ppw:visualization

What problem does it solve?

This Skill helps researchers and practitioners quickly identify the most appropriate visualization types for experimental data, reducing guesswork and improving communicative clarity in manuscripts and reports.

Core Features & Use Cases

• Accepts a plain-language description of data (type, variables, sample size) and a research question, and returns 2–3 chart recommendations ordered by fit.
• Geography-aware: if spatial signals are detected, includes choropleth, spatial scatter, or kernel density map suggestions alongside general charts.
• Provides brief rationales and library hints to facilitate quick implementation in Python or R.

Quick Start

Provide a plain-language data description and your visualization goal, and I will return 2–3 chart recommendations.