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data-visualization

Select chart types and generate publication-ready Python visualizations with matplotlib, seaborn, and plotly.

14|3|Updated Jan 19, 2026
Search Tags:#accessibility#python#plotly#matplotlib#chart#seaborn#data-visualization
One-click install
npx skills add https://github.com/kevinlin/cowork-z --skill data-visualization-kevinlin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/kevinlin/cowork-z/tree/main/src-tauri/resources/skill-templates/data-visualization
Command: npx skills add https://github.com/kevinlin/cowork-z --skill data-visualization-kevinlin

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Create effective data visualizations by guiding chart selection and providing Python code patterns, design principles, and accessibility considerations to communicate insights clearly.

Core Features & Use Cases

  • Chart selection guidance for common data scenarios (time series, comparisons, distributions, correlations, and hierarchy).
  • Python visualization code patterns for matplotlib, seaborn, and plotly to produce publication-quality figures.
  • Design principles and accessibility considerations to improve readability, color theory, and inclusivity in charts.

Quick Start

Select an appropriate chart type for your dataset and implement the corresponding Python snippet to generate a publication-ready figure.

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 Python chart type for my dataset?▼

To choose the right Python chart type, identify your data scenario like time series, comparisons, distributions, correlations, or hierarchy. This skill provides chart selection guidance to match your dataset with the most effective visualization pattern for clear communication.

How do I create publication-quality figures using matplotlib, seaborn, and plotly?▼

To create publication-quality figures, apply the Python visualization code patterns provided by this skill. It covers implementation snippets for matplotlib, seaborn, and plotly to generate clear, polished visuals directly from raw data.

What are the best practices for accessibility in data visualization?▼

Best practices for accessibility in data visualization involve applying design principles for readability, color theory, and inclusivity. This skill integrates accessibility considerations into chart creation to ensure visuals are clear and inclusive for all audiences.

Do I need prior Python visualization experience to use these chart patterns?▼

Yes, using these chart patterns requires familiarity with Python visualization libraries and accessible design best practices. This skill provides advanced guidance and code patterns for matplotlib, seaborn, and plotly, assuming existing foundational knowledge of these frameworks.

When should I use plotly instead of matplotlib or seaborn for my charts?▼

The choice between plotly, matplotlib, and seaborn depends on your output needs. This skill provides code patterns across all three libraries, helping you select the appropriate Python framework based on specific chart interactivity and publication requirements.

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