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Get Started Free →Generate publication-ready regression tables in LaTeX.
.claude/skills/brycewang-stanford-latex-tables/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 65% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 50% | 0% |
| case-22 | ✗→✓ | ▲ Improved | -55% | 0% |
This skill creates clean, publication-ready tables in LaTeX for regression results and summary statistics, using standard academic formatting.
Follow these steps to complete the task:
Before generating any code, ask the user:
Based on the context, generate LaTeX code that:
booktabs for clean horizontal rulesAfter generating output:
latex% ============================================ % Regression Table % ============================================ \begin{table}[htbp]\centering \caption{Effect of Treatment on Outcome} \label{tab:main_results} \begin{tabular}{lccc} \toprule & (1) & (2) & (3) \\ \midrule Treatment & 0.125*** & 0.118*** & 0.102** \\ & (0.041) & (0.039) & (0.046) \\ Controls & No & Yes & Yes \\ Fixed Effects & No & Yes & Yes \\ \midrule Observations & 2,145 & 2,145 & 2,145 \\ R-squared & 0.18 & 0.24 & 0.31 \\ \bottomrule \end{tabular} \begin{tablenotes} \small \item Notes: Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01. \end{tablenotes} \end{table}
booktabsthreeparttable (optional for notes)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 10,870 | 7,526 | -31% | 1 | 1 | 0% | 2,197 | 2,142 | -3% | 0 | 0 | — |
case-02 | fail→pass | 11,231 | 9,919 | -12% | 1 | 1 | 0% | 2,405 | 2,743 | +14% | 0 | 0 | — |
case-03 | fail→fail | 11,253 | 9,392 | -17% | 1 | 1 | 0% | 2,301 | 2,718 | +18% | 0 | 0 | — |
case-04 | fail→fail | 4,987 | 11,634 | +133% | 1 | 1 | 0% | 870 | 3,167 | +264% | 0 | 0 | — |
case-05 | fail→pass | 6,442 | 5,714 | -11% | 1 | 1 | 0% | 1,053 | 1,738 | +65% | 0 | 0 | — |
case-06 | pass→pass | 4,014 | 3,233 | -19% | 1 | 1 | 0% | 615 | 1,295 | +111% | 0 | 0 | — |
case-07 | pass→pass | 11,177 | 10,316 | -8% | 1 | 1 | 0% | 2,046 | 2,711 | +33% | 0 | 0 | — |
case-08 | pass→pass | 9,899 | 9,017 | -9% | 1 | 1 | 0% | 1,709 | 2,436 | +43% | 0 | 0 | — |
case-09 | pass→pass | 8,918 | 8,770 | -2% | 1 | 1 | 0% | 1,813 | 2,401 | +32% | 0 | 0 | — |
case-10 | pass→pass | 8,718 | 6,937 | -20% | 1 | 1 | 0% | 1,738 | 2,092 | +20% | 0 | 0 | — |
case-11 | pass→pass | 6,235 | 10,549 | +69% | 1 | 1 | 0% | 1,161 | 2,791 | +140% | 0 | 0 | — |
case-12 | pass→pass | 9,557 | 8,752 | -8% | 1 | 1 | 0% | 1,696 | 2,416 | +42% | 0 | 0 | — |
case-13 | fail→pass | 7,780 | 7,015 | -10% | 1 | 1 | 0% | 1,350 | 1,974 | +46% | 0 | 0 | — |
case-14 | fail→fail | 10,921 | 8,839 | -19% | 1 | 1 | 0% | 1,770 | 2,523 | +43% | 0 | 0 | — |
case-15 | pass→pass | 16,659 | 11,422 | -31% | 1 | 1 | 0% | 4,271 | 3,391 | -21% | 0 | 0 | — |
case-16 | fail→pass | 6,523 | 4,242 | -35% | 1 | 1 | 0% | 1,175 | 1,767 | +50% | 0 | 0 | — |
case-17 | pass→pass | 14,562 | 10,976 | -25% | 1 | 1 | 0% | 3,555 | 3,049 | -14% | 0 | 0 | — |
case-18 | pass→pass | 6,617 | 4,753 | -28% | 1 | 1 | 0% | 1,066 | 1,666 | +56% | 0 | 0 | — |
case-19 | pass→pass | 8,259 | 5,345 | -35% | 1 | 1 | 0% | 2,047 | 1,641 | -20% | 0 | 0 | — |
case-20 | pass→pass | 9,962 | 9,821 | -1% | 1 | 1 | 0% | 2,385 | 2,956 | +24% | 0 | 0 | — |
case-21 | pass→pass | 6,539 | 7,985 | +22% | 1 | 1 | 0% | 1,576 | 2,186 | +39% | 0 | 0 | — |
case-22 | fail→pass | 30,681 | 11,133 | -64% | 1 | 1 | 0% | 6,158 | 2,780 | -55% | 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. 22 cases were attempted. The headline lift of +23 percentage points is the difference between those two pass rates over the 22 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.