data-visualization
Officialdata visualization
What it does
- 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.
- Use color purposefully: Color should encode data, not decorate
- Highlight the story: Use a bright accent color for the key insight; grey everything else
Derived from the skill's own SKILL.md documentation · extracted 2026-07-23
data-visualization FAQ
What does the data-visualization skill do?
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. 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.
How do I install data-visualization?
Run: npx -y skills add https://github.com/anthropics/knowledge-work-plugins --skill data-visualization --agent claude-code — the source lives at github.com/anthropics/knowledge-work-plugins.
Maintain data-visualization?
Let people know it's listed here — add the badge (live metrics, light/dark aware) or a plain link to your README or docs.
[](https://getagentictools.com/skills/anthropics-knowledge-work-plugins-data-visualization?ref=badge) npx agentictools info skills/anthropics-knowledge-work-plugins-data-visualization The second line is the CLI lookup for this page — handy in READMEs and docs.
