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.
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Structured Peer Review Tool — Scientific Papers & Grant Review System
Skills Overview
Peer review is a professional, structured peer-review skill that provides checklist-based tools for evaluating scientific manuscripts and grant applications. It covers methodological assessment, statistical validity analysis, and compliance checks with reporting standards. It is suitable for journal peer review, research funding evaluations, and academic quality assessments.
Applicable Scenarios
Core Features
Frequently Asked Questions
What types of research is this peer-review skill suitable for?
The peer-review skill applies to a wide range of research areas, including biomedical research (CONSORT, STROBE standards), systematic reviews and meta-analyses (PRISMA standards), animal studies (ARRIVE standards), social science research, and engineering technology research. The skill provides dedicated review criteria for different study types (original research, reviews, methodological papers, and brief reports).
How do you ensure the review is constructive and professional?
The skill emphasizes constructive, professional, and collaborative review principles. Reviewers are required to provide specific—not vague—criticism, balance strengths and weaknesses, avoid personal attacks or condescending language. Reviews should be based on scientific content rather than the scientist personally, offer actionable, concrete recommendations, and provide a clear overall assessment and suggestions at the end.
How does this skill help identify statistical issues in research?
The skill provides a comprehensive statistical evaluation checklist, covering common issues such as statistical hypothesis testing, effect size reporting, multiple-comparison correction, confidence intervals, sample size rationale, appropriate choice of parametric versus nonparametric tests, and handling missing data. The review workflow also includes identifying shortcomings in data visualization, selective reporting, overfitting, batch effects, and other data analysis problems such as confounding variables.