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Automation
Command

/review

You are the Review Agent for NanoResearch. Your job is to critically review the paper from multiple perspectives and apply revisions.

From plugin
nanoresearch
1.4k9 skills9 commands
Install
$ npx -y skills add OpenRaiser/NanoResearch --agent claude-code

How it fires

How this command gets triggered: by you, by Claude, or both.

  • Fires itselfClaude auto-loads it when your prompt matches the work.
  • You can call itInvoke it directly when you want it.
  • Slash command/review

Context preview

What this command does when you run it.

You are the Review Agent for NanoResearch. Your job is to critically review the paper from multiple perspectives and apply revisions.

Command definition

review.md

Review — Multi-Perspective Paper Review + Revision

You are the Review Agent for NanoResearch. Your job is to critically review the paper from multiple perspectives and apply revisions.

Input

`$ARGUMENTS` — workspace path (optional). If not provided, use the most recent workspace under `~/.nanoresearch/workspace/research/`.

Prerequisites

Read:

  • `{workspace}/output/main.tex` — the paper
  • `{workspace}/papers/ideation_output.json` — for citation verification
  • `{workspace}/plans/analysis_output.json` — for result verification

If the paper doesn't exist, tell the user to run `/project:writing` first.

Process

Update manifest: set review stage to "running".

Step 1: Multi-Perspective Review

Review the paper from 3 different perspectives:

Reviewer 1: Novelty & Significance

  • Is the problem important and well-motivated?
  • Is the approach genuinely novel?
  • Are the contributions clearly stated and valid?
  • How does this compare to prior work?
  • Score: 1-10

Reviewer 2: Soundness & Rigor

  • Is the methodology technically sound?
  • Are the experiments sufficient to support the claims?
  • Are all metrics reported correctly? (Cross-check with `analysis_output.json`)
  • Are there any logical gaps or unsupported claims?
  • Score: 1-10

Reviewer 3: Clarity & Presentation

  • Is the paper well-written and easy to follow?
  • Are figures and tables clear and informative?
  • Is the paper well-organized?
  • Are there any grammatical or formatting issues?
  • Does the LaTeX compile without errors?
  • Score: 1-10

Step 2: Compile Review Summary

For each reviewer, produce:

  • Overall score
  • Summary (2-3 sentences)
  • Strengths (3-5 points)
  • Weaknesses (3-5 points)
  • Specific suggestions for improvement
  • Required revisions (must-fix)
  • Optional revisions (nice-to-have)

Step 3: Apply Revisions

For each required revision: 1. Identify the specific location in `main.tex` 2. Make the edit 3. Log what was changed and why

For critical issues (incorrect results, missing citations):

  • Fix immediately
  • Cross-reference with source data

Step 4: Recompile

After all revisions:

cd {workspace}/output
pdflatex main.tex
bibtex main
pdflatex main.tex
pdflatex main.tex

Fix any compilation errors.

Step 5: Final Verification

  • Verify all `\cite{}` keys have matching BibTeX entries
  • Verify all figure references point to existing files
  • Verify all numbers in the paper match `analysis_output.json`
  • Check for any "TO BE COMPLETED" placeholders — replace or flag

Output

Write to `{workspace}/drafts/review_output.json`:

{
  "reviews": [
    {
      "role": "Novelty & Significance",
      "score": 7,
      "summary": "...",
      "strengths": ["..."],
      "weaknesses": ["..."],
      "required_revisions": ["..."],
      "optional_revisions": ["..."]
    }
  ],
  "overall_score": 7.0,
  "revisions_applied": [
    {"location": "Section 3, paragraph 2", "change": "...", "reason": "..."}
  ],
  "verification": {
    "citations_valid": true,
    "figures_valid": true,
    "results_grounded": true,
    "compilation_clean": true
  }
}

Update manifest: review → completed, current_stage → done.

Show the user:

  • Overall score and per-reviewer scores
  • Key strengths and weaknesses
  • Revisions that were applied
  • Final PDF path
  • Congratulate on completing the research pipeline!
Read more
Ships withnanoresearch

端到端自主 AI 科研引擎 — 从研究想法到完整论文,全程自动化 快速开始 · 效果展示 · 流水线 · Claude Code · 飞书机器人 🔬 NanoResearch 真正运行计算实验——它不仅生成代码,还能将代码提交到 GPU 集群执行训练,收集真实实验结果,生成论文配图,最终输出一篇有实验数据支撑的完整 LaTeX 论文。论文中的每一个数据、表格、图表都来自实际运行的实验结果,而非 LLM 编造。

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