Matplotlib API patterns for creating publication-quality visualizations. Use when /ds:eda needs distribution plots, correlation heatmaps, or relationship visualizations, or when /ds:experiment needs result plots (learning curves, confusion matrices, forecast visualizations). For standard ML diagnostic plots use scikit-learn display utilities; for statsmodels diagnostic plots use statsmodels built-in plotting; for quick statistical plots prefer seaborn.
How to install matplotlib?
andikarachman/data-science-plugin/matplotlib
$npx -y skills add andikarachman/data-science-plugin --skill matplotlibInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/andikarachman/data-science-plugin" --skill "andikarachman/data-science-plugin/matplotlib"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/andikarachman/data-science-plugin" that are relevant to the current task. Run `npx skills add "https://github.com/andikarachman/data-science-plugin"` and select the relevant skills, then follow their instructions.
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