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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.

DeepseekModel キュレーション済みスキル 品質 優秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=galaxy-dawn-claude-scholar-skills-review-response-skill-md&format=skill
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name review-response description 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. tags ["Research","Academic","Rebuttal","Paper Writing"] version 0.1.0 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 Provide reviewer comments - Share the reviewer comments text or file with Claude Analysis and classification - Claude automatically parses and classifies the comments Strategy recommendations - Receive response strategy suggestions for each comment Write rebuttal - Generate a structured rebuttal document based on the strategy 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 reviewer comments and paper context optional paper-miner-writing-memory.md if available references/response-strategies.md references/rebuttal-templates.md 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
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