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Amr Review Process

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franklee16
amr-review-process

Use when you need to understand the Academy of Management Review (AMR) developmental, multi-round review and interpret a decision letter focused on theoretical novelty and logical soundness. Explains the process and decision types; it does NOT draft the response document (that is amr-rebuttal).

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamr-review-process
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 Review Process 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-review-process
  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-review-process .claude/skills/amr-review-process
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amr Review Process 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 Review Process 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 Review Process 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.

Understanding AMR's Developmental Review (amr-review-process)

When to trigger

  • You just received an AMR decision letter and need to interpret it
  • You do not know what "developmental review" means or what is expected of you
  • You are deciding whether an R&R is worth pursuing and how to read the editor's signals
  • You need to triage reviewer comments by what AMR actually weights

What AMR review is (durable norms)

AMR uses a double-anonymous, multi-round, developmental review. "Developmental" is the operative word: the goal is to help promising theory papers become stronger, so reviews are detailed and the bar rises across rounds. Verify current specifics on the official page, but the durable shape is:

  • A handling editor (the Editor-in-Chief — currently Kris Byron (Georgia State University), AMR EIC since 2023 succeeding Sherry M. B. Thatcher; verify the current slate on the AOM AMR editorial-leadership page — or an action editor) assigns the paper to several reviewers expert in your conversation. (Exact reviewer count and AMR's acceptance rate are UNVERIFIED — see source map; the rate is low, as for all top AOM journals.)
  • Reviews focus on the things AMR exists to protect: a genuine theoretical contribution (Whetten's What/How/Why/Who-Where-When, AMR 1989, DOI 10.5465/amr.1989.4308371), construct clarity (Suddaby, AMR 2010, DOI 10.5465/amr.2010.0419), and the logical soundness of the argument — not data (there is none). Reviewers do not ask about identification, sample size, robustness, or measurement validity; those are AMJ/ASQ/SMJ criteria.
  • Decisions are commonly: Reject; Reject & Resubmit (encouraged to rebuild, treated as a fresh submission); Major Revision (R&R); less often Minor Revision; rarely an immediate Accept.
  • An R&R is an invitation, not a promise. Multiple rounds are normal at AMR.

Desk-reject triggers specific to AMR

The editor screens for fit and contribution before sending a paper out. Most common AMR desk rejects: (1) "this is an empirical paper"; (2) incremental theory; (3) a literature review with no original move; (4) construct proliferation (relabeling); (5) untestable-in-principle or tautological propositions; (6) poor fit/scope or anonymization failures.

Reading the decision letter

Signal in the letterWhat it usually meansYour move
"Promising but the contribution is not yet clear"Theory may be sound but under-differentiatedRe-run amr-contribution-framing
"Propositions are not well supported by the argument"Logic gaps between premises and propositionsRe-run amr-data-analysis, then amr-theory-development
"Reads as a review of the literature"No real theoretical moveRe-run amr-literature-positioning + amr-writing-style
"Constructs overlap existing ones"Construct distinctiveness challengedRe-run amr-theory-development (domain/distinction)
"Scope/level inconsistency"Level-of-theory confusionRe-run amr-methods (cross-level)
Editor highlights specific reviewer points as essentialThe editor's prioritiesAddress these first and most fully

Triaging reviewer comments

  • Read the editor's letter as the master key. When reviewers disagree, the editor's framing tells you which issues are decision-critical.
  • Separate theory-substance comments from presentation comments. Substance (a missing mechanism, an undefended proposition) outranks wording.
  • Distinguish "must change the theory" from "must explain better." Some comments mean the theory is wrong; others mean the prose hid a sound argument.
  • Note conflicts between reviewers and plan to adjudicate them transparently in the response (handled in amr-rebuttal).

Checklist

  • Decision type identified (Reject / R&R / Reject & Resubmit / Minor)
  • The editor's stated priorities are extracted and ranked
  • Each reviewer comment tagged: theory-substance vs. presentation
  • Comments mapped to the skill that addresses them (theory / logic / positioning / contribution)
  • Reviewer conflicts identified for transparent adjudication
  • Realistic judgment made on whether the revision can meet the raised bar

Anti-patterns

  • Treating an R&R as acceptance and making only cosmetic changes
  • Ignoring the editor's letter and responding only to reviewers
  • Reading "developmental" as "the reviewers will fix it for me"
  • Promising in a rebuttal to add data — AMR neither wants nor accepts data
  • Underestimating the number of rounds and over-claiming the revision is final

Output format

【Decision type】Reject / Reject & Resubmit / Major (R&R) / Minor
【Editor's priorities】ranked list
【Comment triage】substance: [...] | presentation: [...]
【Skill mapping】comment → amr-* skill
【Reviewer conflicts】[...]
【Go / no-go】pursue revision? rationale
【Next step】amr-rebuttal

Frequently asked questions

What does the Amr Review Process AI skill do?

Use when you need to understand the Academy of Management Review (AMR) developmental, multi-round review and interpret a decision letter focused on theoretical novelty and logical soundness. Explains the process and decision types; it does NOT draft the response document (that is amr-rebuttal).

Why use Amr Review Process on TypingMind?

Because you install it once and use it with any model. Amr Review Process 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 Review Process 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-review-process. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Amr Review Process?

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 Review Process?

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

Is the Amr Review Process 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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