Agent skill · data analytics · anthropics
data-visualization
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Why this skill is useful
Provides specific Python code patterns and design principles for effective data visualization that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, seaborn installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
Data Visualization Skill Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations. Chart Selection Guide Choose by Data Relationship What You're Showing Best Chart Alternatives --- --- --- Trend over time Line chart Area chart (if showing cumulative or composition) Comparison across categories Vertical bar chart Horizontal bar (many categories), lollipop chart Ranking Horizontal bar chart Dot plot, slope chart (comparing two periods) Part-to-whole composition Stacked bar chart Treemap (hierarchical), waffle chart Composition over time Stacked area chart 100% stacked bar (for proportion focus) Distribution Histogram Box plot (comparing groups), violin plot, strip plot Correlation (2 variables) Scatter plot Bubble chart (add 3rd variable as size) Correlation (many variables) Heatmap (correlation matrix) Pair plot Geographic patterns Choropleth map Bubble map, hex map Flow / process Sankey diagram Funnel chart (sequential stages) Relationship network Network graph Chord diagram Performance vs. target Bullet chart Gauge (single KPI only) Multiple KPIs at once Small multiples Dashboard with separate charts When NOT to Use Certain Charts Pie charts: Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead. 3D charts: Never. They distort perception and add no information. Dual-axis charts: Use cautiously. They can mislead by implying correlation. Clearly label both axes if used. Stacked bar (many categories): Hard to compare middle segments. Use small multiples or grouped bars instead. Donut charts: Slightly better than pie charts but same fundamental issues. Use for single KPI display at most. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/data-visualization