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Amr Topic Selection

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franklee16
amr-topic-selection

Use when the theoretical puzzle is the bottleneck for an Academy of Management Review (AMR) manuscript — deciding whether an idea is theory-worthy and AMR-fit before any theorizing begins. Frames the puzzle; it does NOT build constructs (that is amr-theory-development) or design any empirical study (AMR has none).

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamr-topic-selection
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 Topic Selection 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-topic-selection
  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-topic-selection .claude/skills/amr-topic-selection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amr Topic Selection 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 Topic Selection 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 Topic Selection 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.

Topic Selection: Is There a Theoretical Puzzle? (amr-topic-selection)

When to trigger

  • You have an interesting phenomenon, trend, or anomaly but no theory yet
  • You are unsure whether the idea is "big enough" for AMR
  • The idea feels like a study you'd run rather than a theory you'd build
  • You can describe what happens but not why existing theory fails to explain it

The AMR fit test

AMR wants a theoretical puzzle, not a topic. A topic is "leadership and remote work." A puzzle is "existing leadership theory predicts X, yet under condition C we should see not-X — and no current theory resolves the contradiction." Run every idea through:

  1. Is it conceptual? AMR publishes no data. If the natural next step is "collect a sample," stop — that is AMJ/ASQ/SMJ. AMR builds the theory those journals later test.
  2. Is there a genuine puzzle / tension / contradiction? A gap ("no one has studied Z") is NOT a puzzle. A puzzle is a place where current theory is internally inconsistent, incomplete in a specific way, or makes a prediction that fails.
  3. Whose theory is at stake? Name the existing theory you will challenge and extend. If you cannot, route to amr-literature-positioning.
  4. Will it change how scholars think? AMR asks for theory that reorients a conversation, not an incremental relabeling.
  5. Does it yield testable knowledge-based claims? AMR's scope requires submissions to "extend theory in ways that develop testable knowledge-based claims." You do no testing — but your propositions must, in principle, be refutable by someone, later.
  6. Is the scope a journal article, not a book? One focused theoretical move, fully developed.

AMR's own scope names four legitimate paths for a topic: (a) develop new theory; (b) significantly challenge or clarify existing theory; (c) synthesize recent advances into fresh theory; (d) identify and delineate a novel theoretical problem. If your idea fits none of these, it is likely not AMR. Exemplars to emulate: Oliver's "Strategic Responses to Institutional Processes" (AMR 1991, DOI 10.5465/amr.1991.4279002) challenged institutional theory's conformity assumption by theorizing agency; Dyer & Singh's "The Relational View" (AMR 1998, DOI 10.5465/amr.1998.1255632) synthesized a new locus of advantage. Neither used data.

Sources of a strong AMR puzzle

Puzzle typeLooks like
Theoretical contradictionTwo established theories make opposite predictions in the same setting
Boundary failureAn accepted theory breaks down at a condition no one has theorized
Construct ambiguityA widely used construct conflates two distinct phenomena that should be separated
Missing mechanismAn empirical regularity is robust but the why has never been theorized
Phenomenon outrunning theoryA new organizational form / practice that current categories cannot capture
ReconceptualizationAn old construct, re-specified, dissolves a long-standing debate

From phenomenon to puzzle (worksheet)

  • State the phenomenon in one sentence (no jargon).
  • State what existing theory predicts here.
  • State why that prediction is incomplete, wrong, or self-contradictory.
  • State the one-sentence theoretical question that the paper will answer.
  • State what scholars will be able to think/explain after your paper that they cannot now.

If any line is blank, the idea is not yet AMR-ready.

Checklist

  • The deliverable is theory, not an empirical test (no data implied as the payoff)
  • A specific tension / contradiction / breakdown is named, not merely a "gap"
  • The existing theory to be challenged and extended is identified
  • The theoretical question fits in one sentence
  • The "so what for theory" is articulable before any propositions exist
  • Scope is one focused theoretical move, not a survey of a field

Anti-patterns

  • "No one has studied X" framed as the contribution (a gap is not a puzzle)
  • An empirical project wearing a theory costume — the real payoff is data
  • A literature-review topic ("the state of research on X") with no new theoretical move
  • Relabeling an existing construct and calling it new
  • A puzzle so broad it would take a book; or so narrow it is a footnote to one paper

The most common AMR desk reject is "this is an empirical paper"; the next is "incremental theory." Screen for both here, before investing in amr-theory-development.

Output format

【Phenomenon】one sentence
【Theoretical tension】what existing theory predicts vs. what should hold
【Theory at stake】the conversation to challenge & extend
【Theoretical question】one sentence
【So-what for theory】what scholars can newly explain
【AMR fit】yes / borderline (route empirical work elsewhere) / no
【Next step】amr-literature-positioning

Frequently asked questions

What does the Amr Topic Selection AI skill do?

Use when the theoretical puzzle is the bottleneck for an Academy of Management Review (AMR) manuscript — deciding whether an idea is theory-worthy and AMR-fit before any theorizing begins. Frames the puzzle; it does NOT build constructs (that is amr-theory-development) or design any empirical study (AMR has none).

Why use Amr Topic Selection on TypingMind?

Because you install it once and use it with any model. Amr Topic Selection 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 Topic Selection 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-topic-selection. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Amr Topic Selection?

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 Topic Selection?

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

Is the Amr Topic Selection 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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