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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
1.3k9 skills9 commands
$ npx -y skills add OpenRaiser/NanoResearch --agent claude-code

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
@openraiser
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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