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Data Visualization

Community
dylantarre
data-visualization

Use when animating charts, graphs, dashboards, data transitions, or any information visualization work.

Overview

Publisherdylantarre
Repositoryanimation-principles
Skill namedata-visualization
Stars
84
Forks
12
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by dylantarre on GitHub. Read the source before you install it.

Installation

Install the Data Visualization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dylantarre/animation-principles.git /tmp/animation-principles
mkdir -p .claude/skills
cp -r /tmp/animation-principles/skills/01-by-domain/data-visualization .claude/skills/data-visualization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Visualization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Visualization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Visualization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Data Visualization Animation

Apply Disney's 12 animation principles to charts, graphs, dashboards, and information displays.

Quick Reference

PrincipleData Viz Implementation
Squash & StretchBar overshoot, elastic settling
AnticipationBrief pause before data loads
StagingSequential reveal, focus hierarchy
Straight Ahead / Pose to PoseStreaming vs snapshot data
Follow Through / OverlappingStaggered element entry
Slow In / Slow OutSmooth value interpolation
ArcPie chart sweeps, flow diagrams
Secondary ActionLabels following data points
TimingEntry 300-500ms, updates 200-300ms
ExaggerationEmphasize significant changes
Solid DrawingConsistent scales, clear relationships
AppealSatisfying 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

javascript
// 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

javascript
// Animation configuration
options: {
    animation: {
        duration: 500,
        easing: 'easeOutQuart',
        delay: (context) => context.dataIndex * 50
    }
}

Data Type Timing

VisualizationEntryUpdateHover
Bar chart400ms stagger300ms100ms
Line chart600ms draw400ms150ms
Pie chart500ms sweep300ms100ms
Scatter plot300ms stagger200ms100ms
Dashboard500-800ms cascade300ms150ms

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.

Frequently asked questions

What does the Data Visualization AI skill do?

Use when animating charts, graphs, dashboards, data transitions, or any information visualization work.

Why use Data Visualization on TypingMind?

Because you install it once and use it with any model. Data Visualization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Visualization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dylantarre/animation-principles/tree/main/skills/01-by-domain/data-visualization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Visualization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Visualization?

As many as you like. As long as a model supports skills, you can use Data Visualization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Visualization AI skill free?

Yes. It is published on GitHub by dylantarre under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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