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Amj Review Process

Community
franklee16
amj-review-process

Use when you need to understand or set expectations for the Academy of Management Journal (AMJ) editorial and peer-review process — desk review, the developmental multi-round R&R culture, and reading a decision letter. Explains the process and how to read decisions; it does not draft the response letter (amj-rebuttal).

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamj-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 Amj 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-Journal-Skills/skills/amj-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-Journal-Skills/skills/amj-review-process .claude/skills/amj-review-process
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amj 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 Amj 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 Amj 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 the Review Process (amj-review-process)

When to trigger

  • Before submitting, to calibrate expectations about timeline and decision types
  • A decision letter arrived and you need to interpret it (R&R vs. reject; major vs. minor)
  • You are unsure how much weight reviewers vs. the action editor carry
  • You want to plan a revision strategy before drafting the response (then go to amj-rebuttal)

Editorial structure, named editors, and exact timelines change. Verify the current editorial team, policies, and turnaround on the official AMJ site; the norms below are durable. As of 2026-05-30: AMJ uses blind review, targets constructive developmental feedback to authors within ~60 days, and the incoming Editor-in-Chief is Gary A. Ballinger (University of Virginia), whose team began receiving submissions on 1 July 2025.

How AMJ review works (durable norms)

  1. Submission & routing. The editor (or an associate/action editor) screens the manuscript and routes it to an action editor in the relevant division. Submission is via ScholarOne (mc.manuscriptcentral.com/AMJ) with all author identifiers removed for blind review.
  2. Desk decision. Manuscripts that are off-fit, atheoretical, fatally underpowered, or that miss one of AMJ's three criteria (empirical contribution, theoretical contribution, practical relevance) may be desk-rejected without external review. A clear theoretical contribution and appropriate design are what get a paper past the desk.
  3. External peer review. Typically several (often three) reviewers in the focal conversation evaluate the theoretical contribution, design rigor, and analysis; AMJ aims to return developmental feedback within roughly 60 days.
  4. Action editor synthesis. The action editor weighs the reviews, forms an independent judgment, and writes the decision letter. The editor's letter — not a vote count — drives the outcome.
  5. Decision. Common outcomes: reject; reject with encouragement to resubmit as new; major revision (R&R); minor revision; (rarely) accept. A first-round acceptance is essentially unheard of.

AMJ is developmental and multi-round

AMJ's culture is developmental: an R&R is an invitation to improve the paper over (often) multiple rounds. Reviewers and the action editor push hard on the theoretical contribution and the method — expect requests to deepen theory, add a study, strengthen identification, or address common-method/endogeneity concerns. Treat an R&R as a serious opportunity, not a near-acceptance, and not a rejection.

Reading the decision letter

  • Find the editor's priorities first. The action editor's letter signals which reviewer points are essential vs. optional. Address the editor's framing above all.
  • Distinguish "fatal" from "fixable." If a core identification or theory concern is fatal and unaddressable, an honest reframe (or a different paper) beats a doomed revision.
  • Gauge encouragement. Language like "we see promise" + a major revision signals a real path; "does not fit our developmental model" signals reject.
  • Count the asks. Map every reviewer point to: theory / method / analysis / framing / writing — this becomes the revision plan that feeds amj-rebuttal.

Planning the revision

  • Decide whether you can satisfy the theoretical-contribution demands; if not, the revision will not succeed regardless of method fixes.
  • Scope the new analyses/data the reviewers imply and whether they are feasible in the revision window.
  • Identify points where reviewers conflict; plan to surface the conflict for the editor rather than silently siding with one.

Checklist

  • Decision type identified (R&R major/minor, reject-resubmit, reject)
  • The action editor's priorities extracted and ranked above individual reviewer asks
  • Every reviewer comment categorized (theory / method / analysis / framing / writing)
  • Fatal vs. fixable concerns separated honestly
  • Feasibility of demanded new theory/data/analyses assessed against the deadline
  • Reviewer conflicts noted for the editor's attention

Anti-patterns

  • Treating an R&R as a near-acceptance and making only cosmetic changes.
  • Treating an R&R as a rejection and walking away from a viable path.
  • Counting reviewer votes instead of reading the action editor's letter.
  • Ignoring the editor's stated priorities while over-investing in a minor reviewer point.
  • Starting to write the response letter before mapping and planning the revision.

Output format

【Decision type】R&R(major/minor) / reject-resubmit / reject
【Editor's priorities】1... 2... 3...
【Comment map】theory:[...] method:[...] analysis:[...] framing:[...] writing:[...]
【Fatal vs. fixable】...
【New work needed】theory / data / analysis — feasible? ...
【Reviewer conflicts】...
【Next step】amj-rebuttal (plan revisions, then draft response)

Frequently asked questions

What does the Amj Review Process AI skill do?

Use when you need to understand or set expectations for the Academy of Management Journal (AMJ) editorial and peer-review process — desk review, the developmental multi-round R&R culture, and reading a decision letter. Explains the process and how to read decisions; it does not draft the response letter (amj-rebuttal).

Why use Amj Review Process on TypingMind?

Because you install it once and use it with any model. Amj 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 Amj 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-Journal-Skills/skills/amj-review-process. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Amj 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 Amj Review Process?

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

Is the Amj 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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