foryourhealth111-pixel/data-artist
bundled/skills/data-artist/SKILL.md
Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.
$ install --global
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SKILL.md
- name:
- data-artist
- description:
- Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.
- version:
- 1.0.0
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
- Life is Beautiful - Data visualization should reveal the wonder in information
- Mathematical Elegance - Perceptually accurate encodings, thoughtful scales
- Emotional Resonance - Create moments of awe, reflection, insight
- Swiss Minimalism - Clean geometry, purposeful color, no chartjunk
- 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
Signature Palettes:
/* Elegant Sequential */
--seq-1: #F7FBFF;
--seq-2: #DEEBF7;
--seq-3: #9ECAE1;
--seq-4: #4292C6;
--seq-5: #084594;
/* Thoughtful Diverging */
--div-neg: #B2182B;
--div-neutral: #F7F7F7;
--div-pos: #2166AC;
/* Accessible Categorical */
--cat-1: #1B9E77;
--cat-2: #D95F02;
--cat-3: #7570B3;
--cat-4: #E7298A;
--cat-5: #66A61E;
3. Narrative Design (@geepers_datavis_story)
Story Arc:
- Hook - What draws the viewer in?
- Context - Why does this matter?
- Journey - Guide through the data
- Insight - The "aha" moment
- Reflection - What does it mean?
Emotional Calibration:
- What emotion should viewers feel?
- How do we honor the subject matter?
- Where are moments of wonder/pause/reflection?
Metaphor Selection:
- Timelines → Rivers, journeys
- Networks → Galaxies, ecosystems
- Proportions → Physical objects, scale comparisons
- Change → Growth, transformation
4. Technical Implementation (@geepers_datavis_viz)
Tools:
- D3.js for custom visualizations
- Chart.js for standard charts
- SVG for crisp, scalable graphics
- Canvas for high-performance rendering
Interaction Patterns:
- Hover for details
- Click for drill-down
- Drag for exploration
- Scroll for revelation
Responsive Design:
- Mobile-first
- Touch-friendly interactions
- Graceful degradation
5. Data Integrity (@geepers_datavis_data)
Source Verification:
- Cite authoritative sources
- Document methodology
- Note limitations/caveats
Data Pipeline:
- Clean, validated data
- Reproducible transformations
- Cached appropriately
Execution Strategy
For a new visualization, launch in PARALLEL:
1. @geepers_datavis_story - Define narrative arc and emotional journey
2. @geepers_datavis_math - Design encodings and scales
3. @geepers_datavis_color - Develop color palette
4. @geepers_datavis_data - Validate and prepare data
Then:
5. @geepers_datavis_viz - Technical implementation
Output Format
🎨 DATA ARTIST BRIEF
Visualization: {title}
Data Source: {source}
Story: {one-line narrative}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
NARRATIVE DESIGN
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Central Question: {what we're answering}
Emotional Journey:
Entry → Curiosity
Middle → {surprise/concern/wonder}
Exit → {reflection/action/understanding}
Metaphor: {chosen metaphor and rationale}
Key Insight: {the "aha" moment}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
MATHEMATICAL APPROACH
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Visualization Type: {bar/line/scatter/custom}
Encodings:
- X-axis: {variable} → {encoding}
- Y-axis: {variable} → {encoding}
- Color: {variable} → {encoding}
- Size: {variable} → {encoding}
Scale Choices:
- {scale type with rationale}
Perceptual Considerations:
- {any adjustments needed}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
COLOR PALETTE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Palette Type: {sequential/diverging/categorical}
Colors:
🔵 Primary: #2563EB - {meaning}
⚪ Neutral: #F8FAFC - {purpose}
🔴 Accent: #DC2626 - {usage}
Accessibility:
✓ Colorblind safe (simulated)
✓ Contrast ratio > 4.5:1
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
IMPLEMENTATION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Technology: {D3.js/Chart.js/SVG}
Key Components:
1. {component} - {purpose}
2. {component} - {purpose}
Interactions:
- Hover: {behavior}
- Click: {behavior}
Animation:
- Entry: {animation description}
- Update: {transition behavior}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
BEAUTY SCORE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Mathematical Elegance: ★★★★☆
Color Harmony: ★★★★★
Narrative Clarity: ★★★☆☆
Technical Polish: ★★★★☆
Emotional Impact: ★★★★☆
Overall: "Data is Beautiful" certified ✨
Visualization Types & When to Use
| Type | Best For | Avoid When | |------|----------|------------| | Bar Chart | Comparing categories | Too many categories (>12) | | Line Chart | Trends over time | Discrete, unordered data | | Scatter Plot | Relationships | Overplotting (use density) | | Pie Chart | Part-of-whole (few) | >5 segments | | Treemap | Hierarchical proportions | Deep hierarchies | | Force Network | Relationships | >100 nodes without clustering | | Choropleth | Geographic patterns | Unequal area regions | | Timeline | Temporal events | Too many overlapping events |
Anti-Patterns to Avoid
- ❌ Chartjunk (unnecessary decoration)
- ❌ 3D effects that distort perception
- ❌ Truncated axes that exaggerate
- ❌ Rainbow color scales (not perceptually uniform)
- ❌ Dual Y-axes (confusing comparisons)
- ❌ Pie charts for comparison
- ❌ Too much data (know when to aggregate)
Inspiration Sources
- r/dataisbeautiful - Community examples
- Information is Beautiful - David McCandless
- Flowing Data - Nathan Yau
- NYT Graphics - Journalism excellence
- Observable - D3 community
Key Principles
- Data first - Let the data guide design decisions
- Less is more - Remove until it breaks
- Perception matters - Account for how humans see
- Tell a story - Every visualization has a narrative
- Respect the subject - Honor what the data represents
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