xiongqi123123

xiongqi123123 / awesome-rebuttal

Public

a skill for rebuttal

15
2
89% credibility
Found May 29, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

Awesome Rebuttal is a skill package that helps academic researchers respond to peer review feedback on their papers. It works with AI coding assistants to organize reviewer comments, plan additional experiments, and draft professional author responses. The tool creates a structured workspace where it maintains notes, drafts, and memories about the rebuttal process, while enforcing safety rules to prevent fabricated claims or rule violations. It supports various academic conference formats including one-page rebuttals and OpenReview-style responses.

How It Works

1
📚 Discover the project

A researcher struggling with reviewer feedback finds a tool that promises to help organize and respond to academic paper critiques.

2
🔧 Install the skill package

They install a ready-made helper that works alongside their AI writing assistant, following simple setup instructions.

3
📁 Set up a workspace for their paper

The tool creates a special folder in their project where it can safely store notes, drafts, and memories about their rebuttal.

4
🤖 AI analyzes reviewer concerns

Their AI assistant reads through the reviewer comments and breaks down each concern into manageable pieces, helping them understand what needs addressing.

5
Choose their approach
🧪
Plan rebuttal experiments

They work with the AI to decide which additional experiments are essential versus optional, prioritizing what will have the most impact.

✍️
Draft author response

They begin writing their rebuttal letter, with the AI helping format responses according to conference guidelines and keeping track of what they've addressed.

6
📄 Generate formatted rebuttal documents

The tool assembles their responses into properly formatted documents, including templates for one-page rebuttals or detailed per-reviewer replies.

✅ Submit with confidence

They have a well-organized, evidence-backed rebuttal ready to submit, with all reviewer concerns addressed in a clear and professional manner.

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AI-Generated Review

What is awesome-rebuttal?

Awesome-rebuttal is a skill package that helps AI coding assistants handle academic paper rebuttals. If you've ever submitted a paper to a venue like NeurIPS, ICML, or CVPR, you know the rebuttal phase is stressful -- parsing reviewer feedback, planning counter-experiments, and drafting responses while respecting strict formatting rules. This tool gives your AI assistant a structured workflow for all of that: it analyzes reviewer concerns, helps triage which experiments matter most, and drafts responses that won't get you rejected for breaking anonymity or venue rules. The package includes LaTeX templates and integrates with agents like Claude Code, Codex, and Cursor.

Why is it gaining traction?

The academic rebuttal process is notoriously painful, and existing tools don't address it. This project fills a real gap by combining strategy planning, evidence organization, and safe drafting into one workflow. The safety gates are particularly smart -- it explicitly blocks the AI from fabricating results or attacking reviewers, which is exactly the kind of mistake that could tank a submission. The multi-format support (one-page PDFs, OpenReview replies, hybrid responses) covers actual venue requirements rather than assuming everyone uses the same system.

Who should use this?

PhD students and researchers submitting to ML/CV/NLP/Robotics venues who want AI assistance during the rebuttal window. If you're juggling multiple reviewer concerns and deadline pressure, this keeps your response organized and prevents embarrassing policy violations. It's less useful for experienced researchers who already have rebuttal workflows, or for non-academic writing where these constraints don't apply.

Verdict

This solves a genuine problem, but the credibility score of 0.8999999761581421% reflects a very early-stage project with only 15 stars and limited community validation. The documentation is solid and the safety-first approach shows thoughtful design, but you're adopting experimental tooling for a high-stakes process. Consider it a promising addition to your workflow, but verify all outputs yourself before submission.

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