hezaohezao/poirot/academic-paper-review
Claude Code and agent skill by hezaohezao/poirot
- community
Checking that the SKILL.md file is still in the repository.
Structured peer-review of academic papers.
Install
npx vskill@latest install hezaohezao/poirot/academic-paper-reviewSKILL.md
Academic Paper Review
Overview
Produces structured, peer-review-quality analyses of academic papers. Follows review standards used by top-tier venues (NeurIPS, ICML, ACL, Nature, IEEE) to provide rigorous, constructive, and balanced assessments.
Covers summary, strengths, weaknesses, methodology assessment, contribution evaluation, literature positioning, and actionable recommendations — all grounded in evidence from the paper itself.
When to Use
- User provides a paper URL (arXiv, DOI, conference proceedings)
- User asks to "review", "analyze", "critique", "assess", or "summarize" a
research paper
- User wants to understand strengths and weaknesses of a study
- User requests a peer-review-style evaluation
Review Methodology
Phase 1: Paper Comprehension
Step 1.1: Identify Paper Metadata
Extract: Title, Authors, Venue/Status, Year, Domain, Paper Type (Empirical / Theoretical / Survey / Systems / Position).
Step 1.2: Deep Reading Pass
Read the paper systematically using browse_page:
- Abstract & Introduction — Identify claimed contributions and motivation
- Related Work — Note how authors position relative to prior art
- Methodology — Understand the proposed approach in detail
- Experiments / Results — Examine datasets, baselines, metrics, outcomes
- Discussion & Limitations — Note self-identified limitations
- Conclusion — Compare concluded claims against actual evidence
Step 1.3: Key Claims Extraction
List the paper's main claims explicitly:
Phase 2: Critical Analysis
Step 2.1: Literature Context Search
Use web_search to understand the research landscape:
Use browse_page on key related papers or surveys.
Step 2.2: Methodology Assessment
| Criterion | Questions to Ask | Rating | |
|---|---|---|---|
| Soundness | Is the approach technically correct? | 1-5 | |
| Novelty | What is genuinely new vs incremental? | 1-5 | |
| Reproducibility | Are details sufficient? Code/data available? | 1-5 | |
| Experimental Design | Are baselines fair? Ablations adequate? | 1-5 | |
| Statistical Rigor | Results significant? Error bars? Multiple runs? | 1-5 | |
| Scalability | Does it scale? Computational costs discussed? | 1-5 |
Step 2.3: Contribution Significance
| Level | Description | |
|---|---|---|
| Landmark | Fundamentally changes the field | |
| Significant | Strong contribution advancing state of the art | |
| Moderate | Useful contribution with some limitations | |
| Marginal | Minimal advance over existing work | |
| Below threshold | Does not meet publication standards |
Phase 3: Review Synthesis
Produce the final review using this template:
Review Principles
- Always suggest how to fix it — Don't just point out problems; propose solutions
- Give credit where due — Acknowledge genuine contributions even in flawed papers
- Be specific — Reference exact sections, equations, figures, tables
- Separate minor from major — Distinguish fatal flaws from fixable issues
Objectivity Standards
- ❌ "This paper is poorly written" (vague, unhelpful)
- ✅ "Section 3.2 introduces notation X without formal definition, making the
proof in Theorem 1 difficult to follow. Consider adding a notation table." (specific, actionable)
Adaptation by Paper Type
| Paper Type | Focus Areas | |
|---|---|---|
| Empirical | Experimental design, baselines, statistical significance, ablations | |
| Theoretical | Proof correctness, assumption reasonableness, tightness of bounds | |
| Survey | Comprehensiveness, taxonomy quality, coverage of recent work | |
| Systems | Architecture decisions, scalability evidence, real-world deployment | |
| Position | Argument coherence, evidence for claims, impact potential |
Common Pitfalls
- ❌ Reviewing the paper you wish was written instead of the paper submitted
- ❌ Demanding additional experiments that are unreasonable in scope
- ❌ Penalizing the paper for not solving a different problem
- ❌ Being overly influenced by writing quality versus technical contribution
- ❌ Providing only a summary without critical analysis
Quality Checklist
- [ ] Paper read completely (not just abstract and introduction)
- [ ] All major claims identified and evaluated against evidence
- [ ] At least 3 strengths and 3 weaknesses with specific references
- [ ] Methodology assessment table complete with ratings and justifications
- [ ] Literature search conducted to contextualize the contribution
- [ ] Recommendations are actionable and constructive
- [ ] Review tone is professional and respectful
Output
- Output the complete review in Markdown
- Save to
.poirot/outputs/review-{paper-topic}.mdviawrite_file - Present to user via
present_files
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingpoirot/backend/agents/skill/builtin_skills/research/academic-paper-review/SKILL.md
Popularity
- 7-day trend score
- 0
Not refreshed yet.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
Embed badge
[](https://verified-skill.com/skills/hezaohezao/poirot/academic-paper-review)