Academic Paper Review

This produces peer review quality analyses of academic papers with the kind of structured feedback you'd see at top venues like NeurIPS or Nature. It walks through the paper systematically, extracts claims and evidence, searches the literature to position the work, then generates a full review with rated methodology assessments, numbered strengths and weaknesses with specific section references, and questions for authors. The output follows standard review templates with metadata, executive summary, contribution list, and methodology scores across six dimensions like reproducibility and statistical rigor. Works with PDFs, arXiv links, or DOIs across any scientific domain. Honest take: the structure is more valuable than the critique itself, since it forces a thorough reading and prevents handwaving about vague concerns. Good for preparing your own reviews or understanding what referees will scrutinize in your submissions.

skills.sh installs: 2.8K
GitHub stars: 85.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add bytedance/deer-flow --skill academic-paper-review --agent claude-code

Installs into .claude/skills of the current project.

Files

Academic Paper Review Skill

Overview

This skill produces structured, peer-review-quality analyses of academic papers and research publications. It follows established academic review standards used by top-tier venues (NeurIPS, ICML, ACL, Nature, IEEE) to provide rigorous, constructive, and balanced assessments.

The review covers summary, strengths, weaknesses, methodology assessment, contribution evaluation, literature positioning, and actionable recommendations — all grounded in evidence from the paper itself.

Core Capabilities

Parse and comprehend academic papers from uploaded PDFs or fetched URLs
Generate structured reviews following top-venue review templates
Assess methodology rigor (experimental design, statistical validity, reproducibility)
Evaluate novelty and significance of contributions
Position the work within the broader research landscape via targeted literature search
Identify limitations, gaps, and potential improvements
Produce both detailed review and concise executive summary formats
Support papers in any scientific domain (CS, biology, physics, social sciences, etc.)