rebuttal

Parse reviewer feedback into classified items and plan manuscript edits and experiments.

3.3k|331|Updated Sep 26, 2025
One-click install
npx skills add https://github.com/ResearAI/DeepScientist --skill rebuttal-researai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rebuttal
Source: https://github.com/ResearAI/DeepScientist/tree/main/src/skills/rebuttal
Command: npx skills add https://github.com/ResearAI/DeepScientist --skill rebuttal-researai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Rebuttal orchestration for review-driven work: it converts reviewer comments, meta-reviews, and editor letters into a durable, auditable rebuttal workflow that can be executed with a structured plan.

Core Features & Use Cases

  • Normalize reviewer input into stable items with IDs like R1-C1.
  • Generate an action plan and evidence updates, linking to manuscript sections and experiments.
  • Produce a ready-to-send response letter and a concise memory of the revision strategy.
  • Use cases include revision in response to journal/review rounds, mapping concerns to experiments, and ensuring traceable traceability.

Quick Start

Transform reviewer comments into an auditable rebuttal workflow and draft the initial action plan and response letter.

Frequently Asked Questions about rebuttal

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write a structured rebuttal letter for journal reviewer feedback?

A structured rebuttal letter maps reviewer feedback into stable items with IDs, classifies concerns, and plans manuscript edits with evidence updates to ensure traceable responses. This workflow generates a ready-to-send response letter and a revision strategy memory.

What is the best way to map reviewer comments to manuscript edits and experiments?

Mapping reviewer comments involves parsing the review packet, classifying items, and linking each concern to specific manuscript sections or supplementary experiments. This action planning creates an auditable traceability record connecting feedback directly to evidence updates.

Can I normalize reviewer feedback from meta-reviews and editor letters into a single workflow?

Yes, normalizing reviewer feedback converts meta-reviews and editor letters into stable items with unique IDs. This orchestration creates a durable, auditable rebuttal workflow executed through a structured action plan for manuscript revisions.

How does an analysis-first rebuttal workflow handle supplementary experiment planning?

An analysis-first rebuttal workflow handles supplementary experiments by mapping reviewer concerns to specific experimental actions and evidence updates. It links planned experiments directly to manuscript sections, ensuring traceable and reproducible documentation of the revision strategy.

Do I need prior manuscript data to generate a response to reviewers?

You need the reviewer comments, meta-reviews, and editor letters to generate a response to reviewers. The workflow parses this review packet to classify items and produce an action plan, ensuring the response letter accurately reflects the required manuscript edits.

When should I use a rebuttal workflow instead of drafting revisions manually?

Use a rebuttal workflow when facing complex review rounds requiring traceable evidence mapping and structured action planning. It prevents disorganized revisions by normalizing feedback into durable items and generating a reproducible response strategy instead of ad-hoc drafting.