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amj-theory-development

Use when the theoretical argument and hypotheses are the bottleneck for an Academy of Management Journal (AMJ) manuscript — building a mechanism and deriving testable hypotheses a priori.

Install / Use

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill amj-theory-development

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Supported Platforms

Universal

Our assessment of amj-theory-development

amj-theory-development scores 85/100 on our quality scale, 2315th of 4,615 Development & Engineering skills we index.

Its SKILL.md is 5.0 KB long, well organised into 9 sections with 1 code example: a solid amount of guidance for an agent.

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

Substance
26/30
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 amj-theory-development 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.

amj-theory-development compared with similar skills

All 4 of these similar skills score higher than amj-theory-development; compare them before choosing.

SkillScoreStarsUpdatedFormat
amj-theory-development (this skill)by brycewang-stanford851.2k19d agoSKILL.md
ai-job-searchby MadsLorentzen10044.9k1d agoCLAUDE.md
claude-howtoby luongnv8910041.7k3d agoCLAUDE.md
algorithmic-artby anthropics100177.9k11d agoSKILL.md
pptxby anthropics100177.9k11d agoSKILL.md

Frequently asked questions

How do I install amj-theory-development?
Run npx skills add brycewang-stanford/Awesome-Journal-Skills --skill amj-theory-development. The install tabs above show the steps for each supported agent.
Which AI agents does amj-theory-development 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 amj-theory-development 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 amj-theory-development still maintained?
The repository was last updated 19 days ago, so amj-theory-development is actively maintained.

name: amj-theory-development description: Use when the theoretical argument and hypotheses are the bottleneck for an Academy of Management Journal (AMJ) manuscript — building a mechanism and deriving testable hypotheses a priori. Constructs the theory; it does not run the analysis (amj-data-analysis) or write the final contribution paragraph (amj-contribution-framing).

Theory Development & Hypotheses (amj-theory-development)

When to trigger

  • Hypotheses read as bald predictions ("A is positively related to B") with no mechanism
  • You have results and are tempted to write hypotheses around them (HARKing risk)
  • The argument leans on one borrowed citation rather than a developed logic
  • Mediators/moderators are present in the data but not theorized
  • A reviewer says "the theory is thin," "the logic is underdeveloped," or "why would this be true?"

The AMJ theory bar

AMJ does not publish atheoretical work — its mission is "to publish empirical research that tests, extends, or builds management theory." Every hypothesis must be derived from an articulated theoretical mechanism, written before the results are known. The argument should make a reader feel they understand why the effect occurs and under what conditions it strengthens or reverses.

For theory-building (typically qualitative) papers, AMJ does not expect a priori hypotheses; it expects a grounded model with emergent propositions. Eisenhardt and Graebner's AMJ guidance on building theory from cases is the canonical exemplar for how rich data become crisp constructs and propositions — do not force such a study into a hypothetico-deductive template.

Building a hypothesis (the mechanism chain)

For each hypothesis, write the explicit chain — do not skip steps:

  1. Antecedent — the predictor and why it matters in this context.
  2. Mechanism — the theoretical process linking antecedent to outcome (e.g., social exchange, signaling, attention allocation, identity threat, resource dependence). Name the theory and the process verb (motivates, signals, depletes, legitimates).
  3. Outcome — the dependent construct, defined at a stated level of analysis.
  4. Direction & form — sign, and whether linear, curvilinear, or threshold.
  5. Boundary — the moderator(s) and the theoretical reason the mechanism is stronger/weaker.

State the level of analysis explicitly (individual, team, firm) and keep the theory at the level where the mechanism operates; flag cross-level mediation as such.

Mediation and moderation done right

  • Mediation: theorize the process construct before testing it; do not infer the mechanism from a significant indirect effect alone. An indirect effect is evidence for a theorized mechanism, not a substitute for the theory.
  • Moderation: give a substantive reason the slope changes — "it depends on" is not a theory until you say why it depends.
  • Curvilinear: justify the turning point theoretically (two opposing forces), not just by a significant squared term.

Hypothesis hygiene

  • Number hypotheses and keep them to a defensible set (typically a focal effect plus a mechanism and one or two boundaries). Avoid a "kitchen sink" of ten weakly motivated predictions.
  • Each hypothesis must be falsifiable and tied to a specific test you will run.
  • Competing/alternative theoretical explanations should be named and addressed, not ignored.
  • A figure of the theoretical model (boxes and arrows) should map one-to-one to the hypotheses.

Checklist

  • [ ] Every hypothesis has an explicit mechanism, not just a predicted sign
  • [ ] The driving theory is named and its process clearly invoked (not a one-line citation)
  • [ ] Level of analysis is stated and consistent across argument and design
  • [ ] Mediators/moderators are theorized a priori with substantive reasons
  • [ ] Hypotheses were derived before analyzing the data (no HARKing)
  • [ ] A theoretical-model figure matches the hypotheses exactly
  • [ ] At least one rival explanation is named and engaged

Anti-patterns

  • HARKing: hypothesizing after results are known; reviewers detect it when every hypothesis is supported and none is surprising.
  • Bald predictions: "We hypothesize A is positively related to B" with no why.
  • Borrowed theory: invoking a famous theory by name without using its actual logic.
  • Mechanism by mediation: claiming a process exists only because the indirect effect is significant.
  • Kitchen-sink hypotheses: many predictions, each thinly argued, that no single theory ties together.
  • Boundary without reason: a moderator with no theoretical account of why the effect should differ.

Output format

【Focal theory】... (process verb: ...)
【H1 (focal effect)】antecedent → mechanism → outcome; direction/form; level
【H2 (mechanism/mediation)】...
【H3+ (boundary/moderation)】... reason the slope changes
【Rival explanation addressed】...
【HARKing check】hypotheses fixed before analysis? yes/no
【Model figure】matches hypotheses one-to-one? yes/no
【Next step】amj-literature-positioning, then amj-methods

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryDevelopment
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