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Rebuttal

Community
fcakyon
rebuttal

Prepare a paper for peer review. Reads the paper and experiment results, selects the strongest ablations, anticipates reviewer questions, finds weaknesses before submission, and drafts rebuttals to actual reviews. Use when the user asks about "reviewer questions", "anticipate reviewers", "rebuttal", "paper weaknesses", "defend the paper", "strengthen the paper", "select the strongest ablations", "fortify the paper", or "prepare for peer review".

Overview

Publisherfcakyon
Repositorylabrat
Skill namerebuttal
Stars
438
Forks
38
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by fcakyon on GitHub. Read the source before you install it.

Installation

Install the Rebuttal AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/fcakyon/labrat/tree/main/plugin/skills/rebuttal
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/fcakyon/labrat.git /tmp/labrat
mkdir -p .claude/skills
cp -r /tmp/labrat/plugin/skills/rebuttal .claude/skills/rebuttal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rebuttal in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Rebuttal on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Rebuttal is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Rebuttal: Prepare for Peer Review

Venue: $ARGUMENTS

Step 1: Read the Paper and Results

Discover the paper, result and config files as in Step 1 (Discover Files) of /labrat:paper-check, including wandb logs and checkpoint directories. Then list the paper's claims (contributions and experiments), its metrics and baselines, and every available run and its outcome, including the ablation variants the configs define.

Step 2: Vulnerability Analysis

Identify weaknesses from a reviewer's perspective:

Technical Weaknesses

  • Missing baselines that reviewers would expect
  • Evaluation metrics that don't fully capture the contribution
  • Assumptions stated without justification
  • Scalability or generalization concerns not addressed
  • Missing error analysis or failure case discussion

Presentation Weaknesses

  • Claims stronger than evidence supports
  • Missing related work that a reviewer in the area would know
  • Unclear methodology (could someone reimplement from the paper alone?)
  • Figures that don't clearly convey the intended message
  • Inconsistencies between sections

Experimental Weaknesses

  • Small dataset size without justification
  • Missing statistical significance tests
  • No comparison with state-of-the-art on standard benchmarks
  • Hyperparameter sensitivity not explored
  • No computational cost comparison

Step 3: Venue-Specific Anticipation

If a venue was given, use its matching entry below.

Top-tier ML/CV conferences (CVPR, NeurIPS, ICLR, ECCV):

  • Expect extensive ablation studies
  • Strong baseline comparisons required
  • Novelty must be clearly articulated
  • Reproducibility is valued

Workshops:

  • More tolerant of work-in-progress
  • Interesting ideas valued over exhaustive evaluation
  • Novel applications of existing methods are acceptable

Journals:

  • Expect thorough related work discussion
  • Deeper analysis and more experiments than conferences
  • Writing quality and organization matter more

Step 4: Question Generation

List the 10 most likely reviewer questions, most likely first, phrased as a reviewer would write them, in this form:

Q: [Reviewer question]
Motivation: [Why this would be asked]
Answerable: [Yes, cite Table X / No, would need experiment Y]
Draft response: [If answerable, 2-3 sentences]

Step 5: Ablation Selection

Take each delta from Ablation analysis in /labrat:compare (same-step, interaction-checked), then rank:

RunFactor ChangedPrimary Metric DeltaNarrative StrengthInclude?

Ranking criteria:

  • Impact magnitude: how much does it change the primary metric? The largest improvement is the most convincing.
  • Narrative strength: does it directly support a specific paper claim?
  • Uniqueness: does it show something no other ablation shows?
  • Anticipated questions: does it preemptively answer likely reviewer concerns?
  • Cost: main paper vs appendix (based on space constraints)
  • Coverage: together the picks prove the core contribution, show each component's value and answer the top questions from Step 4

Recommend: main paper (top 3-5) vs supplementary (rest).

Step 6: Negative Results

Frame negative results as analysis, not failure ("We explored X but found it did not improve over Y because Z"), and put them in the supplementary if they do not fit the main paper.

Step 7: Rebuttal Preparation

If responding to actual reviews:

  1. Read ALL reviews before responding to any
  2. Identify common concerns across reviewers
  3. Prioritize: address factual errors first, then major concerns, then minor ones
  4. Be respectful: thank reviewers, acknowledge valid points
  5. Be specific: point to exact sections, tables, figures
  6. New experiments: only promise what you can deliver in the rebuttal period

Rebuttal structure per reviewer:

We thank Reviewer X for their thoughtful feedback.

**[Major concern]**: [Direct response with evidence]

**[Specific question]**: [Concrete answer]

**[Suggestion]**: [How we will incorporate it]

Output Format

## Rebuttal Report

### Weaknesses
[Table of categorized weaknesses with severity, prioritized]

### Recommended Ablation Subset
[Table with justification for each]

### Top 10 Anticipated Questions
[Numbered list with answerability and draft responses]

### Suggested Edits
[Specific paragraph-level improvements to strengthen before submission]

Add the per-reviewer rebuttal drafts from Step 7 when responding to actual reviews.

Frequently asked questions

What does the Rebuttal AI skill do?

Prepare a paper for peer review. Reads the paper and experiment results, selects the strongest ablations, anticipates reviewer questions, finds weaknesses before submission, and drafts rebuttals to actual reviews. Use when the user asks about "reviewer questions", "anticipate reviewers", "rebuttal", "paper weaknesses", "defend the paper", "strengthen the paper", "select the strongest ablations", "fortify the paper", or "prepare for peer review".

Why use Rebuttal on TypingMind?

Because you install it once and use it with any model. Rebuttal is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Rebuttal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fcakyon/labrat/tree/main/plugin/skills/rebuttal. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Rebuttal?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Rebuttal?

As many as you like. As long as a model supports skills, you can use Rebuttal with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Rebuttal AI skill free?

Yes. It is published on GitHub by fcakyon under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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