本文へ移動
cccskills
無料GitHub で公開

review-response

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.

インストール方法を見る

含まれるファイル(6)

  • SKILL.md5.6 KB
  • references/rebuttal-templates.md16.9 KB
  • references/response-strategies.md18.8 KB
  • references/review-classification.md3.4 KB
  • references/successful-cases.md10.5 KB
  • references/tone-guidelines.md9.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when creating or configuring Claude Code agents and their frontmatter.

日本語の概要は準備中です。原文の説明を表示しています。

Galaxy-Dawn/claude-scholar5,7722026年9月23日 更新

Use only when creating new registrable ML components that require Factory or Registry patterns.

日本語の概要は準備中です。原文の説明を表示しています。

Galaxy-Dawn/claude-scholar5,7722026年9月23日 更新

This skill should be used when the user asks to "debug this", "fix this error", "investigate this bug", "troubleshoot this issue", "find the problem", "something is broken", "this isn't working", "why is this failing", or reports errors/exceptions/bugs. Provides systematic debugging workflow and common error patterns.

日本語の概要は準備中です。原文の説明を表示しています。

Galaxy-Dawn/claude-scholar5,7722026年9月23日 更新

This skill provides reference guidance for citation verification in academic writing. Use when the user asks about "citation verification best practices", "how to verify references", "preventing fake citations", or needs guidance on citation accuracy. This skill supports ml-paper-writing by providing detailed verification principles and common error patterns.

日本語の概要は準備中です。原文の説明を表示しています。

Galaxy-Dawn/claude-scholar5,7722026年9月23日 更新

This skill should be used when the user asks to review a diff or pull request, write review comments, audit code quality, establish review standards, or improve how a team performs code review.

日本語の概要は準備中です。原文の説明を表示しています。

Galaxy-Dawn/claude-scholar5,7722026年9月23日 更新

This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.

日本語の概要は準備中です。原文の説明を表示しています。

Galaxy-Dawn/claude-scholar5,7722026年9月23日 更新

Galaxy-Dawn のスキルをすべて見る

このスキルの問題を報告する