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data-visualization

Generate publication-quality charts with Python using Matplotlib, Seaborn and Plotly.

1|2|Updated Jan 15, 2026
Search Tags:#data storytelling#charts#design principles#visualization#matplotlib#seaborn
One-click install
npx skills add https://github.com/ihmorol/unsw-nb15-handling-binary-multiclass-ids --skill data-visualization-ihmorol
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/ihmorol/unsw-nb15-handling-binary-multiclass-ids/tree/main/.opencode/skills/data-visualization
Command: npx skills add https://github.com/ihmorol/unsw-nb15-handling-binary-multiclass-ids --skill data-visualization-ihmorol

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps users create clear, effective, and accessible data visualizations by providing guidance on chart selection, Python code patterns, and design principles.

Core Features & Use Cases

  • Chart Selection: Recommends the best chart type based on the data relationship being shown.
  • Python Code Examples: Provides ready-to-use code snippets for common chart types using Matplotlib and Seaborn.
  • Design Principles: Offers guidance on color, typography, layout, and accuracy for impactful visuals.
  • Accessibility: Includes considerations for color blindness and screen readers.
  • Use Case: You have a dataset showing sales trends over time for different product categories. This Skill can help you choose the best chart (e.g., a line chart or small multiples) and provide the Python code to generate it, ensuring it's both informative and accessible.

Quick Start

Use the data-visualization skill to generate a bar chart showing the comparison of sales across different product categories from the attached 'sales_data.csv' file.

Frequently Asked Questions about data-visualization

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

FAQPage Schema
How do I choose the right chart type for my data in Python?▼

Chart selection for data visualization depends on the data relationship being shown. This Skill provides guidance on matching chart types, such as using line charts for time trends or heatmaps for correlation matrices, to your specific dataset.

How do I create accessible data visualizations with Matplotlib and Seaborn?▼

Creating accessible data visualizations requires considering color blindness and screen reader compatibility. This Skill provides design principles and Python code patterns using Matplotlib and Seaborn to ensure your charts are both accessible and impactful.

What's the best way to generate publication-quality figures in Python?▼

Generating publication-quality figures involves applying design principles for color, typography, and layout. This Skill offers Python code examples and guidance using Matplotlib, Seaborn, and Plotly to produce effective, high-quality visuals.

Can I use Plotly with pandas and numpy for interactive data visualization?▼

Yes, Plotly is supported alongside pandas and numpy for data visualization. This Skill provides ready-to-use Python code snippets that integrate these libraries to generate various chart types, including interactive and publication-quality figures.

Does this Skill provide guidance on data storytelling and design principles?▼

Data storytelling and design principles are core features of this Skill. It offers guidance on color, typography, layout, and accuracy to help you create impactful data visualizations that effectively communicate your data's narrative.

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