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Research
Skill

/rebuttal-writing

Write point-by-point rebuttals to reviewer comments. Extract concerns from reviews, generate evidence-based responses, and format as a structured rebuttal document. Use after receiving peer review feedback.

From plugin
agent-research-skills
26531 skills1 command
Install
$ npx -y skills add lingzhi227/agent-research-skills --skill rebuttal-writing --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/rebuttal-writing

Context preview

The summary Claude sees to decide when to auto-load this skill.

Write point-by-point rebuttals to reviewer comments. Extract concerns from reviews, generate evidence-based responses, and format as a structured rebuttal document. Use after receiving peer review feedback.

SKILL.md

rebuttal-writing.SKILL.md
name: rebuttal-writing
description: Write point-by-point rebuttals to reviewer comments. Extract concerns from reviews, generate evidence-based responses, and format as a structured rebuttal document. Use after receiving peer review feedback.
argument-hint: [reviews-file]

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

**Concern #1:** [extracted concern]
**Author Response:** [detailed response with evidence]

**Concern #2:** [extracted concern]
**Author Response:** [detailed response with evidence]

## Reviewer #2
...

Step 4: Summary of Changes

Add a brief summary at the top listing all major changes made to the paper:

  • New experiments added
  • Sections revised
  • Clarifications made

Rules

  • **Reply with what was done, not what will be done** — "We have conducted additional experiments" not "We will conduct..."
  • **Be specific** — Reference exact sections, table numbers, equation numbers
  • **Be respectful** — Thank reviewers, acknowledge valid concerns
  • **Address every concern** — Do not skip any reviewer point
  • **Provide evidence** — Every response should include concrete data, citations, or reasoning
  • **Keep responses concise** — Detailed enough to address the concern, but not padded
  • **Highlight changes** — When referring to modified text, use blue text or clearly mark revisions

Related Skills

  • Upstream: [self-review](../self-review/), [paper-revision](../paper-revision/)
  • Downstream: [paper-compilation](../paper-compilation/)
  • See also: [data-analysis](../data-analysis/)
Read more
Ships withagent-research-skills

31 skills for Claude Code covering the full academic research paper lifecycle — from literature search to slide generation — plus GitHub repository analysis for research topics. Extracted from 17 GitHub repos studying LLM-agent-driven research automation.

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Repo: lingzhi227/agent-research-skills

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