Can I use this to generate figures for research papers?

Absolutely. The skill includes specific guidance on exporting to high-resolution vector formats like PDF and SVG with appropriate DPI settings for print publications.

How does it handle complex multi-plot layouts?

It provides advanced templates for subplots, constrained layouts, and GridSpec, allowing you to create sophisticated multi-panel figures without overlapping elements.

Does this skill support the Matplotlib Object-Oriented API?

Yes, it explicitly recommends and provides implementation patterns for the Object-Oriented interface over the state-based pyplot interface for better maintainability and control.

Are the generated plots accessible and colorblind-friendly?

Yes, the skill follows best practices by recommending perceptually uniform and colorblind-friendly colormaps like viridis and cividis, as well as using hatching patterns for categorical data.

Matplotlib Visualization

Name: Matplotlib Visualization
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Generates publication-quality charts and scientific visualizations using Python's foundational plotting library.

About

This skill empowers Claude to create high-quality static, animated, and interactive visualizations using Matplotlib. It provides expert guidance on both the pyplot interface and the recommended object-oriented API, ensuring the creation of professional-grade figures for scientific research, data analysis, and technical reports. By leveraging best practices for layout management, color mapping, and accessibility, users can generate everything from simple line charts to complex multi-panel 3D plots with precise control over every visual element.

Key Features

1 GitHub stars
Automated styling with built-in sheets and colorblind-friendly colormaps
Advanced layout control using subplots, subplot_mosaic, and GridSpec
Comprehensive plot types including line, scatter, bar, histograms, and heatmaps
Publication-ready export options for PNG, PDF, and SVG formats
Expert support for 3D visualizations and interactive animations

Use Cases

Building automated visualization pipelines for data reporting dashboards
Performing exploratory data analysis with statistical distribution plotting
Creating professional figures for scientific journals and research papers

About

Key Features

1 GitHub stars
Automated styling with built-in sheets and colorblind-friendly colormaps
Advanced layout control using subplots, subplot_mosaic, and GridSpec
Comprehensive plot types including line, scatter, bar, histograms, and heatmaps
Publication-ready export options for PNG, PDF, and SVG formats
Expert support for 3D visualizations and interactive animations

Use Cases

Building automated visualization pipelines for data reporting dashboards
Performing exploratory data analysis with statistical distribution plotting
Creating professional figures for scientific journals and research papers