io-data-analysis
Use when executing and reporting the empirical analysis (or the formal-model results) for an International Organization (IO) manuscript so it survives expert double-blind IR review and IO's pre-publication verification. The IO editorial staff re-run quantitative analyses and check formal proofs before final acceptance, and IR data raise distinctive estimation problems (dyadic dependence, selection into treaties/alliances/conflict, gravity structure). Guides analysis and reporting; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill io-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.
# Data Analysis (io-data-analysis) Two facts shape how you analyze for IO. First, IO publishes **international-relations** work, so the estimation problems are IR-specific — dyads are not independent, states select into treaties and wars, trade follows a gravity structure, and many "variables" are estimated constructs. Second, IO's editorial staff later **re-run your quantitative analyses and verify your formal proofs before final acceptance** (see `io-transparency-and-data-policy`). This skill covers execution and reporting; identification choices live in `io-research-design`. ## When to trigger - Estimating the main international effect and supporting analyses; writing the results section - A referee asked for robustness, heterogeneity by issue area, or an alternative estimator - Deriving and presenting the results/comparative statics of a formal model - Separating preregistered from exploratory analyses on a foreign-policy experiment ## IR-specific estimation concerns - **Dyadic and network dependence.** Directed/undirected dyads share members, so observations are not independent. Use two-way or multiway clustering, dyadic-robust SEs, or latent-space/AME network models rather th
- When to trigger
- IR-specific estimation concerns
- Reporting standards IO referees expect
- Formal-model results
- Verification-readiness (engineer it during analysis)
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
- Supplementary resources
What does the io-data-analysis skill do?
Use when executing and reporting the empirical analysis (or the formal-model results) for an International Organization (IO) manuscript so it survives expert double-blind IR review and IO's pre-publication verification. The IO editorial staff re-run quantitative analyses and check formal proofs before final acceptance, and IR data raise distinctive estimation problems (dyadic dependence, selection into treaties/alliances/conflict, gravity structure). Guides analysis and reporting; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill io-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.