Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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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".
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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:
Claim 1: [Specific claim about contribution or finding]
Evidence: [What evidence supports this claim in the paper]
Strength: [Strong / Moderate / Weak]
Claim 2: [...]
...
Use web search to understand the research landscape:
Search queries:
- "[paper topic] state of the art [current year]"
- "[key method name] comparison benchmark"
- "[authors] previous work [topic]"
- "[specific technique] limitations criticism"
- "survey [research area] recent advances"
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 Review: [Paper Title]
## Paper Metadata
- **Authors**: [Author list]
- **Venue**: [Publication venue or preprint server]
- **Year**: [Year]
- **Domain**: [Research field]
- **Paper Type**: [Empirical / Theoretical / Survey / Systems / Position]
## Executive Summary
[2-3 paragraph summary of the paper's core contribution, approach, and main findings.
State your overall assessment upfront: what the paper does well, where it falls short,
and whether the contribution is sufficient for the claimed venue/impact level.]
## Summary of Contributions
1. [First claimed contribution — one sentence]
2. [Second claimed contribution — one sentence]
3. [Additional contributions if any]
## Strengths
### S1: [Concise strength title]
[Detailed explanation with specific references to sections, figures, or tables in the paper.
Explain WHY this is a strength and its significance.]
### S2: [Concise strength title]
[...]
### S3: [Concise strength title]
[...]
## Weaknesses
### W1: [Concise weakness title]
[Detailed explanation with specific references. Explain the impact of this weakness on
the paper's claims. Suggest how it could be addressed.]
### W2: [Concise weakness title]
[...]
### W3: [Concise weakness title]
[...]
## Methodology Assessment
| Criterion | Rating (1-5) | Assessment |
|-----------|:---:|------------|
| Soundness | X | [Brief justification] |
| Novelty | X | [Brief justification] |
| Reproducibility | X | [Brief justification] |
| Experimental Design | X | [Brief justification] |
| Statistical Rigor | X | [Brief justification] |
| Scalability | X | [Brief justification] |
## Questions for the Authors
1. [Specific question that would clarify a concern or ambiguity]
2. [Question about methodology choices or alternative approaches]
3. [Question about generalizability or practical applicability]
## Minor Issues
- [Typos, formatting issues, unclear figures, notation inconsistencies]
- [Missing references that should be cited]
- [Suggestions for improved clarity]
## Literature Positioning
[How does this work relate to the current state of the art?
Are key related works cited? Are comparisons fair and comprehensive?
What important related work is missing?]
## Recommendations
**Overall Assessment**: [Accept / Weak Accept / Borderline / Weak Reject / Reject]
**Confidence**: [High / Medium / Low] — [Justification for confidence level]
**Contribution Level**: [Landmark / Significant / Moderate / Marginal / Below threshold]
### Actionable Suggestions for Improvement
1. [Specific, constructive suggestion]
2. [Specific, constructive suggestion]
3. [Specific, constructive suggestion]
| 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还没有评价。欢迎分享你的使用感受。
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
中文简介正在准备中。当前显示原文说明。