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Get Started Free →Use when designing tables and figures for a Comparative Political Studies (CPS) manuscript so each exhibit is self-contained, comparative-legible, and SAGE-ready. Improves the exhibits; it does not run the analysis or set the data policy.
.claude/skills/brycewang-stanford-cps-tables-figures/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -39% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 70% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 16% | 0% |
CPS exhibits must carry the comparative argument on their own. A reviewer should grasp the cross-case or over-time story from the figure and its caption without hunting through the text. Because references, tables, and figures do not count toward the CPS word limit, exhibits are where you spend space — but only if each one earns its place.
event-study plot around a reform, an RD plot, or a marginal-effects/predicted-probability plot — not a wall of regression coefficients.
push the full grid to the appendix.
exploits (so the reader sees the leverage).
error bars are (CI level). For comparative panels, state the countries and the period.
raw coefficients a reader must decode.
regime type, or the key covariate) rather than alphabetically.
Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-supplement drift). Full map: execution-with-mcp. CPS is comparative politics — cross-national and sub-national designs; emphasize identification and clustered / multiway inference.
etable (multi-model columns) or did_summary_to_latex straight from theresult_id.
plot_from_result / enhanced_event_study_plot / event_study_table —axis units and the SE/clustering note baked in.
interpretable units.
See a full fitted-result → exhibit chain in the JF execution walkthrough.
【Main figure】what it shows + why it is the finding
【Main table】headline spec + which robustness columns; rest → appendix
【Comparative descriptive】the exhibit showing cross-case/over-time leverage
【Self-contained?】captions/notes give estimator, FE, clustering, N, CI [Y/N]
【Production】grayscale-safe + vector/high-res [Y/N]
【Next】cps-writing-style../../resources/code/ — table/figure generation scripts to adapt../../resources/external_tools.md — coefplot / ggplot / marginaleffects and mapping packages| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 20,134 | 15,575 | -23% | 1 | 1 | 0% | 2,353 | 2,841 | +21% | 0 | 0 | — |
case-02 | fail→pass | 32,328 | 12,290 | -62% | 1 | 1 | 0% | 5,322 | 3,269 | -39% | 0 | 0 | — |
case-03 | fail→pass | 18,906 | 12,752 | -33% | 1 | 1 | 0% | 2,263 | 3,292 | +45% | 0 | 0 | — |
case-04 | pass→pass | 22,220 | 14,021 | -37% | 1 | 1 | 0% | 2,375 | 2,474 | +4% | 0 | 0 | — |
case-05 | pass→pass | 16,372 | 15,527 | -5% | 1 | 1 | 0% | 2,340 | 2,886 | +23% | 0 | 0 | — |
case-06 | pass→pass | 20,161 | 19,454 | -4% | 1 | 1 | 0% | 2,439 | 3,458 | +42% | 0 | 0 | — |
case-07 | pass→pass | 19,837 | 14,772 | -26% | 1 | 1 | 0% | 2,382 | 2,768 | +16% | 0 | 0 | — |
case-08 | pass→pass | 17,430 | 16,215 | -7% | 1 | 1 | 0% | 2,230 | 3,003 | +35% | 0 | 0 | — |
case-09 | pass→pass | 24,382 | 14,304 | -41% | 1 | 1 | 0% | 1,898 | 2,648 | +40% | 0 | 0 | — |
case-10 | pass→pass | 21,851 | 9,877 | -55% | 1 | 1 | 0% | 2,120 | 2,826 | +33% | 0 | 0 | — |
case-11 | pass→pass | 22,623 | 16,501 | -27% | 1 | 1 | 0% | 1,988 | 2,774 | +40% | 0 | 0 | — |
case-12 | pass→pass | 15,700 | 11,394 | -27% | 1 | 1 | 0% | 1,612 | 2,668 | +66% | 0 | 0 | — |
case-13 | pass→pass | 22,071 | 10,400 | -53% | 1 | 1 | 0% | 2,200 | 2,807 | +28% | 0 | 0 | — |
case-14 | pass→pass | 16,582 | 18,641 | +12% | 1 | 1 | 0% | 1,855 | 2,764 | +49% | 0 | 0 | — |
case-15 | fail→pass | 18,095 | 15,909 | -12% | 1 | 1 | 0% | 1,610 | 2,735 | +70% | 0 | 0 | — |
case-16 | fail→pass | 22,032 | 11,531 | -48% | 1 | 1 | 0% | 2,260 | 2,380 | +5% | 0 | 0 | — |
case-17 | pass→pass | 15,384 | 6,793 | -56% | 1 | 1 | 0% | 1,759 | 2,256 | +28% | 0 | 0 | — |
case-18 | fail→pass | 19,479 | 13,656 | -30% | 1 | 1 | 0% | 2,254 | 2,606 | +16% | 0 | 0 | — |
case-19 | fail→pass | 21,705 | 14,819 | -32% | 1 | 1 | 0% | 2,593 | 2,513 | -3% | 0 | 0 | — |
case-20 | fail→fail | 26,842 | 22,197 | -17% | 1 | 1 | 0% | 2,780 | 3,376 | +21% | 0 | 0 | — |
case-21 | pass→pass | 19,364 | 16,987 | -12% | 1 | 1 | 0% | 1,886 | 2,845 | +51% | 0 | 0 | — |
case-22 | pass→pass | 15,856 | 13,986 | -12% | 1 | 1 | 0% | 2,188 | 3,480 | +59% | 0 | 0 | — |
case-23 | pass→pass | 19,164 | 23,127 | +21% | 1 | 1 | 0% | 2,488 | 3,664 | +47% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +26 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.