5-7 words each)
- [ ] Figures are simplified and clear (not copy-pasted from papers)
- [ ] Data visualizations have large, readable labels
- [ ] Citations are present and properly formatted
- [ ] Results/data slides dominate the presentation (40-50% of content)

**Structure and Flow:**
- [ ] Clear narrative arc (introduction → methods → results → discussion)
- [ ] Logical progression between slides
- [ ] Slide count appropriate for talk duration (~1 slide per minute)
- [ ] Title slide includes authors, affiliation, date
- [ ] Introduction cites relevant background literature (3-5 papers)
- [ ] Discussion cites comparison papers (3-5 papers)
- [ ] Conclusions slide summarizes key findings
- [ ] Acknowledgments/funding slide at end

**Scientific Content:**
- [ ] Research question clearly stated
- [ ] Methods adequately summarized (not excessive detail)
- [ ] Results presented logically with clear visualizations
- [ ] Statistical significance indicated appropriately
- [ ] Conclusions supported by data shown
- [ ] Limitations acknowledged where appropriate
- [ ] Future directions or broader impact discussed

**Common Presentation Issues to Flag:**

**Critical Issues (Must Fix):**
- Text overflow making content unreadable
- Font sizes too small (<18pt)
- Element overlaps obscuring data
- Insufficient contrast (text hard to read)
- Figures too complex or illegible
- No citations (completely unsupported claims)
- Slide count drastically mismatched to duration

**Major Issues (Should Fix):**
- Inconsistent design across slides
- Too much text (walls of text, not bullets)
- Poorly simplified figures (axis labels too small)
- Cramped layout with insufficient white space
- Missing key structural elements (no conclusion slide)
- Poor color choices (not colorblind-safe)
- Minimal results content (<30% of slides)

**Minor Issues (Suggestions for Improvement):**
- Could use more visuals/diagrams
- Some slides slightly text-heavy
- Minor alignment inconsistencies
- Could benefit from more white space
- Additional citations would strengthen claims
- Color scheme could be more modern

#### Review Report Format for Presentations

**Summary Statement:**
- Overall impression of presentation quality
- Appropriateness for target audience and duration
- Key strengths (visual design, content, clarity)
- Key weaknesses (formatting issues, content gaps)
- Recommendation (ready to present, minor revisions, major revisions)

**Layout and Formatting Issues (By Slide Number):**
```
Slide 3: Text overflow - bullet point 4 extends beyond right margin
Slide 7: Element overlap - figure overlaps with caption text
Slide 12: Font size - axis labels too small to read from distance
Slide 18: Alignment - title not centered
```

**Content and Structure Feedback:**
- Adequacy of background context and citations
- Clarity of research question and objectives
- Quality of methods summary
- Effectiveness of results presentation
- Strength of conclusions and implications

**Design and Accessibility:**
- Overall visual appeal and professionalism
- Color contrast and readability
- Colorblind accessibility
- Consistency across slides

**Timing and Scope:**
- Whether slide count matches intended duration
- Appropriate level of detail for talk type
- Balance between sections

#### Example Image-Based Review Process

```
[14:30:00] PEER REVIEW: Starting review of presentation
[14:30:05] PEER REVIEW: Presentation detected - converting to images
[14:30:10] PDF REVIEW: Running pdf_to_images.py on presentation.pdf
[14:30:15] PDF REVIEW: Converted 25 slides to images in review/ directory
[14:30:20] PDF REVIEW: Inspecting slide 1/25 - title slide
[14:30:25] PDF REVIEW: Inspecting slide 2/25 - introduction
...
[14:35:40] PDF REVIEW: Inspecting slide 25/25 - acknowledgments
[14:35:45] PDF REVIEW: Completed image-based review
[14:35:50] PEER REVIEW: Found 8 layout issues, 3 content issues
[14:35:55] PEER REVIEW: Generating structured feedback by slide number
```

**Remember:** For presentations, the visual inspection via images is MANDATORY. Never attempt to read presentation PDFs as text - it will fail and miss all visual formatting issues.

## Resources

This skill includes reference materials to support comprehensive peer review:

### references/reporting_standards.md
Guidelines for major reporting standards across disciplines (CONSORT, PRISMA, ARRIVE, MIAME, STROBE, etc.) to evaluate completeness of methods and results reporting.

### references/common_issues.md
Catalog of frequent methodological and statistical issues encountered in peer review, with guidance on identifying and addressing them.

## Final Checklist

Before finalizing the review, verify:

- [ ] Summary statement clearly conveys overall assessment
- [ ] Major concerns are clearly identified and justified
- [ ] Suggested revisions are specific and actionable
- [ ] Minor issues are noted but properly categorized
- [ ] Statistical methods have been evaluated
- [ ] Reproducibility and data availability assessed
- [ ] Ethical considerations verified
- [ ] Figures and tables evaluated for quality and integrity
- [ ] Writing quality assessed
- [ ] Tone is constructive and professional throughout
- [ ] Review is thorough but proportionate to manuscript scope
- [ ] Recommendation is consistent with identified issues