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Amj Topic Selection

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franklee16
amj-topic-selection

Use when scoping or sanity-checking a research question for an Academy of Management Journal (AMJ) manuscript — fit, theory-drivenness, and feasibility. Decides whether the idea can earn a theoretical contribution; it does not build the theory itself (use amj-theory-development) or position the literature (amj-literature-positioning).

Overview

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

Use it in TypingMind

Enable Amj 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 Amj 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 Amj 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 & AMJ Fit (amj-topic-selection)

When to trigger

  • You have a dataset, a context, or a phenomenon, but no sharp research question
  • You are unsure whether the idea belongs at AMJ vs. its AOM sister AMR (theory-only, no data), or at non-AOM venues ASQ (Cornell/SAGE) or SMJ (Wiley/SMS), or a field journal
  • The question feels like "no one has studied X here" rather than "theory predicts something surprising"
  • You want to pressure-test fit before investing months in data collection

AMJ fit test (all four must pass)

AMJ's mission is "to publish empirical research that tests, extends, or builds management theory and contributes to management practice," and it judges every paper on three criteria: a strong empirical contribution, a meaningful theoretical contribution, and practical relevance. The fit test below operationalizes those criteria.

  1. Phenomenon-grounded but theory-driven. AMJ wants real organizational phenomena (micro OB, macro/strategy, HR, entrepreneurship, organizational theory) examined through a theoretical lens. A purely descriptive or purely methodological paper does not fit.
  2. A theoretical mechanism, not just an empirical association. Ask: why would A affect B, and when? If you can only say "they are correlated," you have a topic, not yet an AMJ paper.
  3. Original data and a defensible design are feasible. AMJ is empirical and welcomes all empirical methods — qualitative, quantitative, field, laboratory, meta-analytic, and mixed. If you cannot get suitable data of any of these kinds, it is an AMR idea (theory-only, no data), not an AMJ idea.
  4. Surprise or tension. The strongest AMJ questions sit on a tension between theories, a counterintuitive boundary condition, or an unresolved debate — not a confirmation of the obvious.

Choosing the divisional home

AMJ routes manuscripts to divisional action editors. Knowing your home sharpens framing:

If your core construct is...Likely division / framing
Individuals/teams (motivation, leadership, emotion)Micro / OB
Firms/industries (strategy, performance, M&A)Macro / strategy
Selection, training, turnover, careersHR
New ventures, founders, opportunityEntrepreneurship
Institutions, identity, sensemaking, fieldsOrganization theory / OMT

Frame the question in the vocabulary of its home division and cite that division's recent AMJ papers.

From phenomenon to question

  • State the phenomenon in one sentence (what happens in the world).
  • Name the focal theory you will use as a lens.
  • Write the research question as a why/when statement, not a whether statement.
  • Identify the surprising tension: what does naive intuition or a rival theory predict instead?
  • Draft a one-line "potential theoretical contribution" claim and sanity-check it: would a reviewer learn something new about the theory?

Checklist

  • Question is framed as why/when, with an explicit mechanism, not just whether A predicts B
  • There is a tension, puzzle, or boundary condition (not a confirmation of the obvious)
  • The phenomenon is a genuine organizational/management phenomenon, not a methods demo
  • Suitable original data and a credible design are feasible (else it is an AMR idea)
  • Target division identified; 3–5 recent AMJ papers in that division read and notable
  • One-line theoretical-contribution claim drafted and judged non-trivial

Anti-patterns

  • Gap-spotting: "No study has examined X in context Y." A gap is not a contribution; AMJ wants a theoretical reason the gap matters.
  • Borrowed phenomenon, no theory: an interesting dataset with no theoretical question behind it.
  • Wrong venue: a purely conceptual idea (belongs at AMR) or a pure methods/measurement paper.
  • Over-scoping: three theories and ten constructs in one paper — pick the one tension you can defend.
  • Context as the only novelty: "same model, new country/industry" without a theoretical reason the context changes the mechanism.

Output format

【Phenomenon】one sentence (what happens in the world)
【Focal theory / lens】...
【Research question】why/when ...
【Tension / surprise】rival prediction or puzzle ...
【Candidate theoretical contribution】one line ...
【Division / framing home】micro / macro / HR / entrepreneurship / OMT
【AMJ fit verdict】fit / borderline / better at AMR/ASQ/SMJ/field
【Next step】amj-theory-development (build mechanism + hypotheses)

Frequently asked questions

What does the Amj Topic Selection AI skill do?

Use when scoping or sanity-checking a research question for an Academy of Management Journal (AMJ) manuscript — fit, theory-drivenness, and feasibility. Decides whether the idea can earn a theoretical contribution; it does not build the theory itself (use amj-theory-development) or position the literature (amj-literature-positioning).

Why use Amj Topic Selection on TypingMind?

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

Which AI models can use Amj 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 Amj Topic Selection?

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

Is the Amj 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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