review2rebuttal

What problem does it solve?

Manually drafting academic paper rebuttals is time-consuming, prone to unsupported claims, and often leads to missed reviewer concerns and disorganized revision tracking. This Skill automates the end-to-end rebuttal workflow to keep responses grounded, traceable, and aligned with paper evidence.

Core Features & Use Cases

• Atomic Issue Breakdown: Automatically splits raw reviewer comments into structured, trackable issues to ensure no concern is overlooked.
• Evidence-Grounded Drafting: Maps each issue to paper facts and optional code repository evidence to keep rebuttals accurate and defensible.
• Dual Workflow Modes: Supports a lightweight quick mode for fast first-round rebuttal drafting, and a rigorous full mode for end-to-end rebuttal work including experiment planning, follow-up handling, and final remarks.
• Use Case: Researchers responding to peer reviews for academic conferences (e.g., ICLR, ICML, NeurIPS) who need reproducible, auditable rebuttal workspaces instead of disorganized one-off drafts.

Quick Start

Share your paper PDF, reviewer comments, and optional code repository with the AI, and ask it to use the review2rebuttal skill to generate a complete, evidence-backed rebuttal workspace for your paper submission.