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matplotlib

Create and customize publication-quality plots with Matplotlib for PNG, PDF, and SVG outputs.

345|12|Updated Apr 14, 2026
Search Tags:#charts#graphics#python#visualization#plotting#matplotlib#data-visualization
One-click install
npx skills add https://github.com/minicoohei/ai-agent-camp --skill matplotlib-minicoohei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/minicoohei/ai-agent-camp/tree/main/.claude/skills/matplotlib
Command: npx skills add https://github.com/minicoohei/ai-agent-camp --skill matplotlib-minicoohei

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Matplotlib visualization tasks can be verbose and boilerplate-heavy; this skill consolidates guidance for building, styling, and exporting plots with Python's Matplotlib, enabling consistent, publication-ready visuals.

Core Features & Use Cases

  • Line, scatter, bar, histogram, heatmap, contour, and 3D plots with both pyplot and OO interfaces.
  • Style management, color maps, layout, and export in PNG/PDF/SVG for research reports, dashboards, and presentations.

Quick Start

Create a publication-quality plot from your data and export it as a PNG file.

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 plots with Matplotlib for a research report?▼

To create publication-quality plots with Matplotlib, use the provided templates covering both pyplot and object-oriented interfaces to build, style, and export multi-plot figures or 3D visuals consistently for research reports.

Can I export Matplotlib charts directly to SVG and PDF formats?▼

Yes, you can export Matplotlib charts directly to SVG and PDF formats. The skill supports PNG, PDF, and SVG outputs, enabling you to generate vector graphics suitable for dashboards, presentations, and publications.

Does this Matplotlib workflow support data from NumPy and Pandas?▼

Yes, this Matplotlib workflow is NumPy and Pandas-friendly, requiring NumPy, Matplotlib, and SciPy dependencies. It streamlines chart creation by directly handling data arrays and dataframes within the visualization pipeline.

What is the best way to build 3D visuals and heatmaps in Python?▼

The best way to build 3D visuals and heatmaps in Python is using Matplotlib's object-oriented interface, which provides granular control over styling, color maps, and layout for complex multi-plot figures.

How do I style and manage color maps for multiple chart types in Matplotlib?▼

You style and manage color maps for line, scatter, bar, histogram, and contour plots using Matplotlib's style management features, applying consistent layouts and custom color schemes to achieve publication-ready visuals.

Why does generating multi-plot figures in Matplotlib require so much boilerplate code?▼

Generating multi-plot figures in Matplotlib requires boilerplate code because the native API is verbose, but this skill consolidates practical templates and references to eliminate repetitive setup and streamline the chart creation process.

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