Review Rebuttal

Community

chtc66

review-rebuttal

analyze academic reviews and draft a professional rebuttal when the input includes reviewer comments, a meta-review, paper abstract, or user supplied experiment status and the user needs concern classification, response strategy, risk warnings, or a rebuttal draft without inventing evidence.

Overview

Publisher	chtc66
Repository	`academic-skills`
Skill name	`review-rebuttal`
Stars	364
Forks	27
Bundled files	4
License	MIT
Links	Skill source Repository

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.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by chtc66 on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/chtc66/academic-skills/tree/main/review-rebuttal
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.

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/chtc66/academic-skills.git /tmp/academic-skills
mkdir -p .claude/skills
cp -r /tmp/academic-skills/review-rebuttal .claude/skills/review-rebuttal

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review 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 Review 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 Review 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.

Review Rebuttal

用这个 skill 处理投稿后的 reviewer comments、meta-review 和 rebuttal 草稿。重点是拆 concerns、区分补实验与澄清项、组织回复策略，并保持专业克制的语气。

工作流

逐个 reviewer 拆分意见，再汇总跨 reviewer 的共性 concern。
参考 references/review_taxonomy.md 给 concern 分类。
参考 references/tone_guidelines.md 约束语气和风险表达。
参考 references/rebuttal_template.md 生成回复框架和草稿。

输入处理规则

接收 review 全文、meta-review、论文摘要、补充实验现状、作者说明。
如果用户只给摘要和 review，不要假装知道论文正文细节。
如果用户说“我们还没做这个实验”，在草稿中写成计划或澄清，不要写成已有结果。
支持多个 reviewer 合并分析，但要保留 reviewer 之间的差异。

输出规则

默认输出以下部分：
- 审稿意见拆分
- concern 分类
- 必须补实验的点
- 更适合澄清的点
- 回复策略
- rebuttal 草稿
- 高风险表述提醒
如果 reviewer 之间有冲突，单独指出，不要强行统一。
如果 meta-review 明确压住了某些意见，优先围绕 meta-review 组织总体策略。

证据与表述约束

不得编造实验、额外消融或人工分析结果。
不得假装已经补完实验。
不得使用对抗性或情绪化表述。
不要把 reviewer 的误解简单归咎于 reviewer 能力不足，应改写为“论文表述仍可更清楚地说明……”。

何时读引用文件

始终读取 references/rebuttal_template.md。
在 concern 分类时读取 references/review_taxonomy.md。
在起草回复时读取 references/tone_guidelines.md。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Review Rebuttal AI skill do?

Why use Review Rebuttal on TypingMind?

Because you install it once and use it with any model. Review 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 Review Rebuttal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chtc66/academic-skills/tree/main/review-rebuttal. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Review 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 Review Rebuttal?

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

Is the Review Rebuttal AI skill free?

Yes. It is published on GitHub by chtc66 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Review Rebuttal

工作流

输入处理规则

输出规则

证据与表述约束

何时读引用文件

Bundled files

Frequently asked questions

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