matplotlib

Generate publication-grade multi-panel figures with matplotlib for CS research.

3|Updated Mar 11, 2026

Search Tags:#visualization #plotting #matplotlib #export #figures #publication-quality #multi-panel

Ready to run

npx skills add https://github.com/JunMA98/Computer-science-claude-skills --skill matplotlib-junma98

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/JunMA98/Computer-science-claude-skills/tree/main/skills/matplotlib
Command: npx skills add https://github.com/JunMA98/Computer-science-claude-skills --skill matplotlib-junma98

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, matplotlib, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Create publication-quality figures for CS research workflows, enabling precise control over layout, styling, and export formats to support papers, reports, and presentations.

Core Features & Use Cases

Fine-grained control over figure structure, axes, annotations, and export settings.
Supports multi-panel layouts and publication-ready exports (PNG, PDF, SVG).
Use cases include experiment visuals, literature figures, benchmarking reports, and technical documentation.

Quick Start

Create a publication-ready multi-panel figure comparing line and bar plots for a CS experiment.

Frequently Asked Questions about matplotlib

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate publication-ready figures for a research paper?▼

You can generate publication-quality figures by using an object-oriented plotting approach that enforces precise control over layout, styling, and consistent export options to PNG, PDF, and SVG for research workflows.

Can I create multi-panel layouts for technical reports and presentations?▼

Yes, creating multi-panel layouts is fully supported for papers, reports, and presentations. The Skill provides fine-grained control over figure structure, axes, and annotations to build complex experiment visuals.

What types of plots are supported for benchmarking reports and experiment visuals?▼

Supported plot types for benchmarking reports include line, scatter, bar, heatmap, contour, and 3D plots. These options cover standard experiment visualization needs across technical documentation.

Do I need numpy and scipy installed to use this plotting workflow?▼

Yes, numpy and scipy are required dependencies alongside matplotlib. You need these libraries installed in your environment to process data and execute the publication-grade figure generation workflow.

What is the best way to ensure consistent export settings across multiple figures?▼

The best way to ensure consistent export settings is to use the built-in object-oriented plotting approach. This mechanism provides precise layout control and standardized export options to PNG, PDF, and SVG formats.

Why does this workflow use an object-oriented approach for visualization?▼

The object-oriented approach for visualization is enforced to provide fine-grained control over figure structure, axes, and annotations. This ensures precise layout management required for publication-quality outputs.