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
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/matplotlib
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
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
📊 Stats
Stars31,542
Forks3,146
LanguagePython
LicenseMIT
📦 Ships with scientific-agent-skills
</> SKILL.md
matplotlib.SKILL.md
---name: matplotlib
description: 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.
allowed-tools:
- Read
- Write
- Bash
license: https://github.com/matplotlib/matplotlib/tree/main/LICENSE
compatibility: Requires Python 3.10+ and Matplotlib 3.10.x. Use `uv add matplotlib` in projects; interactive Jupyter widgets require `ipympl`.
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---# 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)
- 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
## Setup
For project work, install Matplotlib with uv:
```bash
uv add matplotlib
```
For notebook interactivity:
```bash
uv add matplotlib ipympl
```
Then enable the widget backend in Jupyter with `%matplotlib widget` or `%matplotlib ipympl`.
Matplotlib 3.10 requires Python 3.10+ and NumPy 1.23+. Non-interactive file output works through backends such as Agg, PDF, and SVG. For GUI windows, Matplotlib auto-selects an available backend; if `TkAgg` fails in a uv-managed Python, update uv and Python builds with `uv self update` and `uv python upgrade --reinstall`, or install a Qt backend with `uv add pyside6`.
## 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)**
```python
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()
```
- Convenient for quick, simple plots
- Maintains state automatically
- Good for interactive work and simple scripts
**2. Object-Oriented Interface (Explicit)**
```python
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4])
ax.set_ylabel('some numbers')
plt.show()
```
- **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:**
```python
import matplotlib.pyplot as plt
import numpy as np
# Create figure and axes (OO interface - RECOMMENDED)
