matplotlib

Overview of matplotlib skill

What matplotlib is for

The matplotlib skill is for Python users who need precise control over plots, not just “a chart that looks okay.” It helps you create static, animated, or interactive visualizations with full control over axes, labels, legends, colors, layout, and export formats.

Best fit and real job-to-be-done

Use the matplotlib skill when the goal is to turn data into a figure you can trust, reuse, and publish: scientific plots, dashboards embedded in Python workflows, multi-panel analyses, or custom chart types. It is especially useful for matplotlib for Data Analysis when you need exact formatting and reproducible output.

Why choose matplotlib over a generic prompt

Compared with a generic plotting prompt, the matplotlib skill is better when you care about the Figure/Axes model, subplot structure, saving to PNG/PDF/SVG, and avoiding “one-off” code that breaks across datasets. It is less opinionated than seaborn and more manual than plotly, which is the tradeoff you want when customization matters.

How to Use matplotlib skill

Install and locate the core instructions

Install the matplotlib skill with:

npx skills add K-Dense-AI/claude-scientific-skills --skill matplotlib

Then read SKILL.md first. In this repo, there are no supporting rules/, resources/, or helper scripts, so the main value is in the skill body itself and the examples embedded there.

Give the skill the right input

A strong prompt names the plot goal, data shape, audience, and output constraints. For example: “Create a 2-panel matplotlib figure for a paper: line plot of temperature over time and a scatter plot of error vs. prediction, using a clean serif style, shared x-axis, legend outside the axes, and export as SVG.”

Start from the workflow, not the code

The best matplotlib usage usually follows this path: define the figure purpose, choose the right API (pyplot for quick work, OO API for reusable code), map each data series to an axis, then tune labels, ticks, spacing, and export settings. If you only ask for “a plot,” you often get defaults that need cleanup.

What to read first in the repo

Start with SKILL.md, then focus on the sections about when to use the skill, core concepts, and the object hierarchy. Those parts matter most because they tell you how to structure prompts and avoid mixing up figure-level and axis-level operations.

matplotlib skill FAQ

Is matplotlib the right skill for every chart?

No. Use matplotlib when you need control and compatibility, not when you want the fastest attractive chart. For quick statistical visuals, a seaborn-oriented workflow is often easier. For interactive exploration, plotly is usually a better fit.

Do I need to know Python well to use it?

Basic Python helps, but beginners can still use the matplotlib skill if they can describe their data and desired output clearly. The main barrier is usually not syntax; it is failing to specify layout, labels, and export requirements.

How does matplotlib compare to an ordinary prompt?

An ordinary prompt may produce a working snippet, but the matplotlib skill should reduce guesswork around figure structure, customization, and publication output. It is most valuable when the plot has multiple layers, subplots, or format constraints that make a generic answer too fragile.

When should I not use matplotlib skill?

Do not default to matplotlib if you need browser-based interaction, rapid exploratory dashboards, or highly stylized statistical charts with minimal code. If your task is mostly aesthetic and simple, another visualization tool may be faster.

How to Improve matplotlib skill

Specify the figure before the styling

The best results come from stating the plot job clearly: chart type, number of panels, target audience, and final format. “Make it pretty” is weak; “Make a 3-panel matplotlib figure for a report, with shared legend, compact spacing, and PDF export” is much better.

Provide data shape and failure constraints

Say whether your data is a DataFrame, arrays, grouped categories, time series, or irregular measurements. Also mention constraints like “must work with missing values,” “no seaborn,” “use dark background,” or “fit on one page.” These details prevent output that looks right but is unusable.

Ask for the decisions that affect quality

If you want better matplotlib usage, ask for the choices that change the figure: tick density, color palette, axis limits, annotation strategy, grid visibility, and whether to use tight_layout or constrained_layout. Those are the parts that separate a draft plot from a usable one.

Iterate from plot correctness to polish

First verify that the data mapping is correct, then improve readability, then tune export settings. Common failure modes are mislabeled axes, overcrowded legends, and layout collisions in multi-panel figures. If the first output is close, ask for one targeted revision instead of rewriting the whole prompt.