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Amr Rebuttal

Community
franklee16
amr-rebuttal

Use when writing the response document for an Academy of Management Review (AMR) Revise & Resubmit — structuring point-by-point replies that show the theory was genuinely strengthened, not just defended. Drafts the response; revise the manuscript's theory FIRST (amr-theory-development / amr-data-analysis) before writing the letter.

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamr-rebuttal
Stars
225
Forks
34
Bundled files
Instructions only
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 franklee16 on GitHub. Read the source before you install it.

Installation

Install the Amr 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/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Academy-of-Management-Review-Skills/skills/amr-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/franklee16/academic-research-skills.git /tmp/academic-research-skills
mkdir -p .claude/skills
cp -r /tmp/academic-research-skills/Awesome-Journal-Skills-main/Academy-of-Management-Review-Skills/skills/amr-rebuttal .claude/skills/amr-rebuttal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amr 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 Amr 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 Amr 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.

R&R Response Document (amr-rebuttal)

When to trigger

  • You have an AMR R&R (Major Revision) or a Reject & Resubmit you intend to pursue
  • The theory has been revised and you now need the response-to-reviewers document
  • You must adjudicate conflicting reviewer requests in writing

Revise the theory before you write the letter. A rebuttal that argues without changing the manuscript fails at AMR. Run amr-data-analysis and amr-theory-development first.

Structure of the response

  1. Opening to the editor. Thank the team; summarize the theoretical changes at a high level (new construct definitions, sharpened mechanism, added boundary conditions, clearer contribution). One short paragraph — substance, not flattery.
  2. Roadmap of major changes. A short list of the biggest theory moves you made, so the editor sees the revision's shape before the point-by-point.
  3. Point-by-point responses. For every reviewer comment, in order:
    • Restate the comment (verbatim or faithfully paraphrased).
    • State what you changed in the theory.
    • Quote or cite the revised manuscript location (section/proposition/page).
    • If you disagree, argue it on theoretical grounds — respectfully and with reasoning.
  4. Handling reviewer conflicts. When reviewers disagree, say so, explain your adjudication, and tie it to the editor's stated priorities.

How to respond well at AMR

  • Show, don't assert. "We strengthened the argument" is empty; point to the revised premise, the new mechanism, the added boundary condition.
  • Respond to theory comments with theory. The reviewers are theorists; a substance comment needs a substantive theoretical answer, not a wording tweak.
  • Never offer data. If a reviewer asks for "evidence," reframe within AMR's mission: strengthen the logical support, the mechanism, the boundary conditions, or the disconfirming-case treatment — not an empirical test.
  • Concede what is right. If a reviewer found a genuine logical gap, fix it and say so plainly. Graceful concession builds credibility for the points you do contest.
  • Defend the contribution explicitly if novelty was questioned — restate the before → after and the differentiation from the nearest prior theory, framed against Whetten's What/How/Why/Who-Where-When (AMR 1989, DOI 10.5465/amr.1989.4308371).
  • Sharpen constructs if distinctiveness was challenged — redefine with scope conditions per Suddaby's construct-clarity criteria (AMR 2010, DOI 10.5465/amr.2010.0419).
  • Keep the tone collegial. AMR review is developmental and typically runs multiple rounds, with the bar rising each round; the response should read as a conversation among theorists. (At AMR's empirical sibling AMJ, "we ran additional analyses and the result is robust" is a strong move; at AMR the equivalent strength comes from a tighter, more complete argument.)

Per-comment template

Comment R[n].[k]: [restated comment]
Response: [what changed in the theory + why]
Location: [Section X / Proposition Pn / p. Y]
[If contesting] Respectfully, we retain [X] because [theoretical reasoning].

Checklist

  • The manuscript's theory was actually revised before the letter was written
  • Every comment from every reviewer (and the editor) has a numbered response
  • Each response points to a concrete manuscript location
  • Theory-substance comments answered with theory, not wording
  • No response promises or adds empirical data
  • Reviewer conflicts adjudicated transparently and tied to the editor's priorities
  • Genuine concessions made where reviewers were right
  • Contribution re-defended if novelty was challenged
  • Tone collegial and developmental throughout

Anti-patterns

  • Arguing in the letter without changing the manuscript
  • "We thank the reviewer and have addressed this" with no concrete location or change
  • Answering a deep theoretical objection with a cosmetic edit
  • Promising data or "future empirical validation" as if it resolves a theory gap
  • Defensive or dismissive tone toward a reviewer's substantive point
  • Ignoring one reviewer to please another instead of adjudicating openly
  • Over-claiming the revision is "final" when AMR R&Rs typically run multiple rounds

Output format

【Decision being answered】R&R / Reject & Resubmit
【Theory revised first?】yes (summary of changes) / no → revise first
【Coverage】every editor+reviewer comment has a numbered response: yes/no
【Data offered?】must be: none
【Conflicts adjudicated】[...]
【Contribution re-defended】yes / n/a
【Next step】resubmit via ScholarOne → amr-review-process for the next round

Frequently asked questions

What does the Amr Rebuttal AI skill do?

Use when writing the response document for an Academy of Management Review (AMR) Revise & Resubmit — structuring point-by-point replies that show the theory was genuinely strengthened, not just defended. Drafts the response; revise the manuscript's theory FIRST (amr-theory-development / amr-data-analysis) before writing the letter.

Why use Amr Rebuttal on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Academy-of-Management-Review-Skills/skills/amr-rebuttal. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Amr 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 Amr Rebuttal?

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

Is the Amr Rebuttal AI skill free?

It is published on GitHub by franklee16. Check the repository for licensing terms. 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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