peer-review
Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines.
npx skills add majiayu000/claude-skill-registry --skill peer-review-k-dense-ai-claude-scientific-wr-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill provides a structured framework for evaluating scientific work. It guides the user through stages of peer review, including initial assessment and detailed section-by-section review across Abstract, Introduction, Methods, Results, Discussion, and References. It emphasizes checking reproducibility, ethics, statistics, and reporting standards, and it includes guidance for integrating figures and schematics to enhance communication. It also outlines methodologies for writing a review report with a summary, major and minor comments, and questions for authors. It additionally mentions how to use a related resource (venue-templates) to calibrate reviewer expectations and describes when to add visual schematics using a specific workflow and tool commands for generating diagrams. The skill specifies stages, key questions, and critical elements to verify, such as sample sizes, randomization, data handling, software versions, and data availability. It also addresses data presentation and figure integrity, ethical considerations, and writing quality.
How it works
- It instructs applying a systematic peer-review process across manuscript and grant reviews.
- Stage 1: Initial Assessment asks for a brief 2-3 sentence output capturing the manuscript’s essence and initial impression.
- Stage 2: Detailed Section-by-Section Review covers Abstract, Introduction, Methods, Results, Discussion, and References with explicit checklists for each subsection (Accuracy, Clarity, Completeness, Accessibility, Context, Rigor, Detail, Ethics, Statistics, Validation, etc.).
- Stage 3: Methodological and Statistical Rigor focuses on statistical assumptions, effect sizes, multiple testing, confidence intervals, power analysis, and design controls.
- Stage 4: Reproducibility and Transparency assesses data availability, code or materials sharing, and adherence to reporting guidelines.
- Stage 5: Figure and Data Presentation evaluates figure quality, labeling, legends, statistics reporting, accessibility, and integrity checks against manipulation.
- Stage 6: Ethical Considerations verifies human/animal ethics approvals, data privacy, conflicts of interest, and research integrity.
- Stage 7: Writing Quality and Clarity assesses structure, language, terminology, and accessibility for non-specialists.
- It also provides structuring for peer-review reports: Summary Statement, Major Comments, Minor Comments, Specific Line-by-Line Comments (Optional), and Questions for Authors, all with guidance on content and phrasing.
- The tone and best practices emphasize constructive, specific, objective, and thorough feedback.
When to use it
The skill is intended for use when conducting peer review of scientific manuscripts and evaluating grant proposals, with checks for methodological rigor, statistical appropriateness, reproducibility, ethics, and reporting standards. It also provides guidance to calibrate expectations to target venues via a related resource, and recommends adding schematics for complex concepts when appropriate.
What it can touch
- Tools and workflows described include generating schematics via a described command line:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png. - It references a related resource for reviewer expectations and mentions using a
scientific-schematicsskill to create diagrams, with outputs saved in afigures/directory and automatic review/refinement steps.
Caveats
- License: MIT
- The skill describes a comprehensive, structured approach but does not guarantee manuscript acceptance or publication outcomes. It relies on standard reporting guidelines (CONSORT, STROBE, PRISMA) and emphasizes adherence to disciplinary norms. It notes the use of venue-specific guidelines through a related resource but does not include those guidelines itself.
Summary: The skill codifies a rigorous, stage-based peer review workflow with explicit criteria across manuscript sections, emphasizing reproducibility, ethics, statistics, and clear feedback, suitable for manuscript and grant review across disciplines.
# 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 ad
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Peer Review Workflow
- Stage 1: Initial Assessment
- Stage 2: Detailed Section-by-Section Review
- Stage 3: Methodological and Statistical Rigor
- Stage 4: Reproducibility and Transparency
- Stage 5: Figure and Data Presentation
- Stage 6: Ethical Considerations
- Stage 7: Writing Quality and Clarity
- Structuring Peer Review Reports
- Summary Statement
- Major Comments
python scripts/generate_schematic.py "your diagram description" -o figures/output.png python skills/scientific-slides/scripts/pdf_to_images.py presentation.pdf review/slide --dpi 150
What does the peer-review skill do?
Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill peer-review-k-dense-ai-claude-scientific-wr-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.