Does it handle 3D plotting and complex layouts?

Yes, it provides implementation patterns for 3D surfaces, scatter plots, and complex multi-panel figures using advanced tools like GridSpec and mosaic layouts.

Can I use this for academic publications?

Absolutely. The skill includes specific workflows for exporting vector-based formats like PDF/SVG and high-DPI PNGs optimized for scientific journals.

Does this skill support the Object-Oriented interface?

Yes, it prioritizes the Object-Oriented API (Figure and Axes) as a best practice for production-grade code, while still supporting the pyplot interface for quick tasks.

What is the Matplotlib Claude Code Skill?

It is a specialized capability that helps Claude generate, debug, and customize high-quality scientific visualizations using the Matplotlib library in Python.

Matplotlib Scientific Visualization

Name: Matplotlib Scientific Visualization
Author: K-Dense-AI

byK-Dense-AI

•

3,718

Data Science & ML

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

About

This skill empowers Claude to create sophisticated scientific visualizations using Matplotlib, covering both the user-friendly pyplot interface and the robust object-oriented API. It provides structured guidance for designing complex multi-panel figures, 3D visualizations, and customized charts while adhering to scientific standards. Whether you are generating high-resolution figures for academic papers or exploring datasets, this skill ensures best practices in colormap selection, accessibility, and layout management for professional-grade results.

Key Features

Support for diverse plot types including line, scatter, bar, heatmaps, and 3D
Advanced layout control using subplots, subplot_mosaic, and GridSpec
Implementation of accessible and colorblind-friendly visualization standards
3,718 GitHub stars
Expert guidance for both Object-Oriented and pyplot Matplotlib interfaces
Publication-quality export settings for PNG, PDF, and SVG formats

Use Cases

Building custom visualization templates for consistent branding and styling
Automating the generation of data exploration plots during large-scale analysis
Creating professional figures and charts for scientific journals and research reports

About

Key Features

Support for diverse plot types including line, scatter, bar, heatmaps, and 3D
Advanced layout control using subplots, subplot_mosaic, and GridSpec
Implementation of accessible and colorblind-friendly visualization standards
3,718 GitHub stars
Expert guidance for both Object-Oriented and pyplot Matplotlib interfaces
Publication-quality export settings for PNG, PDF, and SVG formats

Use Cases

Building custom visualization templates for consistent branding and styling
Automating the generation of data exploration plots during large-scale analysis
Creating professional figures and charts for scientific journals and research reports