Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Generate insightful, publication-quality visualizations from complex datasets.
.claude/skills/data-visualization-expert/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-13 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 33% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | 47% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 57% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 37% | 0% |
<!--
#
#
-->
A dedicated skill for transforming raw data (CSV, JSON, Excel) into compelling visual narratives. Specializes in statistical and scientific plotting.
pd.read_csv()) and inspect columns/types.bash# Agent prompt: "Visualize the distribution of 'Age' vs 'Income' from customers.csv" # Triggers generation of `plot_age_income.py` using Seaborn scatterplot.
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 11,358 | 11,925 | +5% | 1 | 1 | 0% | 2,279 | 3,033 | +33% | 0 | 0 | — |
case-02 | pass→pass | 4,921 | 6,404 | +30% | 1 | 1 | 0% | 933 | 1,368 | +47% | 0 | 0 | — |
case-03 | pass→pass | 9,295 | 12,027 | +29% | 1 | 1 | 0% | 1,987 | 3,127 | +57% | 0 | 0 | — |
case-04 | pass→pass | 16,125 | 17,884 | +11% | 1 | 1 | 0% | 3,254 | 4,442 | +37% | 0 | 0 | — |
case-05 | pass→pass | 10,276 | 7,890 | -23% | 1 | 1 | 0% | 2,183 | 2,105 | -4% | 0 | 0 | — |
case-06 | fail→fail | 18,050 | 13,282 | -26% | 1 | 1 | 0% | 2,787 | 3,157 | +13% | 0 | 0 | — |
case-07 | pass→pass | 19,823 | 16,578 | -16% | 1 | 1 | 0% | 4,035 | 3,962 | -2% | 0 | 0 | — |
case-08 | pass→pass | 14,014 | 10,441 | -25% | 1 | 1 | 0% | 2,835 | 3,008 | +6% | 0 | 0 | — |
case-09 | pass→pass | 15,793 | 10,269 | -35% | 1 | 1 | 0% | 2,679 | 2,401 | -10% | 0 | 0 | — |
case-10 | pass→pass | 8,928 | 9,407 | +5% | 1 | 1 | 0% | 1,765 | 2,531 | +43% | 0 | 0 | — |
case-11 | pass→pass | 22,187 | 16,328 | -26% | 1 | 1 | 0% | 3,450 | 3,794 | +10% | 0 | 0 | — |
case-12 | pass→pass | 14,744 | 13,408 | -9% | 1 | 1 | 0% | 2,294 | 2,749 | +20% | 0 | 0 | — |
case-13 | fail→pass | 21,450 | 13,633 | -36% | 1 | 1 | 0% | 3,345 | 3,309 | -1% | 0 | 0 | — |
case-14 | pass→pass | 16,029 | 17,456 | +9% | 1 | 1 | 0% | 3,225 | 4,044 | +25% | 0 | 0 | — |
case-15 | pass→pass | 24,601 | 21,005 | -15% | 1 | 1 | 0% | 5,453 | 4,318 | -21% | 0 | 0 | — |
case-16 | pass→pass | 11,844 | 9,846 | -17% | 1 | 1 | 0% | 2,298 | 2,375 | +3% | 0 | 0 | — |
case-17 | pass→pass | 15,539 | 11,889 | -23% | 1 | 1 | 0% | 3,023 | 2,905 | -4% | 0 | 0 | — |
case-18 | fail→fail | 13,958 | 18,221 | +31% | 1 | 1 | 0% | 2,678 | 4,015 | +50% | 0 | 0 | — |
case-19 | fail→fail | 12,917 | 13,343 | +3% | 1 | 1 | 0% | 2,368 | 3,139 | +33% | 0 | 0 | — |
case-20 | pass→pass | 17,353 | 16,655 | -4% | 1 | 1 | 0% | 3,408 | 3,931 | +15% | 0 | 0 | — |
case-21 | pass→pass | 11,577 | 9,158 | -21% | 1 | 1 | 0% | 1,538 | 2,298 | +49% | 0 | 0 | — |
case-22 | pass→pass | 19,896 | 11,743 | -41% | 1 | 1 | 0% | 3,085 | 2,804 | -9% | 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 +5 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.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 7/26/2026 | — |
Other measured skills in the registry, with their headline benchmark lift.