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Academic Paper Review

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bytedance
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".

Overview

Publisherbytedance
Repositorydeer-flow
Skill nameacademic-paper-review
Stars
82.6K
Forks
11.4K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bytedance on GitHub. Read the source before you install it.

Installation

Install the Academic Paper Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bytedance/deer-flow.git /tmp/deer-flow
mkdir -p .claude/skills
cp -r /tmp/deer-flow/skills/public/academic-paper-review .claude/skills/academic-paper-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Paper Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Academic Paper Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Academic Paper Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.)

When to Use This Skill

Always load this skill when:

  • User provides a paper URL (arXiv, DOI, conference proceedings, journal link)
  • User uploads a PDF of a research paper or preprint
  • User asks to "review", "analyze", "critique", "assess", or "summarize" a research paper
  • User wants to understand the strengths and weaknesses of a study
  • User requests a peer-review-style evaluation of academic work
  • User asks for help preparing a review for a conference or journal submission

Review Methodology

Phase 1: Paper Comprehension

Thoroughly read and understand the paper before forming any judgments.

Step 1.1: Identify Paper Metadata

Extract and record:

FieldDescription
TitleFull paper title
AuthorsAuthor list and affiliations
Venue / StatusPublication venue, preprint server, or submission status
YearPublication or submission year
DomainResearch field and subfield
Paper TypeEmpirical, theoretical, survey, position paper, systems paper, etc.
Step 1.2: Deep Reading Pass

Read the paper systematically:

  1. Abstract & Introduction — Identify the claimed contributions and motivation
  2. Related Work — Note how authors position their work relative to prior art
  3. Methodology — Understand the proposed approach, model, or framework in detail
  4. Experiments / Results — Examine datasets, baselines, metrics, and reported outcomes
  5. Discussion & Limitations — Note any self-identified limitations
  6. Conclusion — Compare concluded claims against actual evidence presented
Step 1.3: Key Claims Extraction

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: [...]
...

Phase 2: Critical Analysis

Step 2.1: Literature Context Search

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.

Step 2.2: Methodology Assessment

Evaluate the methodology using the following framework:

CriterionQuestions to AskRating
SoundnessIs the approach technically correct? Are there logical flaws?1-5
NoveltyWhat is genuinely new vs. incremental improvement?1-5
ReproducibilityAre details sufficient to reproduce? Code/data available?1-5
Experimental DesignAre baselines fair? Are ablations adequate? Are datasets appropriate?1-5
Statistical RigorAre results statistically significant? Error bars reported? Multiple runs?1-5
ScalabilityDoes the approach scale? Are computational costs discussed?1-5
Step 2.3: Contribution Significance Assessment

Evaluate the significance level:

LevelDescriptionCriteria
LandmarkFundamentally changes the fieldNew paradigm, widely applicable breakthrough
SignificantStrong contribution advancing the state of the artClear improvement with solid evidence
ModerateUseful contribution with some limitationsIncremental but valid improvement
MarginalMinimal advance over existing workSmall gains, narrow applicability
Below thresholdDoes not meet publication standardsFundamental flaws, insufficient evidence
Step 2.4: Strengths and Weaknesses Analysis

For each strength or weakness, provide:

  • What: Specific observation
  • Where: Section/figure/table reference
  • Why it matters: Impact on the paper's claims or utility

Phase 3: Review Synthesis

Step 3.1: Assemble the Structured Review

Produce the final review using the template below.

Review Output Template

markdown
# 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]

Review Principles

Constructive Criticism

  • 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, and 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 after the problem formulation." (specific, actionable)

Ethical Review Practices

  • Do NOT dismiss work based on author reputation or affiliation
  • Evaluate the work on its own merits
  • Flag potential ethical concerns (bias in datasets, dual-use implications) constructively
  • Maintain confidentiality of unpublished work

Adaptation by Paper Type

Paper TypeFocus Areas
EmpiricalExperimental design, baselines, statistical significance, ablations, reproducibility
TheoreticalProof correctness, assumption reasonableness, tightness of bounds, connection to practice
SurveyComprehensiveness, taxonomy quality, coverage of recent work, synthesis insights
SystemsArchitecture decisions, scalability evidence, real-world deployment, engineering contributions
PositionArgument coherence, evidence for claims, impact potential, fairness of characterizations

Common Pitfalls to Avoid

  • ❌ Reviewing the paper you wish was written instead of the paper that was 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
  • ❌ Treating absence of comparison to your own work as a weakness
  • ❌ Providing only a summary without critical analysis

Quality Checklist

Before finalizing the review, verify:

  • Paper was read completely (not just abstract and introduction)
  • All major claims are identified and evaluated against evidence
  • At least 3 strengths and 3 weaknesses are provided with specific references
  • The methodology assessment table is complete with ratings and justifications
  • Questions for authors target genuine ambiguities, not rhetorical critiques
  • Literature search was conducted to contextualize the contribution
  • Recommendations are actionable and constructive
  • The overall assessment is consistent with the identified strengths and weaknesses
  • The review tone is professional and respectful
  • Minor issues are separated from major concerns

Output Format

  • Output the complete review in Markdown format
  • Save the review to /mnt/user-data/outputs/review-{paper-topic}.md when working in sandbox
  • Present the review to the user using the present_files tool

Notes

  • This skill complements the deep-research skill — load both when the user wants the paper reviewed in the context of the broader field
  • For papers behind paywalls, work with whatever content is accessible (abstract, publicly available versions, preprint mirrors)
  • Adapt the review depth to the user's needs: a brief assessment for quick triage versus a full review for submission preparation
  • When reviewing multiple papers comparatively, maintain consistent criteria across all reviews
  • Always disclose limitations of your review (e.g., "I could not verify the proofs in Appendix B in detail")

Frequently asked questions

What does the Academic Paper Review AI skill do?

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".

Why use Academic Paper Review on TypingMind?

Because you install it once and use it with any model. Academic Paper Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Academic Paper Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bytedance/deer-flow/tree/main/skills/public/academic-paper-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Academic Paper Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Academic Paper Review?

As many as you like. As long as a model supports skills, you can use Academic Paper Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Academic Paper Review AI skill free?

Yes. It is published on GitHub by bytedance under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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