What Python libraries does the create-viz skill use?

It primarily utilizes Matplotlib and Seaborn for high-quality static images, and Plotly for interactive charts that support features like hovering and zooming.

Does it follow professional design standards?

Yes, the skill automatically applies best practices such as removing 'chart junk', using accessible color palettes, and ensuring axes are properly labeled and scaled.

How are the generated charts delivered?

Charts are typically saved as high-resolution PNG files in your current directory and displayed directly in the chat interface.

Can I visualize data directly from a database?

Yes, if you have a data warehouse connector configured, the skill can execute SQL queries and immediately load the results into a visualization workflow.

What types of charts can this skill create?

It supports a vast range of visualizations including line charts, bar charts, heatmaps, Sankey diagrams, scatter plots, choropleth maps, and box plots.

Data Visualization Pro

Name: Data Visualization Pro
Author: anthropics

byanthropics

•

11,566

•

Data Science & ML

Generates publication-quality, professional data visualizations and charts using Python libraries like Matplotlib and Plotly.

The create-viz skill enables knowledge workers to transform raw data from SQL queries, CSV files, or pasted text into high-quality visual insights directly within Claude. It intelligently recommends the most effective chart types based on data relationships—such as trends, distributions, or correlations—and applies professional design principles to ensure clarity and accuracy. Whether you need static PNGs for a slide deck or interactive Plotly charts for data exploration, this skill automates the Python coding process to deliver report-ready graphics with minimal effort.

Key Features

0111,566 GitHub stars

02Direct integration with data warehouses for seamless query-to-chart workflows

03Professional styling including colorblind-friendly palettes and clean layouts

04Automatic chart type recommendation based on data structure and purpose

05Automated data cleaning and preparation using pandas DataFrames

06Support for static Matplotlib/Seaborn and interactive Plotly visualizations

Use Cases

01Creating interactive scatter plots to identify correlations in complex customer datasets

02Transforming monthly SQL sales queries into annotated trend lines for executive reports

03Generating distribution histograms and box plots for statistical data analysis

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add anthropics/knowledge-work-plugins create-viz

For use in Claude.ai and ChatGPT

Key Features

0111,566 GitHub stars

02Direct integration with data warehouses for seamless query-to-chart workflows

03Professional styling including colorblind-friendly palettes and clean layouts

04Automatic chart type recommendation based on data structure and purpose

05Automated data cleaning and preparation using pandas DataFrames

06Support for static Matplotlib/Seaborn and interactive Plotly visualizations

Use Cases

01Creating interactive scatter plots to identify correlations in complex customer datasets

02Transforming monthly SQL sales queries into annotated trend lines for executive reports

03Generating distribution histograms and box plots for statistical data analysis

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add anthropics/knowledge-work-plugins create-viz

For use in Claude.ai and ChatGPT