Data visualization for Python: Matplotlib, Seaborn, Plotly, Altair, hvPlot/HoloViz, and Bokeh. Use when creating exploratory charts, interactive dashboards, publication-quality figures, or choosing the right library for your data and audience.
Quickly install data-science-visualization AI skill to your development environment via command line
Source: legout/data-platform-agent-skills.
Use this skill for creating effective visualizations: choosing the right library, chart type, and interactivity level for your data and audience.
| Library | Best For | Interactivity | Learning Curve |
| Matplotlib | Publication-quality static plots, fine control | Static | Moderate | | Seaborn | Statistical visualization, quick EDA | Static | Easy | | Plotly | Interactive web charts, dashboards | High | Easy | | Altair | Declarative statistical charts, large datasets | Medium | Easy |
Data visualization for Python: Matplotlib, Seaborn, Plotly, Altair, hvPlot/HoloViz, and Bokeh. Use when creating exploratory charts, interactive dashboards, publication-quality figures, or choosing the right library for your data and audience. Source: legout/data-platform-agent-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/legout/data-platform-agent-skills --skill data-science-visualizationData visualization for Python: Matplotlib, Seaborn, Plotly, Altair, hvPlot/HoloViz, and Bokeh. Use when creating exploratory charts, interactive dashboards, publication-quality figures, or choosing the right library for your data and audience. Source: legout/data-platform-agent-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/legout/data-platform-agent-skills --skill data-science-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/legout/data-platform-agent-skills