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Academic Paper Review

Community
HezaoHezao
academic-paper-review

Structured peer-review of academic papers.

Overview

PublisherHezaoHezao
Repositorypoirot
Skill nameacademic-paper-review
Stars
252
Forks
26
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 HezaoHezao 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.
    https://github.com/HezaoHezao/poirot/tree/master/poirot/backend/agents/skill/builtin_skills/research/academic-paper-review
  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/HezaoHezao/poirot.git /tmp/poirot
mkdir -p .claude/skills
cp -r /tmp/poirot/poirot/backend/agents/skill/builtin_skills/research/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

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:

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

List the paper's main claims explicitly:

Claim 1: [Specific claim]
Evidence: [What evidence supports this]
Strength: [Strong / Moderate / Weak]

Phase 2: Critical Analysis

Step 2.1: Literature Context Search

Use web_search to understand the research landscape:

"[paper topic] state of the art [current year]"
"[key method name] comparison benchmark"
"[specific technique] limitations criticism"

Use browse_page on key related papers or surveys.

Step 2.2: Methodology Assessment
CriterionQuestions to AskRating
SoundnessIs the approach technically correct?1-5
NoveltyWhat is genuinely new vs incremental?1-5
ReproducibilityAre details sufficient? Code/data available?1-5
Experimental DesignAre baselines fair? Ablations adequate?1-5
Statistical RigorResults significant? Error bars? Multiple runs?1-5
ScalabilityDoes it scale? Computational costs discussed?1-5
Step 2.3: Contribution Significance
LevelDescription
LandmarkFundamentally changes the field
SignificantStrong contribution advancing state of the art
ModerateUseful contribution with some limitations
MarginalMinimal advance over existing work
Below thresholdDoes not meet publication standards

Phase 3: Review Synthesis

Produce the final review using this 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 core contribution, approach, and main findings.
State overall assessment upfront.]

## Summary of Contributions
1. [First claimed contribution]
2. [Second claimed contribution]

## Strengths
### S1: [Concise strength title]
[Detailed explanation with specific references to sections, figures, tables.]

### S2: [Concise strength title]
[...]

## Weaknesses
### W1: [Concise weakness title]
[Detailed explanation. Explain impact. Suggest how to address.]

### W2: [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]
2. [Question about methodology choices]

## Literature Positioning
[How does this work relate to current state of the art? Key related works cited?]

## Recommendations
**Overall Assessment**: [Accept / Weak Accept / Borderline / Weak Reject / Reject]
**Confidence**: [High / Medium / Low]
**Contribution Level**: [Landmark / Significant / Moderate / Marginal / Below threshold]

### Actionable Suggestions for Improvement
1. [Specific, constructive suggestion]
2. [Specific, constructive suggestion]

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 TypeFocus Areas
EmpiricalExperimental design, baselines, statistical significance, ablations
TheoreticalProof correctness, assumption reasonableness, tightness of bounds
SurveyComprehensiveness, taxonomy quality, coverage of recent work
SystemsArchitecture decisions, scalability evidence, real-world deployment
PositionArgument 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}.md via write_file
  • Present to user via present_files

Frequently asked questions

What does the Academic Paper Review AI skill do?

Structured peer-review of academic papers.

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/HezaoHezao/poirot/tree/master/poirot/backend/agents/skill/builtin_skills/research/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 HezaoHezao 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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