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Matplotlib

Listed
davila7·vlatest·data·MIT

Generates static, interactive, and animated visualizations from Python code using Matplotlib, aiding in data exploration and presentation.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill matplotlib -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

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scanned

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Not scanned. none of the 457 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as davila7/claude-code-templates.
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About this skill

What it does

The matplotlib skill enables AI agents to generate visualizations using the Matplotlib library in Python. It allows for creating line plots, bar charts, scatter plots, and more, providing a powerful way to represent data graphically.

When to use it

  • To visualize trends or patterns in numerical datasets.
  • For generating reports that include graphical representations of findings.
  • When presenting analysis results to stakeholders who prefer visual insights over raw numbers.
  • To support exploratory data analysis by quickly plotting different aspects of the data.

Key capabilities

  • Generating line plots, bar charts, and scatter plots
  • Customizing plot appearance (colors, labels, titles)
  • Saving generated figures in various formats (e.g., PNG, PDF)

Example prompts

  • "Create a line plot showing sales trends over time."
  • "Generate a bar chart comparing monthly website traffic."
  • "Plot a scatter diagram to visualize the relationship between temperature and ice cream sales."

Tips & gotchas

  • Ensure that your data is in a format compatible with Matplotlib (e.g., NumPy arrays or Pandas DataFrames).
  • Be mindful of plot resolution and file size when saving figures for high-quality outputs.