Review Response
Turns reviewer comments into structured rebuttals by parsing feedback, classifying it as major/minor/misunderstanding, then generating professional responses with the right tone. It enforces an evidence anchor rule so you can't claim "we added experiments" without naming the actual table or figure. Integrates with ml-paper-writing memory if installed to learn phrasing from successful papers. Built from ICLR spotlight analysis, so it knows to acknowledge strengths first, justify your experimental choices, and flag ethical considerations proactively. Use it when you need to respond to peer review and want to avoid the common mistakes that tank rebuttals. Includes reference docs for classification criteria and response templates.
- skills.sh installs
- 568
- GitHub stars
- 6K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add galaxy-dawn/claude-scholar --skill review-response --agent claude-codeInstalls into .claude/skills of the current project.
Files
Review Response
A systematic review response workflow that helps researchers efficiently and professionally reply to reviewer comments.
Core Features
- Review Analysis - Parse and classify reviewer comments (Major/Minor/Typo/Misunderstanding)
- Response Strategy - Develop response strategies for different comment types (Accept/Defend/Clarify/Experiment)
- Rebuttal Writing - Write structured, professional rebuttal documents
- 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
Sponsors
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- Repository
- github.com/galaxy-dawn/claude-scholar
- Categories
- Git & Pull Requests, Documentation, Code Review & Quality, Automation & Workflows
- First seen
- Jun 3, 2026
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