# Data Artist

Create beautiful data visualizations with mathematical elegance, color theory, and...

- Repository: https://github.com/foryourhealth111-pixel/Vibe-Skills (3789 stars)
- Category: Design
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-artist -g -a claude-code -y`
Codex: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-artist -g -a codex -y`
Cursor: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-artist -g -a cursor -y`
Gemini CLI: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-artist -g -a gemini-cli -y`

## Description

Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Data Artist

You are creating a work of data art. This skill brings together mathematical elegance, emotional resonance, narrative design, and technical excellence to transform raw data into something beautiful that tells a story and moves the viewer.

## The "Data is Beautiful" Philosophy

### Core Principles

1. **Life is Beautiful** - Data visualization should reveal the wonder in information
2. **Mathematical Elegance** - Perceptually accurate encodings, thoughtful scales
3. **Emotional Resonance** - Create moments of awe, reflection, insight
4. **Swiss Minimalism** - Clean geometry, purposeful color, no chartjunk
5. **Narrative Journey** - Guide the viewer through a story

### What Makes Data Beautiful

- **Clarity** - The data speaks clearly without distortion
- **Proportion** - Visual weight matches data importance
- **Rhythm** - Patterns emerge naturally from the encoding
- **Surprise** - Reveals insights not obvious in raw numbers
- **Humanity** - Connects data to human experience

## Visualization Domains

### 1. Mathematical Foundations (@geepers_datavis_math)

**Scale Selection:**
- Linear for comparison
- Log for orders of magnitude
- Sqrt for area perception
- Time scales for temporal data

**Visual Encoding:**
- Position (most accurate)
- Length/height (good)
- Angle/slope (moderate)
- Area (requires sqrt scaling)
- Color intensity (least precise)

**Perceptual Accuracy:**
- Ensure encodings don't mislead
- Account for human perception biases
- Use perceptually uniform color scales

### 2. Color Design (@geepers_datavis_color)

**Palette Types:**
- Sequential: Low → High (single hue)
- Diverging: Negative ↔ Neutral ↔ Positive
- Categorical: Distinct groups (max 7-9)

**Color Principles:**
- Perceptual uniformity (Lab/HCL color space)
- Colorblind accessibility (avoid red-green only)
- Emotional resonance (warm/cool, muted/vibrant)
- Cultural considerations

The rest: https://github.com/foryourhealth111-pixel/Vibe-Skills/blob/HEAD/bundled/skills/data-artist/SKILL.md