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Amr Methods

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franklee16
amr-methods

Use when choosing and applying a theory-construction method for an Academy of Management Review (AMR) manuscript — building constructs, defining their domain, specifying relationships, articulating the underlying logic/mechanisms, and setting boundary conditions. This is the THEORY-CONSTRUCTION craft, NOT empirical method; AMR publishes no datasets, no measures, and no statistical tests.

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamr-methods
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 Methods 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-methods
  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-methods .claude/skills/amr-methods
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amr Methods 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 Methods 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 Methods 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.

Theory-Construction Method (amr-methods)

AMR publishes NO empirical data. There are no samples, no measures, no estimation, and no results. "Method" here means the craft of building theory: how you construct constructs, derive relationships, ground mechanisms, and bound the theory. If your project needs data to make its point, it belongs at AMJ / ASQ / SMJ.

When to trigger

  • Constructs and propositions exist but the way they were built feels ad hoc
  • A reviewer would ask "where did this construct's domain come from?"
  • The theory needs a more disciplined construction approach than intuition
  • You need to choose among theory-building styles for your puzzle

Choosing a theory-construction approach

ApproachWhen it fitsCore moves
Conceptual elaborationExtending a known theory to a new domainCarry over the core logic; re-specify constructs for the new setting; add boundary conditions
Analogical / metaphorical transferA construct from another field illuminates a management phenomenonMap source → target carefully; theorize where the analogy holds and breaks
Typology constructionThe phenomenon has distinct, theoretically meaningful typesDerive dimensions from theory (not data); ensure types are mutually exclusive and jointly exhaustive; theorize transitions (e.g., Oliver's strategic-responses typology, AMR 1991, DOI 10.5465/amr.1991.4279002)
Process theorizingThe phenomenon is a sequence/becoming, not a variance relationshipSpecify stages, triggers, feedback loops, and timing; theorize the engine that drives movement
Construct re-specificationAn existing construct conflates distinct phenomenaSplit or merge; re-define the domain; re-derive downstream relationships
Cross-level theorizingThe action spans individual ↔ collectiveSpecify emergence (bottom-up) and top-down effects; avoid level confusion

Building each component well

  • Constructs: a good construct is necessary (the theory cannot be stated without it), distinct (not a synonym), and has a clear domain and scope conditions (Suddaby's construct-clarity criteria, AMR 2010, DOI 10.5465/amr.2010.0419). Define what it excludes, not only what it includes. Construct domain is the conceptual analog of avoiding a contaminated sample — but done in argument, not data cleaning.
  • Relationships: derive, don't assert. Each link should follow from the constructs and an explicit mechanism. Specify form (causal / recursive / moderating / mediating / constitutive).
  • Mechanisms: name the engine — the why that makes theory explanatory rather than descriptive (Whetten, AMR 1989, DOI 10.5465/amr.1989.4308371). Prefer mechanisms with micro-foundations — actors doing things for stated reasons — over black-box "is associated with."
  • Boundary conditions: state the contextual, temporal, and level limits. Strong theory specifies its own scope; this is contribution, not hedging.
  • Falsifiability: design propositions to be refutable in principle (AMR's scope demands "testable knowledge-based claims"). This is the AMR analog of identification rigor at AMJ — credibility rests on tight, falsifiable logic, not on a clean estimate.

Checklist

  • A named construction approach (or explicit hybrid) is used, not intuition alone
  • Each construct is necessary, distinct, and domain-bounded
  • Each relationship is derived from constructs + mechanism, not asserted
  • Mechanisms are specified with micro-foundations where possible
  • Typologies (if used) are mutually exclusive and jointly exhaustive, derived from theory
  • Process theories specify stages, triggers, feedback, and timing
  • Boundary conditions are stated as part of the theory
  • No empirical apparatus has crept in (no samples, measures, tests)

Anti-patterns

  • Treating "method" as data collection — there is none at AMR
  • A typology whose dimensions came from convenience, not theory
  • A process "model" that is really a static box-and-arrow diagram with no engine
  • Borrowing a construct from another field without theorizing where the analogy fails
  • Cross-level claims that confuse the level of theory with the level of the mechanism
  • Asserting relationships and back-filling a mechanism afterward

Output format

【Construction approach】elaboration / analogy / typology / process / re-specification / cross-level
【Constructs built】[name + how its domain was derived]
【Mechanism basis】micro-foundations / structural / cognitive / ...
【Boundary conditions】contextual / temporal / level
【Empirical apparatus present?】must be: none
【Next step】amr-data-analysis (stress-test the logic)

Frequently asked questions

What does the Amr Methods AI skill do?

Use when choosing and applying a theory-construction method for an Academy of Management Review (AMR) manuscript — building constructs, defining their domain, specifying relationships, articulating the underlying logic/mechanisms, and setting boundary conditions. This is the THEORY-CONSTRUCTION craft, NOT empirical method; AMR publishes no datasets, no measures, and no statistical tests.

Why use Amr Methods on TypingMind?

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

Which AI models can use Amr Methods?

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 Methods?

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

Is the Amr Methods 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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