peer-review
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.
npx skills add LeonChaoX/qinyan-academic-skills --skill peer-review --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
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.
How it works
- The skill prescribes conducting a Stage-based peer review workflow (initial assessment, detailed section-by-section review, methodological/statistical rigor, reproducibility/transparency, figure/data presentation, ethical considerations, writing quality).
- It emphasizes evaluating: Abstract/Title, Introduction, Methods (reproducibility, rigor, detail, ethics, statistics, validation), Results (presentation, figures/tables, statistics, objectivity, completeness, reproducibility), Discussion (interpretation, limitations, context, future directions), References (completeness, currency, balance, accuracy, self-citation).
- It includes a separate methodological/statistical rigor section covering statistical assumptions, effect sizes, multiple testing, confidence intervals, power analysis, and design considerations (controls, replication, randomization, blinding).
- It mandates assessing data availability, code/materials accessibility, and adherence to reporting standards (CONSORT, STROBE, PRISMA) under reproducibility/ transparency.
- It provides a structured structure for the final review: Summary Statement, Major Comments, Minor Comments, and optional Specific Line-by-Line Comments, plus Questions for Authors.
- It recommends using visual schematics to enhance communication and offers a mechanism to generate publication-quality diagrams via a separate schematic skill.
When to use it
Use this skill 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
What it can touch
- The skill outlines a comprehensive evaluation workflow and specific checklists for each manuscript section, as well as requirements for data/code availability, and adherence to reporting standards. It indicates evaluating whether data availability, code access, and methodological details are sufficient for reproducibility.
Caveats
- License: MIT license
- Declared tool: claude-code
- Allowed tools: Read Write Edit Bash
- Focus is on critique and structured feedback; it does not guarantee publication outcomes.
# 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 la
- 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?
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill peer-review --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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.