data-visualization
Use when animating charts, graphs, dashboards, data transitions, or any information visualization work.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/dylantarre/animation-principles --skill data-visualization- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads data-visualization automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.
FAQ
What does the data-visualization skill do?
Use when animating charts, graphs, dashboards, data transitions, or any information visualization work.
How do I install data-visualization?
Run npx skills add https://github.com/dylantarre/animation-principles --skill data-visualization in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/data-visualization/ (personal) or .claude/skills/data-visualization/ (project).
Which AI agents can use data-visualization?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is data-visualization safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains data-visualization?
It is published by dylantarre in dylantarre/animation-principles. It has 584 recorded installs.
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