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Amr Tables Figures

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franklee16
amr-tables-figures

Use when building the conceptual exhibits of an Academy of Management Review (AMR) manuscript — process models, 2x2 typologies, multi-level frameworks, and propositions tables that carry theoretical work. These are CONCEPTUAL figures, NOT data plots; AMR has no charts of results because it has no data.

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamr-tables-figures
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 Tables Figures 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-tables-figures
  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-tables-figures .claude/skills/amr-tables-figures
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amr Tables Figures 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 Tables Figures 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 Tables Figures 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.

Conceptual Figures & Propositions Tables (amr-tables-figures)

AMR exhibits do theoretical work. There are no scatterplots, bar charts, or regression tables — there is no data. Every figure must be readable directly off the theory: each box is a defined construct, each arrow is a stated proposition or mechanism.

When to trigger

  • The theory is built but has no visual model, or the model is decorative
  • Your figure is boxes and arrows with no mechanism behind the arrows
  • You have many propositions and the reader cannot see their structure
  • A typology is asserted in prose but never shown as a grid

The exhibit toolkit

ExhibitUse it whenIt must show
Process modelThe theory is a sequence / becomingStages, triggers, feedback loops, timing — and the engine that drives movement
2x2 (or NxM) typologyThe phenomenon has theoretically distinct typesTwo theory-derived dimensions; mutually exclusive, jointly exhaustive cells; ideally theorized transitions between cells (cf. Oliver's antecedent-conditions × responses logic, AMR 1991, DOI 10.5465/amr.1991.4279002)
Multi-level frameworkThe action spans individual ↔ collectiveLevels as horizontal bands; emergence (bottom-up) and top-down arrows labeled
Variance/relational modelCore constructs and their specified linksConstructs as nodes; arrows labeled with proposition numbers and form (causal/moderating/mediating)
Propositions tableThere are many propositionsPn

Design rules (what reviewers check)

  • Every box is a defined construct. No box should appear that was not defined in the text.
  • Every arrow is earned. Each arrow corresponds to a proposition or a stated mechanism; label it (e.g., "P3") so the figure and text are in lock-step.
  • The mechanism is visible or cited. A bare arrow with no associated mechanism is decoration. If the space is tight, label the arrow with the proposition number whose mechanism the text supplies.
  • The figure adds, not repeats. It should let a reader grasp the structure of the argument at a glance — not merely re-list the propositions.
  • Levels and time are explicit when the theory is multi-level or processual.
  • Consistency: construct labels in the figure match the text exactly; numbering matches.

The empirical-exhibit reframe (what each AMR exhibit replaces)

Empirical sibling exhibit (AMJ/ASQ/SMJ)AMR conceptual replacement
Table 1: descriptive statistics / correlationsConstruct-definition table (definitions + scope conditions)
Regression / coefficient tablePropositions table (P1...Pn)
Marginal-effects / interaction plotConceptual model figure with moderator arrows
Event-study / time-series plotProcess model with stages and feedback loops

If any exhibit reports a number estimated from data, the manuscript is a misfiled empirical paper. A construct-definition table also supports construct clarity (Suddaby, AMR 2010, DOI 10.5465/amr.2010.0419).

Propositions table template

| #  | Proposition (constructs + form)                  | Mechanism (why)         | Boundary condition |
|----|--------------------------------------------------|-------------------------|--------------------|
| P1 | The stronger A, the more likely B                | M1: ...                 | holds when C       |
| P2 | A's effect on B is amplified by D                | M2: ...                 | weakens when ...   |

Checklist

  • Every box/node is a construct defined in the text
  • Every arrow maps to a proposition or a stated mechanism, and is labeled
  • The figure shows argument structure, not just a restatement
  • Typologies use theory-derived dimensions; cells are MECE; transitions theorized if relevant
  • Process models show stages, triggers, feedback, timing, and the driving engine
  • Multi-level frameworks label emergence and top-down effects and the levels
  • A propositions table accompanies any paper with more than ~4 propositions
  • Figure labels and numbering match the text exactly
  • Resolution / format meet AOM submission requirements (verify current guidelines)

Anti-patterns

  • A "model" figure of boxes and arrows with no theoretical mechanism behind the arrows
  • A figure introducing constructs that never appear in the text
  • A typology whose dimensions were chosen for tidiness, not derived from theory
  • Arrows with no direction or no form (is it causal? moderating? nobody can tell)
  • A figure that simply re-renders the propositions list, adding nothing
  • Decorative clip-art / gradients that obscure the logical structure

Output format

【Exhibit type】process / typology / multi-level / relational + propositions table
【Constructs shown】all defined in text? yes/no
【Arrows】each maps to Pn or mechanism? yes/no
【Adds beyond text?】shows structure / merely repeats (fix)
【Format check】meets AOM figure guidelines (verify)
【Next step】amr-writing-style

Frequently asked questions

What does the Amr Tables Figures AI skill do?

Use when building the conceptual exhibits of an Academy of Management Review (AMR) manuscript — process models, 2x2 typologies, multi-level frameworks, and propositions tables that carry theoretical work. These are CONCEPTUAL figures, NOT data plots; AMR has no charts of results because it has no data.

Why use Amr Tables Figures on TypingMind?

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

Which AI models can use Amr Tables Figures?

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 Tables Figures?

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

Is the Amr Tables Figures 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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