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matplotlib

Create customizable scientific plots and publication-ready figures from numerical data.

74|5|Updated Dec 10, 2025
Search Tags:#data analysis#visualization#matplotlib#publication figures#style configuration#scientific plotting
One-click install
npx skills add https://github.com/dralkh/seerai --skill matplotlib-dralkh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/dralkh/seerai/tree/main/skills/matplotlib
Command: npx skills add https://github.com/dralkh/seerai --skill matplotlib-dralkh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, matplotlib, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Matplotlib helps you turn raw numerical data into clear, customizable, publication-quality visualizations when built-in charting is too limited or you need exact control over every visual detail.

Core Features & Use Cases

  • Fine-Grained Plot Control: Adjust every element of a figure, including axes, spines, ticks, legends, labels, annotations, and layout.
  • Broad Plot Coverage: Create line charts, scatter plots, bar charts, histograms, heatmaps, contour plots, box plots, violin plots, and 3D surface figures.
  • Research and Reporting Workflows: Produce multi-panel scientific figures, apply reusable style presets, and export clean PNG, PDF, or SVG outputs for papers, slides, and reports.
  • Practical Example: Use this Skill to compare experimental groups with a styled box plot, add confidence bands to a time series, and export the final figure for a manuscript submission.

Quick Start

Ask for a matplotlib figure that visualizes your data with publication-ready styling, clear labels, and the most appropriate plot type for your analysis.

Frequently Asked Questions about matplotlib

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create publication-ready scientific plots from numerical data?▼

You can create publication-ready scientific plots by using Matplotlib object-oriented APIs with NumPy arrays. This approach enables fine-grained control over axes, ticks, legends, and labels to produce deterministic high-quality outputs for research and reporting.

Can I build multi-panel layouts and 3D surface plots in the same figure?▼

Yes, multi-panel layouts and 3D surface plots can be built together. The Skill supports creating multi-panel scientific figures alongside line charts, heatmaps, contour plots, and 3D surfaces within a single customizable visualization workflow.

How do I apply reusable style presets to matplotlib figures?▼

You apply reusable style presets by using scriptable rcParams and style management. This allows you to configure styling deterministically across multiple publication figures, ensuring consistent visual output for papers and reports.

Does this visualization Skill support fitting statistical distributions to plotted data?▼

Yes, statistical distribution fitting is supported through SciPy integration. You can fit distributions to your data and visualize the results using various plot types like histograms, violin plots, and box plots for exploratory analysis.

What is the best way to export clean PNG, PDF, or SVG outputs for a manuscript?▼

The best way to export clean outputs for manuscripts is by using Matplotlib's scriptable figure export functions. You can generate styled multi-panel figures with precise layout control and export them directly to PNG, PDF, or SVG formats.

Do I need NumPy arrays to generate charts, or can I use standard Python lists?▼

You need NumPy arrays to generate charts effectively. The Skill's processing logic requires Matplotlib object-oriented APIs and NumPy arrays to handle numerical data and produce customizable scientific plots with deterministic output.

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