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Amr Literature Positioning

Community
franklee16
amr-literature-positioning

Use when you must identify which theoretical conversation an Academy of Management Review (AMR) manuscript enters and how it "challenges and extends" that conversation. Positions the argument within a literature; it does NOT summarize the field (AMR punishes review-essays) and does NOT build the new theory (that is amr-theory-development).

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamr-literature-positioning
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 Literature Positioning 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-literature-positioning
  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-literature-positioning .claude/skills/amr-literature-positioning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amr Literature Positioning 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 Literature Positioning 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 Literature Positioning 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.

Literature Positioning: Whose Conversation, and How You Challenge It (amr-literature-positioning)

When to trigger

  • You have a puzzle but cannot name the theory stream you are arguing with
  • Your literature section reads like a survey ("scholars have studied...")
  • You cite broadly but engage no single conversation deeply
  • A reader asks "so which theory are you extending, exactly?"

The principle: engage, don't summarize

AMR is a journal of conversations. Every accepted paper enters a specific theoretical conversation and moves it. The literature section is not a catalog of who-did-what — it is the set-up of the tension your theory will resolve. Two questions govern everything:

  1. Which conversation am I in? Pick the primary theoretical stream (e.g., the attention-based view; institutional logics; dynamic capabilities). A paper that is "in five conversations" is in none.
  2. How do I challenge AND extend it? AMR's hallmark phrase. You must do both: show a limit/contradiction in the existing theory (challenge) and offer the new theoretical machinery that resolves it (extend). Pure praise = no contribution; pure critique = no theory. AMR's scope explicitly welcomes work that "significantly challenges or clarifies existing theory."

As you characterize the conversation, define the constructs you build on or revise. Suddaby's AMR "Editor's Comments: Construct Clarity in Theories of Management and Organization" (2010, DOI 10.5465/amr.2010.0419) is the field standard: each construct needs a precise definition, scope conditions, and clear semantic relationships to neighbors. Positioning that leaves core constructs fuzzy invites the "this is a relabel" objection.

Positioning moves (pick the one your puzzle supports)

MoveWhat you do
Add a missing constructExisting theory lacks a concept needed to explain the phenomenon; you introduce and define it
Re-specify a relationshipA relationship treated as direct is really mediated / moderated / recursive
Bound a theoryYou theorize the conditions under which the existing theory holds vs. fails
Bridge two conversationsYou connect two streams that have ignored each other, generating new propositions
Reconceptualize a constructYou split, merge, or redefine a construct, dissolving a standing debate
Shift the levelA construct theorized at one level (individual) is re-theorized at another (collective) with new dynamics

Exemplar

Dyer & Singh's "The Relational View" (AMR 1998, DOI 10.5465/amr.1998.1255632) is model positioning: it names two dominant conversations (the resource-based view and the industry-structure view), shows both locate competitive advantage at the wrong unit, and opens new theoretical space (relational rents accruing to dyads/networks) — challenge AND extend, by argument, with no dataset. Cite the conversation in APA-style (AOM house style).

How to read the conversation (durable practice)

  • Read the most recent AMR/AMJ theoretical pieces on the construct; reviewers are usually drawn from this stream.
  • Track the conversation forward: what did the last 3–5 years add, and where did it stall?
  • Identify the assumption the conversation shares — challenging a shared assumption is often the strongest move.
  • Note the seminal anchor(s) the stream returns to; engage them directly, not just recent citations.

Checklist

  • One primary theoretical conversation is named and justified
  • The shared assumption / limit you target is stated explicitly
  • Both halves present: what you challenge AND what you extend
  • Recent (not only seminal) work in the stream is engaged
  • Secondary literatures are clearly subordinate, used only to set up the move
  • The section sets up a tension the theory will resolve — it does not merely report

Anti-patterns

  • A "literature review" that summarizes findings instead of building an argument
  • Breadth over depth: citing everyone, engaging no one
  • Citing only seminal works and ignoring the live, recent conversation
  • Critiquing existing theory with no constructive extension offered
  • Claiming a "gap" rather than a tension (see amr-topic-selection)
  • Positioning against a straw-man version of the existing theory

Output format

【Primary conversation】theory stream + seminal anchor
【Shared assumption / limit targeted】...
【Challenge】what existing theory cannot do
【Extend】the new theoretical machinery you will add
【Positioning move】add-construct / re-specify / bound / bridge / reconceptualize / shift-level
【Next step】amr-theory-development

Frequently asked questions

What does the Amr Literature Positioning AI skill do?

Use when you must identify which theoretical conversation an Academy of Management Review (AMR) manuscript enters and how it "challenges and extends" that conversation. Positions the argument within a literature; it does NOT summarize the field (AMR punishes review-essays) and does NOT build the new theory (that is amr-theory-development).

Why use Amr Literature Positioning on TypingMind?

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

Which AI models can use Amr Literature Positioning?

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 Literature Positioning?

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

Is the Amr Literature Positioning 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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