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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill amr-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 "t
- The empirical-analog reframe (keep the folder, change the content)
- When to trigger
- The four logic tests
- 1. Premise-to-conclusion check (per proposition)
- 2. Thought experiment / counterfactual
- 3. Alternative-explanation audit
- 4. Disconfirming-case search
- Internal-coherence checks across the whole theory
- Checklist
- Anti-patterns
- Output format
What does the amr-data-analysis 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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill amr-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.