Data visualization
macOS bootstrap dotfiles: Brewfile, declarative symlinks, app prefs, macOS defaults, agent skills, vault secrets, make doctor. Fork-friendly.
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Use when animating charts, graphs, dashboards, data transitions, or any information visualization work.
SKILL.md
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Data Visualization Animation
Apply Disney's 12 animation principles to charts, graphs, dashboards, and information displays.
Quick Reference
| Principle | Data Viz Implementation |
|---|---|
| Squash & Stretch | Bar overshoot, elastic settling |
| Anticipation | Brief pause before data loads |
| Staging | Sequential reveal, focus hierarchy |
| Straight Ahead / Pose to Pose | Streaming vs snapshot data |
| Follow Through / Overlapping | Staggered element entry |
| Slow In / Slow Out | Smooth value interpolation |
| Arc | Pie chart sweeps, flow diagrams |
| Secondary Action | Labels following data points |
| Timing | Entry 300-500ms, updates 200-300ms |
| Exaggeration | Emphasize significant changes |
| Solid Drawing | Consistent scales, clear relationships |
| Appeal | Satisfying reveals, professional polish |
Principle Applications
Squash & Stretch: Bars can overshoot target height then settle. Pie slices expand slightly on hover. Bubbles compress on collision. Keep total values accurate—animation is transitional.
Anticipation: Brief loading state before data appears. Slight shrink before expansion. Counter briefly pauses before rapid counting. Prepares user for incoming information.
Staging: Reveal data in meaningful sequence—most important first. Highlight active data series. Dim unrelated elements during focus. Guide the data story with motion.
Straight Ahead vs Pose to Pose: Real-time streaming data animates continuously (straight ahead). Dashboard snapshots transition between states (pose to pose). Match approach to data nature.
Follow Through & Overlapping: Data points enter with staggered timing. Labels settle after their data elements. Grid lines appear before data. Legends animate with slight delay.
Slow In / Slow Out: Value changes ease smoothly—no jarring jumps. Use d3.easeCubicInOut or equivalent. Counter animations accelerate then decelerate. Progress bars ease to completion.
Arc: Pie charts sweep clockwise from 12 o'clock. Sankey diagram flows follow curved paths. Network graphs use force-directed arcs. Radial charts expand from center.
Secondary Action: Tooltips follow data point movement. Value labels count up as bars grow. Axis tick marks respond to scale changes. Shadows indicate data depth.
Timing: Initial entry: 300-500ms staggered. Data updates: 200-300ms. Hover states: 100-150ms. Filter transitions: 400-600ms. Slower timing aids comprehension.
Exaggeration: Significant changes deserve attention—pulse or glow outliers. Threshold crossings trigger emphasis. Anomalies animate more dramatically. Don't exaggerate the data itself.
Solid Drawing: Maintain consistent scales during animation. Transitions shouldn't distort data relationships. Preserve axis alignment. Visual hierarchy must remain clear throughout motion.
Appeal: Data entry should feel satisfying. Professional, purposeful motion builds trust. Avoid gratuitous animation—every motion should aid understanding.
Code Patterns
D3.js
// Staggered bar entry with easing
bars.transition()
.duration(500)
.delay((d, i) => i * 50)
.ease(d3.easeCubicOut)
.attr("height", d => yScale(d.value))
.attr("y", d => height - yScale(d.value));
// Smooth data updates
bars.transition()
.duration(300)
.ease(d3.easeCubicInOut)
.attr("height", d => yScale(d.value));
Chart.js
// Animation configuration
options: {
animation: {
duration: 500,
easing: 'easeOutQuart',
delay: (context) => context.dataIndex * 50
}
}
Data Type Timing
| Visualization | Entry | Update | Hover |
|---|---|---|---|
| Bar chart | 400ms stagger | 300ms | 100ms |
| Line chart | 600ms draw | 400ms | 150ms |
| Pie chart | 500ms sweep | 300ms | 100ms |
| Scatter plot | 300ms stagger | 200ms | 100ms |
| Dashboard | 500-800ms cascade | 300ms | 150ms |
Accessibility Note
Always respect prefers-reduced-motion. Data visualization animation should aid comprehension, not hinder it. Provide instant-state fallback for users who disable motion.
Gives 0 of the 12 instructions most data analysis skills give
Counted across 286 of the 286 authors here whose files we hold, read 2026-08-06
- use excel formulas instead of hardcoded calculated valuesin 35 of 286, across 7 files
- match existing template conventions when modifying filesin 35 of 286, across 7 files
- document sources for all hardcoded valuesin 35 of 286, across 7 files
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- format years as text stringsin 30 of 286, across 3 files
- recalculate formulas using recalc.py after modificationsin 30 of 286, across 3 files
- format negative numbers using parenthesesin 30 of 286, across 3 files
- fix all identified formula errors before finishingin 27 of 286, across 1 file
- use colorblind-safe palettesin 19 of 286, across 12 files
- Name tests after the prevented bugin 13 of 286, across 8 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.