Agent skill · creative production · aiskillstore
matplotlib
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Why this skill is useful
Provides specific workflows and customization techniques for creating complex visualizations that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Matplotlib Overview Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations. When to Use This Skill This skill should be used when: Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.) Generating scientific or statistical visualizations Customizing plot appearance (colors, styles, labels, legends) Creating multi-panel figures with subplots Exporting visualizations to various formats (PNG, PDF, SVG, etc.) Building interactive plots or animations Working with 3D visualizations Integrating plots into Jupyter notebooks or GUI applications Core Concepts The Matplotlib Hierarchy Matplotlib uses a hierarchical structure of objects: 1. Figure - The top-level container for all plot elements 2. Axes - The actual plotting area where data is displayed (one Figure can contain multiple Axes) 3. Artist - Everything visible on the figure (lines, text, ticks, etc.) 4. Axis - The number line objects (x-axis, y-axis) that handle ticks and labels Two Interfaces 1. pyplot Interface (Implicit, MATLAB-style) Convenient for quick, simple plots Maintains state automatically Good for interactive work and simple scripts 2. Object-Oriented Interface (Explicit) Recommended for most use cases More explicit control over figure and axes Better for complex figures with multiple subplots Easier to maintain and debug Common Workflows 1. Basic Plot Creation Single plot workflow: 2. Multiple Subplots Creating subplot layouts: 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/matplotlib--80cb7a