sf-data-analysis
Use when executing and reporting the analysis for a Social Forces (SF) manuscript so it survives expert, double-anonymized review — honest uncertainty, robustness, and triangulation appropriate to quantitative, demographic, network, or computational work. Guides analysis norms; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sf-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 (sf-data-analysis) Social Forces built its standing on **methodological rigor**, and its reviewers are sophisticated. The analysis must report uncertainty honestly, probe robustness that could actually break the result, and stay reproducible. This skill covers execution and reporting norms; design decisions live in `sf-research-design`. Keep an eye on the exhibit budget — results must fit within **10 tables and figure panels** (see `sf-tables-figures`). ## When to trigger - Running main and supporting analyses; building the results section - A reviewer asked for robustness, heterogeneity, or alternative specifications - Reconciling confirmatory vs. exploratory analyses - Making the analysis reproducible before drafting the data availability statement ## Analysis norms SF expects 1. **Report uncertainty honestly.** Confidence/credible intervals, not just stars; the magnitude and substantive meaning of the estimate, not just its significance. 2. **Robustness that probes, not decorates.** Show specifications that could *break* the result (alternative measures, samples, estimators, fixed effects), and say what you learn. 3. **Heterogeneity with discipline.** Pre-specify
- When to trigger
- Analysis norms SF expects
- Demographic / computational / network specifics
- Reproducibility while you work (not at the end)
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Evidence pass for Social Forces
- Output format
- Supplementary resources
What does the sf-data-analysis skill do?
Use when executing and reporting the analysis for a Social Forces (SF) manuscript so it survives expert, double-anonymized review — honest uncertainty, robustness, and triangulation appropriate to quantitative, demographic, network, or computational work. Guides analysis norms; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sf-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.