npx skills add ...
npx skills add bytedance/deer-flow --skill academic-paper-review
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
npx skills add bytedance/deer-flow --skill academic-paper-review
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.
Always load this skill when:
Thoroughly read and understand the paper before forming any judgments.
Extract and record:
| Field | Description |
|---|---|
| Title | Full paper title |
| Authors | Author list and affiliations |
| Venue / Status | Publication venue, preprint server, or submission status |
| Year | Publication or submission year |
| Domain | Research field and subfield |
| Paper Type | Empirical, theoretical, survey, position paper, systems paper, etc. |
Read the paper systematically:
List the paper's main claims explicitly:
Use web search to understand the research landscape:
Use web_fetch on key related papers or surveys to understand where this work fits.
Evaluate the methodology using the following framework:
| Criterion | Questions to Ask | Rating |
|---|---|---|
| Soundness | Is the approach technically correct? Are there logical flaws? | 1-5 |
| Novelty | What is genuinely new vs. incremental improvement? | 1-5 |
| Reproducibility | Are details sufficient to reproduce? Code/data available? | 1-5 |
| Experimental Design | Are baselines fair? Are ablations adequate? Are datasets appropriate? | 1-5 |
| Statistical Rigor | Are results statistically significant? Error bars reported? Multiple runs? | 1-5 |
| Scalability | Does the approach scale? Are computational costs discussed? | 1-5 |
Evaluate the significance level:
| Level | Description | Criteria |
|---|---|---|
| Landmark | Fundamentally changes the field | New paradigm, widely applicable breakthrough |
| Significant | Strong contribution advancing the state of the art | Clear improvement with solid evidence |
| Moderate | Useful contribution with some limitations | Incremental but valid improvement |
| Marginal | Minimal advance over existing work | Small gains, narrow applicability |
| Below threshold | Does not meet publication standards | Fundamental flaws, insufficient evidence |
For each strength or weakness, provide:
Produce the final review using the template below.
| Paper Type | Focus Areas |
|---|---|
| Empirical | Experimental design, baselines, statistical significance, ablations, reproducibility |
| Theoretical | Proof correctness, assumption reasonableness, tightness of bounds, connection to practice |
| Survey | Comprehensiveness, taxonomy quality, coverage of recent work, synthesis insights |
| Systems | Architecture decisions, scalability evidence, real-world deployment, engineering contributions |
| Position | Argument coherence, evidence for claims, impact potential, fairness of characterizations |
Before finalizing the review, verify:
/mnt/user-data/outputs/review-{paper-topic}.md when working in sandboxpresent_files tooldeep-research skill — load both when the user wants the paper reviewed in the context of the broader field