Can I create complex layouts with multiple subplots?

Absolutely. The skill covers various layout methods including standard subplots, subplot_mosaic for flexible grids, and GridSpec for maximum control.

Does the skill help with visualization accessibility?

Yes, it provides guidance on using colorblind-friendly colormaps like viridis and cividis, as well as adding patterns and high-contrast labels.

Is it compatible with data science libraries like Pandas?

Yes, it is designed to integrate seamlessly with NumPy and Pandas for direct plotting from arrays and DataFrames.

How do I ensure my plots are publication-ready?

The skill includes best practices for high-DPI exports, vector formats like PDF/SVG, and specific parameter settings like bbox_inches='tight'.

Does this skill support the Object-Oriented interface?

Yes, it specifically recommends and provides guidance for the Object-Oriented API (Figure/Axes) for better control and maintainability.

Matplotlib Visualization

Name: Matplotlib Visualization
Author: ovachiever

byovachiever

•

Data Science & ML

Generates professional-grade scientific plots and data visualizations using Python's foundational plotting library.

The Matplotlib skill empowers developers and data scientists to create high-quality, publication-ready visualizations within the Claude Code environment. It provides expert guidance on navigating the complex Matplotlib hierarchy, choosing between the pyplot and object-oriented interfaces, and implementing best practices for layout, styling, and accessibility. Whether you are performing exploratory data analysis or building complex multi-panel figures for scientific papers, this skill streamlines the process from initial data mapping to final export in high-resolution formats like PNG, PDF, and SVG.

Key Features

01Expert guidance for both Pyplot and Object-Oriented interfaces

02Customizable styling with rcParams, colormaps, and colorblind-friendly palettes

03Optimized export settings for high-resolution and vector-based publication figures

04Automated creation of diverse plot types including line, scatter, bar, and heatmaps

05Advanced layout management using Subplots, Mosaic, and GridSpec

065 GitHub stars

Use Cases

01Generating publication-quality figures for scientific research and technical reports

02Automating the generation of data-driven dashboards and performance metrics

03Exploratory data analysis using rapid-prototype charts and histograms

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ovachiever/droid-tings matplotlib

For use in Claude.ai and ChatGPT

Download Skill