jop-data-analysis
Use for analysis-stage decisions on a The Journal of Politics (JOP) manuscript — uncertainty, robustness, and reporting norms — written so the work is reproducible from line one. JOP makes acceptance contingent on replicability and a JOP replication analyst re-runs the code, so every reported number must come from a script. Guides analysis; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jop-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 (jop-data-analysis) At JOP, analysis and **reproducibility are the same task**: acceptance is **contingent on replicability**, and a **JOP replication analyst** re-runs your code at conditional acceptance. Write the analysis so that every number in the paper is **regenerated by a script** — and reported with honest uncertainty within the **page budget**. ## When to trigger - Setting up the estimation/analysis pipeline - Deciding which robustness checks belong in the main text vs the Online Appendix - A reviewer asked for additional specifications, uncertainty, or sensitivity - Preparing numbers that must match the deposited replication package exactly ## Analysis norms - **Report uncertainty**, not just point estimates: CIs, SEs (clustered appropriately), and substantive effect sizes a general reader can interpret. - **Specification transparency**: show the primary specification clearly; relegate the grid of alternatives to the Online Appendix, but reference it. - **Robustness that targets the threat**: each check should answer a specific objection (confounding, functional form, sample, measurement), not pad the count. - **Multiple comparisons**: adjust or pre-speci
- When to trigger
- Analysis norms
- Reproducible-from-line-one (the JOP analyst will re-run this)
- Fit the analysis to the page budget
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- What a JOP analysis referee is looking for
- Worked micro-example (illustrative figures)
- Referee pushback patterns and the JOP fix
- Output format
- Supplementary resources
What does the jop-data-analysis skill do?
Use for analysis-stage decisions on a The Journal of Politics (JOP) manuscript — uncertainty, robustness, and reporting norms — written so the work is reproducible from line one. JOP makes acceptance contingent on replicability and a JOP replication analyst re-runs the code, so every reported number must come from a script. Guides analysis; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jop-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.