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.
npx skills add K-Dense-AI/scientific-agent-skills --skill matplotlib --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ## 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-interactiv
- Overview
- When to Use This Skill
- Setup
- Core Concepts
- The Matplotlib Hierarchy
- Two Interfaces
- Common Workflows
- 1. Basic Plot Creation
- 2. Multiple Subplots
- 3. Plot Types and Use Cases
- 4. Styling and Customization
- 5. Saving Figures
- 6. Working with 3D Plots
- Best Practices
uv add matplotlib uv add matplotlib ipympl uv run python scripts/plot_template.py uv run python scripts/style_configurator.py
What does the matplotlib skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill matplotlib --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.