Amj Contribution Framing logo

Amj Contribution Framing

Community
franklee16
amj-contribution-framing

Use when turning results into an explicit theoretical contribution for an Academy of Management Journal (AMJ) manuscript — the "what new theory do we learn?" statement and the discussion section. Frames the contribution and implications; it does not build the original theory (amj-theory-development) or run the analysis (amj-data-analysis).

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamj-contribution-framing
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 Contribution Framing 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-contribution-framing
  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-contribution-framing .claude/skills/amj-contribution-framing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amj Contribution Framing 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 Contribution Framing 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 Contribution Framing 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.

Contribution Framing & Discussion (amj-contribution-framing)

When to trigger

  • Results are in but the "so what for theory" is vague or missing
  • The discussion restates findings instead of advancing the theory
  • A reviewer says the contribution is "incremental," "descriptive," or "unclear"
  • You can describe what you found but not what the field now knows that it did not before
  • The practical-implications paragraph is generic ("managers should pay attention to X")

The contribution question AMJ asks

AMJ evaluates every paper on three criteria: a strong empirical contribution, a meaningful theoretical contribution, and practical relevance. The empirical contribution must be paired with a theoretical one: what do we now understand about the theory that we did not before? — and the discussion must also speak to why it matters for management practice. A clear, defensible theoretical answer is essential; strong results with no theoretical advance read as a technical report, not an AMJ paper. AMJ's From the Editors tradition frames this as the "so what?" and "who cares?" tests.

Types of theoretical contribution (claim the right one)

Contribution typeWhat it does
Reveal a new mechanismExplains why an established effect occurs
Establish a boundaryShows when an effect holds, weakens, or reverses
Adjudicate competing theoriesShows which of two rival predictions holds, and why
Integrate / bridgeConnects two literatures into a more general account
Challenge an assumptionOverturns a taken-for-granted premise (problematization)
Introduce/refine a constructConceptualizes and validates a new construct that earns its keep

State explicitly which type(s) you claim; do not gesture vaguely at "contributing to the literature."

Writing the discussion

A strong AMJ discussion typically:

  1. Restates the core finding in theoretical terms (one short paragraph, no number-dump).
  2. Theoretical implications — for each, name the literature, state what changes, and how (this is the heart of the section).
  3. Practical implications — a genuine AMJ criterion, not an afterthought: specific to the phenomenon and grounded in the findings, never boilerplate. (Practical relevance is one of AMJ's three formal acceptance criteria, but it complements rather than substitutes for the theoretical contribution.)
  4. Limitations — honest, but framed as boundary conditions and future directions, not as fatal flaws.
  5. Future research — concrete questions the study opens, tied to the mechanism/boundary.

Match the discussion to the front end: the contributions you claim here must be the ones you promised in the introduction. A reviewer will check that the intro's promise and the discussion's delivery line up.

The contribution statement (intro and discussion)

Draft 2–4 explicit contribution sentences and place a version in the introduction and the discussion. Each should be of the form: "We contribute to [literature] by showing [mechanism/boundary/integration], which advances [theory] because [reason]."

Checklist

  • The theoretical contribution type is named (mechanism/boundary/adjudication/integration/assumption/construct)
  • 2–4 explicit contribution sentences, consistent between intro and discussion
  • Discussion advances theory, not just restates results
  • Practical implications are specific to the phenomenon and the findings
  • Limitations framed as boundary conditions + future directions, not fatal flaws
  • Future research questions follow from the mechanism/boundary, not generic
  • Intro's promised contribution = discussion's delivered contribution

Anti-patterns

  • Restating results as "contribution": "We found A affects B" is a finding, not a contribution.
  • Incremental framing: "We extend prior work to a new setting" with no theoretical change.
  • Over-claiming: asserting a paradigm shift the single study cannot support.
  • Boilerplate practical implications: "managers should be aware of X" untethered from the data.
  • Intro/discussion mismatch: promising one contribution, delivering another.
  • Limitations as confession: listing flaws without converting them into boundary conditions.

Output format

【Contribution type(s)】mechanism / boundary / adjudication / integration / assumption / construct
【Contribution sentences】1–4, intro + discussion aligned
【Theoretical implications】per literature: what changes and why ...
【Practical implications】specific, grounded ...
【Limitations → boundaries】...
【Future research】concrete, mechanism-tied ...
【Intro↔discussion consistency】yes/no
【Next step】amj-tables-figures, then amj-writing-style

Frequently asked questions

What does the Amj Contribution Framing AI skill do?

Use when turning results into an explicit theoretical contribution for an Academy of Management Journal (AMJ) manuscript — the "what new theory do we learn?" statement and the discussion section. Frames the contribution and implications; it does not build the original theory (amj-theory-development) or run the analysis (amj-data-analysis).

Why use Amj Contribution Framing on TypingMind?

Because you install it once and use it with any model. Amj Contribution Framing 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 Contribution Framing 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-contribution-framing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Amj Contribution Framing?

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 Contribution Framing?

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

Is the Amj Contribution Framing 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇