Use when running and reporting the analyses for a Comparative Political Studies (CPS) manuscript — estimation, uncertainty, robustness, and multi-method triangulation on comparative data. Sets analysis norms; it does not choose the identification strategy (see cps-research-design).
SkillScore86Maintained
Automatic
◆Data Analyst pack
Last verified 9/1/2026
What it changes
Passed 19 of 22 test cases with the skill, and 14 of 22 without it.
Without skill64%
With skill86%
Checks it passed that the model alone missed
Concludes the evaluation with 【Next】cps-tables-figures
Lists cps-tables-figures as the downstream workflow link
Includes a verification entry for 【Reproducible?】 in the output summary
and 7 more in the full report
5 fixed14 passed either way3 still failing0 broke
Pass-rate lift
+23 pp
Tokens
+31%
Agent turns
0%
Response time
-10%
Model gemini-3.6-flashMeasured 9/1/2026v119 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
I have finished estimating our panel models on foreign direct investment and labor rights across 32 developing countries over 15 years. Could you review our empirical results and run the comparative data evaluation? Please structure your output into these specific blocks: headline result with point estimates, inference choices and clustering approach, measurement details and alternative variable codings, a failure-mode audit checking potential comparative hazards (like cross-regime equivalence, sample selection, persistence, leverage, and subgroup multiplicity), targeted robustness checks, theoretical heterogeneity findings with multiple-testing adjustments, replication verification, and the next steps for workflow downstream.
What was checked, without and with the skill
Checked
Without skill
With skill
Formats evaluation output using bracketed section headers including 【Headline result】 and 【Inference】
Missed
Passed
Includes a verification entry for 【Reproducible?】 in the output summary
Missed
Passed
Concludes the evaluation with 【Next】cps-tables-figures
Missed
Passed
case-01 · gemini-3.6-flash · 9/1/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md109 linessingle file
Data Analysis (cps-data-analysis)
Once the design is fixed (cps-research-design), this skill governs how the analyses are run and
reported so a CPS reviewer trusts them. Comparative data bring distinctive hazards: few clusters
(countries), cross-national measurement error, missing data that differ by regime, and the temptation to
over-read a panel correlation as causal. The standard is modern, transparent, and replication-ready.
When to trigger
Estimating the main results, robustness, and heterogeneity
A reviewer questioned standard errors, specification, measurement, or fragility of the result
Deciding what goes in the main text vs. the supplementary/online appendix
Triangulating quantitative estimates with case evidence
Analysis priorities (in order)
Main estimate that matches the design. The headline specification should be the one the
identification argument justifies — not the one with the biggest coefficient or most stars.
Honest uncertainty. Cluster at the assignment level (usually country / country-year); with few
countries use wild-cluster bootstrap or randomization inference. Report CIs, not just stars.
Measurement transparency. Name the source and coding of each comparative variable (e.g., V-Dem,
Polity, CSES, Manifesto Project); show robustness to alternative codings of the key construct.
Robustness as a coherent story. Alternative specifications, samples, codings, and estimators that
probe the threats named in the design — not a scattershot table of every variant.
Heterogeneity by theory. Subgroups/scope conditions pre-specified by the mechanism
(cps-theory-building), not data-mined; adjust for multiple comparisons.
Mechanism evidence. Tie the quantitative result to the mechanism — mediation cautiously, or case
evidence in a multi-method design.
Comparative-data hazards to address explicitly
Hazard
Symptom
Fix
Few clusters (countries)
over-rejection, tiny SEs
wild-cluster bootstrap / randomization inference
Cross-national measurement error
results flip across codings
show robustness to V-Dem/Polity/alt scales
Differential missingness
sample changes by regime type
report attrition; multiple imputation with caution
Use when executing and reporting the analysis for an American Journal of Political Science (AJPS) manuscript. AJPS will have a third-party verifier re-run your exact code against the numerical results in the main text before publication, so analyze reproducibly from the first line. Guides analysis and reporting norms; it does not fabricate results.