Review Response

Galaxy-Dawn/claude-scholar/skills/review-response

作者 Galaxy-Dawn903787345d6aec134086afa2b97715a78fbde519無授權條款5.7K 個星標收錄於 2026年10月9日更新於 2026年10月9日儲存庫2 週前更新

Systematic review response workflow from comment analysis to professional rebuttal writing. Use when the user asks to "write rebuttal", "respond to reviewers", "draft review response", or "analyze review comments". Improves paper acceptance rates.

AI 產生的概覽

引導研究者分析審稿意見,並撰寫結構化、專業的答辯回覆文件。

功能
此技能提供一套系統化的學術同儕審查回覆流程:解析並分類審稿意見,針對不同意見類型提出應對策略,撰寫結構化的答辯文件,並檢查語氣是否專業得體。它還要求每則回覆都附上證據錨點,註明論文位置、圖表或分析產物、引用或規劃中的實驗。最終產出答辯文件與策略建議,並參考隨附的參考指南。
適用情境
當使用者要求撰寫答辯、回覆審稿人、起草審稿回覆或分析審稿意見時使用,通常是在論文投稿收到同儕審查意見之後。
執行需求
無需指令碼,僅包含指示與參考文件。若存在來自另一個技能路徑的已安裝寫作記憶檔案,則會選擇性讀取;否則使用本機參考文件繼續。

Review Response

A systematic review response workflow that helps researchers efficiently and professionally reply to reviewer comments.

Core Features

  1. Review Analysis - Parse and classify reviewer comments (Major/Minor/Typo/Misunderstanding)
  2. Response Strategy - Develop response strategies for different comment types (Accept/Defend/Clarify/Experiment)
  3. Rebuttal Writing - Write structured, professional rebuttal documents
  4. Tone Management - Optimize tone to maintain professionalism, respect, and evidence-based arguments

Workflow

Receive reviewer comments -> Parse and classify -> Develop strategy -> Write responses -> Tone check -> Final rebuttal

When to Use

Use this skill when you need to:

  • "Help me write a rebuttal"
  • "How to respond to reviewer comments"
  • "Analyze these review comments"
  • "Develop a review response strategy"

Usage Steps

  1. Provide reviewer comments - Share the reviewer comments text or file with Claude
  2. Analysis and classification - Claude automatically parses and classifies the comments
  3. Strategy recommendations - Receive response strategy suggestions for each comment
  4. Write rebuttal - Generate a structured rebuttal document based on the strategy
  5. Optimize tone - Review and optimize the professionalism and politeness of responses

Core Principles

  • Professionalism - Maintain an academically professional tone and expression
  • Respectfulness - Respect the reviewers' opinions and time
  • Evidence-based - Support every response with sufficient reasoning and evidence
  • Completeness - Ensure all reviewer comments receive a response

Success Factors (Based on ICLR Spotlight Paper Analysis)

Key lessons extracted from successful rebuttal cases:

1. Acknowledge Strengths, Respond Positively to Criticism

  • Reviewers will first acknowledge the paper's strengths (novelty, impact, practical applicability)
  • Even spotlight papers receive constructive criticism
  • Strategy: Thank reviewers for acknowledged strengths first, then address criticism specifically

2. Provide Clarity and Intuitive Understanding

  • Even high-quality papers may have clarity issues
  • Need to provide intuition and detailed explanations for readers with different backgrounds
  • Strategy: Expand key sections, move technical details to appendix, add step-by-step walkthroughs

3. Thorough Justification of Experimental Setup

  • Need to justify experimental setup choices
  • Consider and discuss alternative metrics
  • Provide comprehensive experiments to support claims
  • Strategy: Add ablation studies, explain why specific experimental setups were chosen

4. Emphasis on Ethical Considerations

  • For research involving privacy, security, and other sensitive topics, ethical considerations are crucial
  • Reviewers pay special attention to ethical implications
  • Strategy: Proactively discuss ethical considerations, even if reviewers don't explicitly request it

5. Highlight Practical Application Value

  • Reviewers value practical applicability and scalability of methods
  • "Easily applicable" and "scalable" are important strengths
  • Strategy: Emphasize practical benefits and scalability in the rebuttal

Integration with active installed writing memory

When the rebuttal task involves:

  • tone calibration,
  • rebuttal phrasing,
  • clarification language,
  • structuring multi-point responses,
  • or learning from strong prior paper/review writing,

read the active installed writing memory before drafting:

  • ~/.claude/skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md on Claude Code installs
  • the equivalent installed skill-home path on Codex/OpenCode branches
  • otherwise skip this optional memory and continue with the local review-response references

Default read order for rebuttal work

  1. reviewer comments and paper context
  2. optional paper-miner-writing-memory.md if available
  3. references/response-strategies.md
  4. references/rebuttal-templates.md
  5. references/tone-guidelines.md

Read narrowly:

  • start with How this helps our writing,
  • then inspect Reusable phrasing,
  • then inspect Venue-specific signals if the rebuttal is venue-sensitive,
  • use Writing patterns mined only when the response needs stronger rhetorical structure.

Do not quote the memory mechanically. Use it to improve structure, clarity, restraint, and professionalism.

Evidence anchor rule

Every response row must include one of:

  • paper location,
  • result table / figure / analysis artifact,
  • citation / Evidence Record ID,
  • planned experiment with status,
  • unresolved if no evidence exists yet.

Do not claim "we added experiments" or "the results show" without naming the artifact. If an objection has multiple atomic points, split it and cover each point separately.

Reference Documents

For detailed guides, refer to:

  • references/review-classification.md - Review comment classification criteria
  • references/response-strategies.md - Response strategy library
  • references/rebuttal-templates.md - Rebuttal templates and examples
  • references/tone-guidelines.md - Tone and expression guidelines

Related Tools

  • Agent: rebuttal-writer - Dedicated agent for rebuttal writing and optimization
  • Command: /rebuttal <review_file> - Quick-start the rebuttal workflow

來源與署名

來源:Galaxy-Dawn/claude-scholar位於skills/review-response提交9037873

授權條款: 無授權條款

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