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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.

Install / Use

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill amr-data-analysis

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

88/100

Supported Platforms

Universal

Our assessment of amr-data-analysis

amr-data-analysis scores 88/100 on our quality scale, 261st of 505 Data & Analytics skills we index.

Its SKILL.md is 5.9 KB long, well organised into 12 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

With 1,158 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
17/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 19 days ago, so amr-data-analysis is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

amr-data-analysis compared with similar skills

All 4 of these similar skills score higher than amr-data-analysis; compare them before choosing.

SkillScoreStarsUpdatedFormat
amr-data-analysis (this skill)by brycewang-stanford881.2k19d agoSKILL.md
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pptxby anthropics100177.9k11d agoSKILL.md
designby nextlevelbuilder100130.2k12d agoSKILL.md
ui-ux-pro-maxby nextlevelbuilder100130.2k12d agoSKILL.md

Frequently asked questions

How do I install amr-data-analysis?
Run npx skills add brycewang-stanford/Awesome-Journal-Skills --skill amr-data-analysis. The install tabs above show the steps for each supported agent.
Which AI agents does amr-data-analysis work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is amr-data-analysis safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is amr-data-analysis still maintained?
The repository was last updated 19 days ago, so amr-data-analysis is actively maintained.

name: amr-data-analysis description: 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.

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 specifications | Internal consistency + counterfactual probes on premises | | Ruling out confounders | Engaging 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

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryData
Updated19d ago
Forks153

Languages

Stata

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions