create-viz

Generate publication-quality charts from DataFrames, CSVs, or query results using matplotlib, seaborn, or Plotly.

1|Updated Mar 19, 2026

Search Tags:#python #visualization #pandas #data-analysis #plotly #matplotlib #seaborn

Ready to run

npx skills add https://github.com/cy-wali/knowledge --skill create-viz-cy-wali

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: create-viz
Source: https://github.com/cy-wali/knowledge/tree/main/data/skills/create-viz
Command: npx skills add https://github.com/cy-wali/knowledge --skill create-viz-cy-wali

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Create precise, aesthetically pleasing data visualizations from raw data sources using Python, reducing design guesswork and chart-manipulation time.

Core Features & Use Cases

Chart selection guidance: Recommends appropriate chart types based on data relationships (trend, distribution, comparison) to communicate insights clearly.
Code-driven visuals: Generates publication-ready charts with matplotlib, seaborn, or Plotly, ensuring professional styling and reproducibility.
Workflow support: Works with DataFrames, CSVs, or query results to produce visuals for reports, dashboards, and presentations.

Quick Start

Provide your data source and desired chart type, and I will generate a publication-quality visualization.

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 data visualizations from a pandas DataFrame?▼

To generate publication-quality data visualizations from a pandas DataFrame, provide your data source and desired chart type to receive reproducible Python code templates using matplotlib, seaborn, or Plotly with consistent professional styling.

What is the best way to choose chart types for my CSV data analysis report?▼

Choosing chart types for CSV data analysis reports requires evaluating data relationships like trend, distribution, or comparison. The skill provides chart selection guidance to recommend appropriate visuals that communicate insights clearly.

Can I use Plotly and seaborn to create interactive dashboards from query results?▼

Yes, you can use Plotly and seaborn to create dashboards from query results. The skill processes query outputs and generates publication-ready charts suitable for reports, dashboards, and presentations using these Python libraries.

Does this approach work with raw CSV files or do I need to preprocess data into DataFrames?▼

This approach works directly with raw CSV files, DataFrames, and query results. It handles data processing using pandas DataFrames to produce visuals, ensuring you can turn varied data sources into charts without separate preprocessing.

What is the difference between using matplotlib, seaborn, or Plotly for data visualization?▼

The difference between matplotlib, seaborn, and Plotly involves styling and interactivity. The skill generates code-driven visuals across all three libraries, ensuring professional styling and reproducibility while guiding design choices based on your chart requirements.

Why does my generated chart lack professional styling for presentations?▼

Generated charts lack professional styling when default settings are applied. This skill resolves that by applying consistent style templates and design choices to matplotlib, seaborn, and Plotly outputs, ensuring publication-quality aesthetics for presentations.