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create-viz

Generate publication-quality charts from data using Python libraries.

23.4k|2.8k|Updated Jan 23, 2026
Search Tags:#charts#python#data analysis#visualization#plotly#matplotlib#seaborn
One-click install
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill create-viz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: create-viz
Source: https://github.com/anthropics/knowledge-work-plugins/tree/main/data/skills/create-viz
Command: npx skills add https://github.com/anthropics/knowledge-work-plugins --skill create-viz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the creation of publication-quality data visualizations, transforming raw data into clear, insightful charts for reports and presentations.

Core Features & Use Cases

  • Data Visualization: Creates charts from query results, pasted data, or files using Python libraries like Matplotlib, Seaborn, and Plotly.
  • Chart Type Recommendation: Suggests appropriate chart types (line, bar, scatter, heatmap, etc.) based on data relationships and user intent.
  • Design Best Practices: Applies principles of color, typography, layout, and accuracy for effective data communication.
  • Use Case: Turn a table of monthly sales figures into a line chart showing revenue trends over time, with clear labels and a descriptive title.

Quick Start

Use the create-viz skill to show monthly revenue for the last 12 months as a line chart with the trend highlighted.

Frequently Asked Questions about create-viz

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

FAQPage Schema
How do I generate publication-quality charts from raw data?▼

Generate publication-quality charts by passing raw data, query results, or files to the visualization engine, which applies design best practices for color, typography, and layout to produce clear, accurate visualizations.

Can I create interactive plots using Python visualization libraries?▼

Yes, you can create both static and interactive plots using Python. The tool leverages libraries like Plotly, Matplotlib, and Seaborn to generate visualizations tailored for reports and presentations.

How do I choose the right chart type for my data analysis?▼

The tool automatically suggests appropriate chart types, such as line, bar, scatter, or heatmap, based on your underlying data relationships and specific visualization intent.

Does this approach work for visualizing trends and distributions?▼

Yes, the visualization process explicitly supports creating charts for trends, comparisons, distributions, and correlations, transforming data into insightful visual representations.

What is the best way to turn a table of monthly sales figures into a line chart?▼

Provide the monthly sales data as input, and the tool generates a line chart showing revenue trends over time, complete with clear labels and a descriptive title for effective communication.

Are there limitations when using Python for data visualization?▼

The primary limitation is dependency on Python libraries like Matplotlib, Seaborn, and Plotly; users must ensure their data is properly formatted before generating accurate visualizations.

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