matplotlib
Technical implementation skill for rendering publication-grade plots and custom visualizations.
Install
mkdir -p .claude/skills/matplotlib-liq22 && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/18410" && unzip -o skill.zip -d .claude/skills/matplotlib-liq22 && rm skill.zipInstalls to .claude/skills/matplotlib-liq22
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Implementation skill for low-level plotting with matplotlib (Python). Use for fine-grained control over plot elements, novel plot types (3D surfaces, custom projections), subplots/mosaics, rcParams styling, or PNG/PDF/SVG export. Do not use for figure planning — defer to scientific-visualization; this is only the rendering engine.Key capabilities
- →Render publication-quality figures with matplotlib
- →Control Figure/Axes hierarchy and plot types
- →Apply `rcParams` styling and colormaps
- →Export to specific formats and resolutions (PDF, PNG, SVG)
- →Bulk restyle figures using `.mplstyle` sheets
- →Produce figures for embedding in `paper/tex/` or `paper/draft/`
How it works
The skill renders matplotlib figures with fine-grained control over plot elements, styling, and export options. It takes data and figure specifications as input and produces high-quality image files.
Inputs & outputs
When to use matplotlib
- →Rendering publication-quality plots
- →Exporting SVG/PDF figures
- →Custom 3D plot rendering
About matplotlib
Handles low-level Matplotlib rendering, allowing precise control over figure elements, custom projections, and multi-format exports (PDF/PNG/SVG).
Implementation skill for low-level plotting with matplotlib (Python). Use for fine-grained control over plot elements, novel plot types (3D surfaces, custom projections), subplots/mosaics, rcParams styling, or PNG/PDF/SVG export. Do not use for figure planning — defer to scientific-visualization; th
When not to use it
- →For figure planning (panel layout, journal column widths, captions).
- →For classical ML plots (defer to scikit-learn).
- →For deep-learning training curves (defer to pytorch-lightning).
Limitations
- →The skill does not decide figure content, panel choice, or captions.
- →The skill does not fabricate data; it plots only values present in a run or supplied by the user.
- →The skill does not write figures anywhere except `paper/assets/figures/` (or a clearly named scratch dir).
How it compares
This skill provides low-level control over matplotlib rendering for publication-quality figures and specific export formats, unlike higher-level visualization tools that abstract away such details.
Compared to similar skills
matplotlib side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| matplotlib (this skill) | 0 | 16d | Review | Advanced |
| scientific-visualization | 26 | 7mo | Review | Intermediate |
| openalex-database | 48 | 7mo | Review | Intermediate |
| bio-data-visualization-color-palettes | 0 | 1mo | No flags | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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