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Amr Data Analysis

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franklee16
amr-data-analysis

Use when stress-testing the LOGIC of an Academy of Management Review (AMR) theory manuscript — checking logical coherence, running thought experiments and counterfactuals, addressing alternative explanations and disconfirming cases, and verifying each proposition follows from its argument. This is ARGUMENT DEVELOPMENT, NOT data analysis; AMR publishes no datasets, no statistics, and no empirical results.

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamr-data-analysis
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 Data Analysis 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-data-analysis
  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-data-analysis .claude/skills/amr-data-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amr Data Analysis 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 Data Analysis 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 Data Analysis 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.

Argument Development & Logic Check (amr-data-analysis)

AMR publishes NO empirical data. There is nothing to estimate, plot, or test. The "analysis" in an AMR paper is the analysis of the argument itself: does each proposition follow logically from the constructs and mechanisms? At AMR, logical soundness plays the role that statistical rigor plays at empirical journals.

The empirical-analog reframe (keep the folder, change the content)

This skill replaces an empirical "identification + robustness" stage. The mapping:

Empirical sibling (AMJ/ASQ/SMJ)AMR theory analog
Identification strategy (IV, DiD, RD, matching)Generative mechanism — the why (Whetten 1989, DOI 10.5465/amr.1989.4308371)
Robustness checks / alternative specificationsInternal consistency + counterfactual probes on premises
Ruling out confoundersEngaging and bettering the strongest rival theory
Replication package (data + code)Transparent reasoning — premises and derivations a reader can re-derive
"Estimates are significant and robust"Propositions are falsifiable in principle (AMR's "testable knowledge-based claims")

There is no instrument, no parallel-trends test, no placebo here; their presence signals a misfiled empirical paper.

When to trigger

  • Propositions are written but you are not sure they actually follow from the argument
  • The theory "feels right" but has not been adversarially tested
  • A reviewer would raise an alternative explanation you have not addressed
  • The argument chain has hidden leaps between premises

The four logic tests

Run every proposition through these before drafting.

1. Premise-to-conclusion check (per proposition)

For each Pn, write the chain explicitly: premise → premise → mechanism → conclusion. If any step is missing, the proposition is asserted, not derived. Use a Toulmin frame: claim / grounds / warrant / backing / rebuttal. The warrant (the mechanism that licenses the inference) is where most theory papers are thin.

2. Thought experiment / counterfactual

Manipulate the focal construct in your head and trace the consequence: "If construct X rose sharply while everything else held, what does the theory predict for Y, and is that prediction sensible?" Then run the counterfactual: "Under what condition would X move and Y not follow?" If the counterfactual is plausible and unexplained, you are missing a boundary condition (route back to amr-theory-development).

3. Alternative-explanation audit

For each proposition, name the strongest rival theoretical account of the same relationship. Then either (a) show why your mechanism is more complete/parsimonious, or (b) integrate the rival as a boundary condition. Ignoring rivals is the fastest path to a reject — reviewers are the rival theorists.

4. Disconfirming-case search

Actively look for a case where the proposition should fail. A theory that "explains everything" explains nothing. Either the disconfirming case is covered by a stated boundary condition, or the proposition needs to be narrowed.

Internal-coherence checks across the whole theory

  • Consistency: no two propositions contradict each other (unless the tension is the point and is theorized). Constructs mean the same thing throughout — no concept drift (a core Suddaby construct-clarity criterion, AMR 2010, DOI 10.5465/amr.2010.0419).
  • Non-circularity: a construct is not defined by its effects, then used to explain those effects.
  • Sufficiency: the constructs and mechanisms are enough to generate the propositions — nothing is smuggled in mid-argument.
  • Parsimony: every construct earns its place; drop any that does no logical work.

Exemplar: Oliver (AMR 1991, DOI 10.5465/amr.1991.4279002) "analyzes" by argument — deriving a typology and propositions from antecedent conditions and addressing why organizations might resist rather than conform (the rival expectation) — all logic, no data.

Checklist

  • Each proposition has an explicit premise → mechanism → conclusion chain
  • The warrant (mechanism) for each inference is stated, not assumed
  • A thought experiment has been run on each focal relationship
  • Counterfactuals are addressed by boundary conditions, not ignored
  • The strongest alternative explanation for each proposition is named and handled
  • A disconfirming case has been sought for each proposition
  • The theory is internally consistent, non-circular, sufficient, and parsimonious
  • No empirical evidence is invoked as proof (AMR has none)

Anti-patterns

  • Propositions presented as self-evident, with the argument left to the reader
  • Hand-waving the mechanism ("it stands to reason that...")
  • Defending the theory by asserting it would be "supported by data" — there are no data
  • Ignoring the obvious rival theory the reviewers hold
  • A theory that cannot be wrong: no boundary, no disconfirming case, no rebuttal addressed
  • Circular reasoning: defining a construct by the outcome it is meant to explain

Output format

【Per-proposition logic】P1: chain ok? / gap at warrant? ... Pn
【Thought experiments run】[focal construct → predicted consequence]
【Counterfactuals → boundary conditions】[...]
【Alternative explanations handled】[rival → resolution]
【Disconfirming cases】[case → covered by boundary / narrow proposition]
【Coherence】consistent / non-circular / sufficient / parsimonious : pass/fix
【Next step】amr-contribution-framing

Frequently asked questions

What does the Amr Data Analysis AI skill do?

Use when stress-testing the LOGIC of an Academy of Management Review (AMR) theory manuscript — checking logical coherence, running thought experiments and counterfactuals, addressing alternative explanations and disconfirming cases, and verifying each proposition follows from its argument. This is ARGUMENT DEVELOPMENT, NOT data analysis; AMR publishes no datasets, no statistics, and no empirical results.

Why use Amr Data Analysis on TypingMind?

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

Which AI models can use Amr Data Analysis?

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 Data Analysis?

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

Is the Amr Data Analysis 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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