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matplotlib

Generate publication-quality static, multi-panel, and 3D plots from numerical data.

203|27|Updated Apr 19, 2026
Search Tags:#data visualization#matplotlib#publication figures#scientific plotting#plot styling#export png pdf svg#object-oriented api
One-click install
npx skills add https://github.com/franklee16/academic-research-skills --skill matplotlib-franklee16
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/franklee16/academic-research-skills/tree/main/visualization/matplotlib
Command: npx skills add https://github.com/franklee16/academic-research-skills --skill matplotlib-franklee16

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you turn research data into accurate, clear, publication-quality figures by giving you full control over plot construction, styling, layout, and export.

Core Features & Use Cases

  • End-to-end figure creation: Build figures using the recommended object-oriented API (Figure/Axes) for reliable, maintainable plotting.
  • Multiple plot types: Generate line, scatter, bar, histogram, heatmap, contour, box/violin, and 3D plots for common econometrics and social-science outputs.
  • Publication-grade styling and exporting: Configure fonts, colors/colormaps, legends, grid/layout, and save outputs as PNG/PDF/SVG with appropriate DPI and bounding boxes.

Quick Start

Use the matplotlib skill to generate a publication-quality two-panel figure and export it to PNG with tight bounding boxes.

Frequently Asked Questions about matplotlib

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

FAQPage Schema
How do I create publication-quality figures from numerical data for research papers?▼

To create publication-quality figures from numerical data, use the object-oriented Figure/Axes API to build multi-panel plots, apply consistent colormaps and legends, and export outputs as PNG, PDF, or SVG with appropriate DPI and tight bounding boxes for journal-ready styling.

What is the best way to manage layout and avoid overlapping elements in multi-panel scientific plots?▼

To manage layout and prevent overlapping elements in multi-panel scientific plots, apply constrained_layout or tight_layout within the object-oriented API, ensuring proper spacing and consistent visual structure across all generated figures.

Can I generate both 2D and 3D visualizations for econometrics and social-science outputs?▼

Yes, you can generate both 2D and 3D visualizations for econometrics and social-science outputs, including line, scatter, bar, histogram, heatmap, contour, box, violin, and 3D plots using the standard object-oriented API.

How do I export plots to PNG, PDF, and SVG while maintaining color accessibility and correct DPI?▼

To export plots to PNG, PDF, and SVG while maintaining color accessibility and correct DPI, follow best practices for rasterization and bounding boxes during the save operation, ensuring outputs meet publication standards.

Do I need any external dependencies to build static, journal-ready figures?▼

No external dependencies are required to build static, journal-ready figures, as the skill operates independently using its YAML-defined instructions and internal scripts to configure styling, fonts, and plotting logic.

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