Turns peer review comments into structured, evidence-based rebuttals with proper academic formatting. It parses reviewer concerns, categorizes them by severity, then generates responses that cite specific sections, equations, and experimental results from your paper. The workflow enforces good rebuttal practices: acknowledge the point, respond with concrete evidence, and write in past tense ("We have added" not "We will add"). Outputs in standard rebuttal format with reviewer-by-reviewer responses and a summary of changes at the top. Useful after getting reviews back, especially when you're staring at three pages of criticism and need to organize your responses methodically. Saves you from the tedious work of extracting and numbering each concern manually.

skills.sh installs
1.7K
GitHub stars
459
last updated
Aug 21, 2026

Install

$ npx -y skills add lingzhi227/agent-research-skills --skill rebuttal-writing --agent claude-code

Installs into .claude/skills of the current project.

Files

Rebuttal Writing

Generate structured, evidence-based rebuttals to peer review comments.

Input

  • $0 — Reviewer comments (text file, or pasted directly)
  • Optional: current paper draft for reference

References

  • Rebuttal prompts and format templates: ~/.claude/skills/rebuttal-writing/references/rebuttal-prompts.md

Workflow

Step 1: Parse Review Comments

For each reviewer:

  1. Extract individual concerns/questions/weaknesses
  2. Categorize each: major concern, minor concern, question, suggestion
  3. Identify the core issue behind each concern

Step 2: Generate Responses

For each concern:

  1. Acknowledge the reviewer's point
  2. Respond with evidence — cite specific sections, equations, experiments, or results from the paper
  3. Describe what was done (not what will be done) — "We have added...", "Our experiments show..."
  4. If additional experiments are needed, describe the new results concretely

Step 3: Format Rebuttal

Use the standard rebuttal format:

# Response to Reviewers

We thank all reviewers for their constructive feedback. We address each concern below.

## Reviewer #1

Sponsors

Details

Repository
github.com/lingzhi227/agent-research-skills
Categories
Git & Pull Requests, Documentation, Code Review & Quality, AI & Agent Building, Data Science & ML
First seen
Jun 3, 2026

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