Does it follow accessibility and design best practices?

Absolutely. It prioritizes clear labeling, high-contrast color schemes, and design patterns that are accessible to colorblind users while removing 'chart junk'.

Can I use this to build web-based dashboards?

Yes, it provides optimized implementation patterns for building interactive dashboards using Streamlit and Plotly that are ready for web deployment.

Which visualization libraries does this skill support?

It supports a wide range of industry standards including Matplotlib, Seaborn, Plotly, D3.js, and Streamlit, covering both static and interactive needs.

Can it help me choose the right chart for my data?

Yes, the skill includes logic to match your data structure—such as comparison, distribution, composition, or relationship—with the most effective chart type.

Data Visualization

Name: Data Visualization
Author: cosmix

bycosmix

Data Science & ML

Generates insightful, professional-grade charts and interactive dashboards using industry-standard libraries and design principles.

About

This skill empowers Claude to design and implement clear, impactful data visualizations by selecting the most appropriate chart types for specific data structures and business goals. It handles everything from static Python plots using Matplotlib and Seaborn to sophisticated interactive web-based dashboards with Plotly and Streamlit. By prioritizing core design principles like clarity, accessibility, and high data-to-ink ratios, the skill ensures that complex datasets are transformed into actionable insights that are easy for any target audience to interpret and use.

Key Features

0 GitHub stars
Implementation of static plots with Matplotlib and Seaborn
Adherence to data design best practices and accessibility standards
Creation of interactive visualizations and dashboards with Plotly
Automated chart type selection based on data relationships
Integration with Streamlit for rapid web-based dashboard deployment

Use Cases

Visualizing scientific data and statistical distributions for research analysis
Building real-time monitoring tools for application logs and system health metrics
Analyzing business performance metrics through interactive financial dashboards

About

Key Features

0 GitHub stars
Implementation of static plots with Matplotlib and Seaborn
Adherence to data design best practices and accessibility standards
Creation of interactive visualizations and dashboards with Plotly
Automated chart type selection based on data relationships
Integration with Streamlit for rapid web-based dashboard deployment

Use Cases

Visualizing scientific data and statistical distributions for research analysis
Building real-time monitoring tools for application logs and system health metrics
Analyzing business performance metrics through interactive financial dashboards