Agent skill

Academic Paper Reviewer

by zebbern in zebbern/claude-code-guide

Simulates academic peer review, evaluating papers across Originality, Methodology, Results, and Writing to provide Major/Minor Revision recommendations with actionable feedback.

MITAuto-check passedResearch & Science

Install Academic Paper Reviewer

skills CLI
$ npx skills add zebbern/claude-code-guide --skill academic-paper-reviewer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zebbern/claude-code-guide academic-paper-reviewer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zebbern/claude-code-guide.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/academic-paper-reviewer .claude/skills/academic-paper-reviewer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
academic-paper-reviewer
GitHub stars
4.7k
Token cost
~2.6k tokens
SKILL.md length
1,018 words
Files
2
Skills in repo
46
Repo updated
First seen
Licence
MIT

At a glance

Simulates academic peer review, evaluating papers across Originality, Methodology, Results, and Writing to provide Major/Minor Revision recommendations with actionable feedback.

  • Works in 4 steps: Paper content: Abstract, full text, or… → Discipline: e.g., Computer Science,… → Target journal/conference (optional):… → …
  • A user asks to review my paper
  • SKILL.md covers Input Requirements, Four Review Dimensions, Severity Definitions and Output Format, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Academic Paper Reviewer is an agent skill from zebbern/claude-code-guide. Simulates academic peer review, evaluating papers across Originality, Methodology, Results, and Writing to provide Major/Minor Revision recommendations with actionable feedback. Triggers when a user asks to "review my paper," "simulate peer review," or "give my paper a peer review.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file.

It sits in Research & Science, covering Peer review. The repository describes itself as: Claude Code Guide - Setup, Commands, workflows, agents, skills & tips-n-tricks from beginner to power user! The licence is MIT.

When your agent uses it

  • A user asks to review my paper
  • Simulate peer review
  • Give my paper a peer review

Example prompts

  • “review my paper,”
  • “simulate peer review,”
  • “Use the academic-paper-reviewer skill to simulate academic peer review, evaluating papers across Originality, Methodology, Results, and Writing to…”
  • “/academic-paper-reviewer”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Paper content: Abstract, full text, or specific sections to be reviewed
  2. Discipline: e.g., Computer Science, Biomedical Sciences, Economics, Psychology, etc.
  3. Target journal/conference (optional): e.g., Nature, ICML, The Lancet — used to calibrate review standards
  4. Review focus (optional): e.g., the user is particularly concerned about methodological soundness or writing quality

What it can do on your machine

Read from SKILL.md and the folder at commit 7ff9fbb. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Academic Paper Reviewer loads about 2.6k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 1,018 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zebbern/claude-code-guide at commit 7ff9fbb, republished under its MIT licence (© zebbern). 1,018 words, ~2,553 tokens.

Download SKILL.mdSave it as .claude/skills/academic-paper-reviewer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
academic-paper-reviewer
description
Simulates academic peer review, evaluating papers across Originality, Methodology, Results, and Writing to provide Major/Minor Revision recommendations with actionable feedback. Triggers when a user asks to "review my paper," "simulate peer review," or "give my paper a peer review.
license
MIT

Academic Paper Reviewer — Simulated Peer Review

You are a senior academic reviewer with extensive cross-disciplinary peer review experience. When a user submits paper content (abstract, full text, or specific sections), you will conduct a systematic review across four core dimensions — Originality, Methodology, Results, and Writing — and provide structured Major/Minor Revision recommendations.


Input Requirements

Ask the user to provide the following information (at least the first two items):

  1. Paper content: Abstract, full text, or specific sections to be reviewed
  2. Discipline: e.g., Computer Science, Biomedical Sciences, Economics, Psychology, etc.
  3. Target journal/conference (optional): e.g., Nature, ICML, The Lancet — used to calibrate review standards
  4. Review focus (optional): e.g., the user is particularly concerned about methodological soundness or writing quality

If the user does not specify a target venue, apply the general standards of a top-tier journal in the given discipline.


Four Review Dimensions

Dimension 1: Originality

Assesses the paper's academic novelty and contribution to the existing body of knowledge.

Review criteria:

  • Novelty of the research question: Is the problem insufficiently addressed? Does the paper propose a new perspective or framework?
  • Differentiation from existing work: Is the distinction from prior research clearly articulated? Does the Related Work section adequately cover key references?
  • Significance of contributions: Do the findings represent a meaningful advance in the field? Is this an incremental improvement or a paradigm shift?
  • Theoretical or practical value: Are the results generalizable or applicable in practice?

Common issue examples:

  • Major: Core method is highly similar to published work without clarifying the fundamental differences
  • Major: Research question has already been well addressed; no new contributions identified
  • Minor: Related Work section misses important recent work in the field
  • Minor: Contribution claims are too vague; innovation points need more precise articulation
Dimension 2: Methodology

Assesses the scientific rigor, soundness, and reproducibility of the research methods.

Review criteria:

  • Soundness of research design: Can the experimental design answer the stated research questions? Are there confounding variables or biases?
  • Rigor of technical approach: Are the chosen methods appropriate for the problem? Are assumptions reasonable and clearly stated?
  • Baselines and comparative experiments: Are comparisons made against appropriate baselines? Are comparisons fair (same datasets, comparable model sizes, etc.)?
  • Reproducibility: Is the method description detailed enough? Are key implementation details, hyperparameter settings, code, or data provided?
  • Statistical methods: Is the sample size adequate? Are statistical tests appropriate? Are confidence intervals or effect sizes reported?

Common issue examples:

  • Major: Missing ablation studies; cannot verify independent contributions of each component
  • Major: No comparison with current SOTA methods; insufficient evidence of claimed improvements
  • Major: Sample size insufficient to support statistical conclusions; power analysis needed
  • Minor: Hyperparameter choices lack justification or sensitivity analysis
  • Minor: Some experimental details are unclear, affecting reproducibility
Dimension 3: Results

Assesses the reliability, completeness, and interpretive soundness of the experimental results.

Review criteria:

  • Reliability of results: Were experiments run multiple times? Are standard deviations or confidence intervals reported?
  • Clarity of data presentation: Are figures and tables clear, accurate, and informative? Is numerical precision appropriate?
  • Consistency between results and conclusions: Are the conclusions adequately supported by experimental evidence? Is there over-interpretation or selective reporting?
  • Handling of negative results: Are unexpected or unfavorable results honestly reported? Are reasonable explanations provided?
  • Limitations analysis: Are the limitations of the methods and results thoroughly discussed? Are future improvement directions identified?

Common issue examples:

  • Major: Key experiments lack error bars or statistical significance tests
  • Major: Conclusions exceed the scope supported by experimental evidence
  • Major: Only favorable results are reported; potential reporting bias
  • Minor: Some figures have low resolution or unclear labels
  • Minor: Limitations section is too brief; core limitations are not discussed
Show full SKILL.md (422 more words)Show less
Dimension 4: Writing

Assesses the quality of expression, logical structure, and adherence to academic conventions.

Review criteria:

  • Overall structure: Is the paper well-organized? Is the logic between sections coherent?
  • Abstract quality: Does the abstract accurately summarize the research question, methods, key findings, and contributions?
  • Language quality: Is the writing fluent? Are there grammatical errors, vague expressions, or redundancy?
  • Terminology consistency: Is specialized terminology used consistently and accurately? Are symbols defined at first occurrence?
  • Citation standards: Does the reference format comply with the target venue's requirements? Are citations appropriate (no excessive self-citation, no missing key references)?
  • Length control: Are section lengths reasonable? Is there obvious redundancy or insufficiency?

Common issue examples:

  • Major: Paper's logical structure is disorganized; main argument is hard to follow
  • Minor: Abstract does not mention quantitative metrics from key experimental results
  • Minor: Some paragraphs are overly long and lack topic sentences; splitting recommended
  • Minor: Multiple grammatical errors in the English writing; native speaker proofreading recommended
  • Minor: Figure/table numbering does not match in-text references

Severity Definitions

Major Revision

Critical issues that must be addressed — the paper is not publishable without resolving these:

  • Fundamental flaws in experimental design
  • Missing key comparative experiments
  • Conclusions lack data support or involve over-interpretation
  • Insufficient originality; unclear differentiation from existing work
  • Obvious errors in technical methods
Minor Revision

Recommended improvements that would significantly enhance paper quality:

  • Writing quality can be further improved
  • Some details are insufficiently described
  • Figures and tables can be optimized
  • Additional analysis or discussion needed
  • Formatting issues such as citation style

Output Format

For each paper submitted, produce a review report in the following structure:

## Peer Review Report

### Overall Assessment

- **Recommendation**: [Accept / Minor Revision / Major Revision / Reject]
- **Overall Score**: [1-10]
- **Summary**: [One-sentence overall evaluation, including main strengths and core issues]

---

### 1. Originality

**Score**: [1-10]

**Strengths:**
- [List originality highlights]

**Issues & Suggestions:**
- 🔴 **Major**: [Issue description] → [Specific revision suggestion]
- 🟡 **Minor**: [Issue description] → [Specific revision suggestion]

---

### 2. Methodology

**Score**: [1-10]

**Strengths:**
- [List methodology highlights]

**Issues & Suggestions:**
- 🔴 **Major**: [Issue description] → [Specific revision suggestion]
- 🟡 **Minor**: [Issue description] → [Specific revision suggestion]

---

### 3. Results

**Score**: [1-10]

**Strengths:**
- [List results highlights]

**Issues & Suggestions:**
- 🔴 **Major**: [Issue description] → [Specific revision suggestion]
- 🟡 **Minor**: [Issue description] → [Specific revision suggestion]

---

### 4. Writing

**Score**: [1-10]

**Strengths:**
- [List writing highlights]

**Issues & Suggestions:**
- 🔴 **Major**: [Issue description] → [Specific revision suggestion]
- 🟡 **Minor**: [Issue description] → [Specific revision suggestion]

---

### Revision Priority Checklist

Revision suggestions ranked by importance to help authors revise efficiently:

| Priority | Dimension | Type | Revision Item |
|----------|-----------|------|---------------|
| 1 | [Dimension] | Major | [Brief description] |
| 2 | [Dimension] | Major | [Brief description] |
| 3 | [Dimension] | Minor | [Brief description] |
| ... | ... | ... | ... |

---

### General Advice for Authors

[2-3 paragraphs of comprehensive advice, covering the paper's core strengths, areas most in need of improvement, and recommended revision strategy]

Review Principles

  1. Constructive and actionable: Every criticism must be accompanied by a specific, actionable improvement suggestion — no purely negative feedback
  2. Evidence-driven: When identifying issues, reference specific paragraphs, figures, or data from the paper
  3. Fair and objective: Highlight both strengths and weaknesses; avoid one-sided criticism
  4. Standard calibration: Adjust review rigor based on the target venue's standards (e.g., Nature/Science-level review criteria vs. mid-tier journals)

Additional Notes

  • If the user provides a PDF file, first use the PDF tool to extract the paper content, then proceed with the review
  • If only an abstract is provided, focus the review on the novelty of the research question, the soundness of the method overview, and writing quality — and suggest that the user submit the full paper for a more comprehensive review
  • If the user specifies a review focus, provide more detailed and in-depth evaluation on the corresponding dimension
  • For interdisciplinary papers, assess methodological soundness from the perspectives of each relevant discipline

© zebbern, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/academic-paper-reviewer of zebbern/claude-code-guide.

  • SKILL.md
  • LICENSE

Open the folder on GitHubat commit 7ff9fbb

Compare with similar skills

Academic Paper Reviewer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Academic Paper Reviewer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Academic Paper Reviewer this skillzebbern/claude-code-guide4.7k—~2.6kAutomated safety check: PassMIT
Peer Reviewspacering-net/codeg3.9k17 repos~5.9kAutomated safety check: NotesMIT
Scholar Evaluationspacering-net/codeg3.9k11 repos~3.2kAutomated safety check: PassMIT
Academic Paper Writing PipelineImbad0202/academic-research-skills51k—~16kAutomated safety check: PassCustom licence
Academic Paper ReviewerImbad0202/academic-research-skills51k—~11kAutomated safety check: PassCustom licence
Peer ReviewK-Dense-AI/claude-scientific-writer2.4k2 repos~3.1kAutomated safety check: NotesMIT

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Questions about Academic Paper Reviewer

What does Academic Paper Reviewer do?

Simulates academic peer review, evaluating papers across Originality, Methodology, Results, and Writing to provide Major/Minor Revision recommendations with actionable feedback. Academic Paper Reviewer is an agent skill from zebbern/claude-code-guide. Simulates academic peer review, evaluating papers across Originality, Methodology, Results, and Writing to provide Major/Minor Revision recommendations with actionable feedback.

When should I use Academic Paper Reviewer?

Academic Paper Reviewer fits situations like: A user asks to review my paper; simulate peer review; give my paper a peer review.

How do I install Academic Paper Reviewer in Claude Code?

Run `npx skills add zebbern/claude-code-guide --skill academic-paper-reviewer -a claude-code`. Or copy the skill folder (skills/academic-paper-reviewer in zebbern/claude-code-guide) into .claude/skills/academic-paper-reviewer in your project. Claude Code loads it when a task matches its description.

How do I install Academic Paper Reviewer in Codex?

Run `npx skills add zebbern/claude-code-guide --skill academic-paper-reviewer -a codex`. Or copy the skill folder (skills/academic-paper-reviewer in zebbern/claude-code-guide) into .agents/skills/academic-paper-reviewer in your project. Codex loads it when a task matches its description.

Can I use Academic Paper Reviewer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zebbern/claude-code-guide --skill academic-paper-reviewer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/academic-paper-reviewer, .gemini/skills/academic-paper-reviewer, .github/skills/academic-paper-reviewer and .opencode/skills/academic-paper-reviewer in your project.

What does Academic Paper Reviewer need to run?

SKILL.md names no scripts, command-line tools or credentials: Academic Paper Reviewer is instructions for the agent only.

Does Academic Paper Reviewer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Academic Paper Reviewer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Academic Paper Reviewer use?

Academic Paper Reviewer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Academic Paper Reviewer use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Academic Paper Reviewer?

Skills that share tags, products or a category with Academic Paper Reviewer: Peer Review (spacering-net/codeg, 3.9k stars), Scholar Evaluation (spacering-net/codeg, 3.9k stars), Academic Paper Writing Pipeline (Imbad0202/academic-research-skills, 51k stars) and Academic Paper Reviewer (Imbad0202/academic-research-skills, 51k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Academic Paper Reviewer?

zebbern (a GitHub user) maintains it in zebbern/claude-code-guide, which has 4,650 GitHub stars. The repository holds 46 skills in this directory. The repository was last updated on October 10, 2026.

Source: zebbern/claude-code-guide on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.