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.
Quickly install data-visualization AI skill to your development environment via command line
Source: anthropics/knowledge-work-plugins.
Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.
| 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) |
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. Source: anthropics/knowledge-work-plugins.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill data-visualizationCreate 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. Source: anthropics/knowledge-work-plugins.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/anthropics/knowledge-work-plugins --skill data-visualization Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/anthropics/knowledge-work-plugins