viz

Generate production-ready data visualizations using Python and R plotting libraries.

1|Updated Jul 5, 2026
One-click install
npx skills add https://github.com/AidenSbVevo/claude-code-starter --skill viz-aidensbvevo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: viz
Source: https://github.com/AidenSbVevo/claude-code-starter/tree/main/skills/viz
Command: npx skills add https://github.com/AidenSbVevo/claude-code-starter --skill viz-aidensbvevo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates the guesswork in data visualization by providing a structured framework for creating clear, honest, and professional-grade charts that effectively communicate insights.

Core Features & Use Cases

  • Multi-Library Mastery: Expert-level implementation across Python (matplotlib, seaborn, plotly, Altair) and R (ggplot2, ComplexHeatmap).
  • Production Standards: Automated application of style templates, colorblind-safe palettes, and vector-ready export settings.
  • Use Case: When you need to generate a multi-panel figure for a research paper or a high-stakes executive dashboard, this skill ensures your plots are legible, consistent, and visually optimized for the target medium.

Quick Start

Use the viz skill to generate a publication-quality scatter plot from the current dataframe with a trend line and colorblind-safe styling.

Frequently Asked Questions about viz

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

FAQPage Schema
How do I create a publication-quality multi-panel figure in Python?▼

To create a multi-panel figure, this skill uses matplotlib and seaborn to generate complex layouts with standardized style templates, colorblind-safe palettes, and vector-ready export settings for professional audiences.

Can I use ggplot2 to generate statistical evaluation plots for a research paper?▼

Yes, you can use ggplot2 through this skill to create statistical evaluation plots. It applies automated style templates and ensures high-fidelity, accessible output suitable for publication-quality research figures.

What is the best way to build an interactive dashboard with Python and R plotting libraries?▼

The best way to build an interactive dashboard is using this skill's expert-level implementation across Python (plotly, Altair) and R (ggplot2, ComplexHeatmap), ensuring visual consistency and programmatic rendering.

Does this data visualization skill support colorblind-safe palettes and vector-ready export?▼

Yes, this data visualization skill enforces production standards by automatically applying colorblind-safe palettes and vector-ready export settings to ensure charts are legible and visually optimized for the target medium.

How do I automate visual consistency across multiple data visualization charts?▼

You automate visual consistency by applying this skill's standardized style templates during programmatic rendering, which eliminates guesswork and ensures all charts maintain professional-grade clarity and accessibility.