scientific-reviewer

Automated peer review systematically evaluates claims, methodology, citations, and evidence quality in research documents. Submit your manuscript or research article with instructions to assess claim validity, experimental design, statistical rigor, literature coverage, and reproducibility; the Skill generates structured feedback, executive summaries, and actionable recommendations for improvement.

Claims-evidence mapping identifies major assertions in a paper and evaluates whether supporting evidence is adequate, logically sound, and properly cited. The process traces each claim to its sources, assesses methodological rigor, checks citation adequacy, and flags unsupported or weakly supported conclusions.

Yes, reproducibility verification examines whether methods, data, and statistical approaches are described with sufficient detail and transparency for replication. The Skill assesses methodological completeness, data availability documentation, controls, statistical validity, and identifies gaps that would prevent independent reproduction of results.

Citation quality evaluation checks whether the paper cites relevant prior work, covers the literature adequately, and identifies missing references or gaps in context. The Skill identifies claims lacking proper attribution, assesses whether cited sources genuinely support stated conclusions, and flags areas needing additional literature review.

Structured review applies to research articles, reviews, methods papers, technical reports, brief communications, case studies, and preprints. The Skill handles diverse scientific document formats and generates outputs including executive summaries, detailed reviews, and actionable recommendations tailored to each document type.

The Skill provides detailed logic and methodological critique including statistical assessment and experimental design evaluation. While domain knowledge enhances interpretation, the structured output—flagging statistical issues, control problems, and methodological weaknesses—is designed for both specialists and researchers new to a subfield.