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Praneeth Kawya Thathsara
Praneeth Kawya Thathsara

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I Built a Rive Skill for AI Coding Agents — SKILL.md for Codex, ChatGPT, Cursor & Gemini

#rive #ai #opensource #webdev

I Built an Open-Source Rive Skill for AI Coding Agents

AI coding agents are getting much better at building apps.

But there is still a problem when the task involves Rive.

You can ask an AI tool to:

  • integrate a .riv file
  • create State Machine logic
  • connect runtime inputs
  • work with Data Binding
  • debug an animation
  • optimize a Rive component
  • prepare a developer handoff

and the answer may look convincing.

But Rive evolves quickly.

An AI model may remember:

  • an older runtime API
  • outdated SDK syntax
  • old State Machine patterns
  • incomplete Data Binding behavior
  • a renderer option that changed
  • generic animation advice that does not match production Rive workflows

That is why I created:

Rive Skill

Production-ready Rive guidance for AI coding agents

👉 GitHub:
https://github.com/uianimation/rive-skill

👉 Download SKILL.md directly:
https://github.com/uianimation/rive-skill/raw/refs/heads/main/SKILL.md

The goal is simple:

Give AI agents a maintainable Rive-specific instruction layer instead of forcing them to rely only on generic model knowledge.

Why AI Agents Need Rive-Specific Guidance

Rive is not just another animation file format.

A production Rive project can include:

Artboards
Timelines
Bones
Meshes
Constraints
Components
Layouts
State Machines
Listeners
Events
Data Binding
View Models
Converters
Nested Artboards
Runtime APIs
Renderers
Scripting
Rive MCP
Accessibility
Performance Constraints
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That is a lot of system behavior for an AI coding assistant to reason about correctly.

And the problem becomes more serious when an animation moves from:

Looks good in the Rive Editor
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to:

Works correctly in the actual product
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Those are not the same thing.

The Real Problem: Rive Handoff Is a System Problem

A Rive animator can create a beautiful animation.

A developer can have the correct SDK installed.

And the integration can still fail.

Why?

Because production Rive work needs a clear contract between:

Designer
   ↓
Rive Animator
   ↓
.riv Architecture
   ↓
Developer
   ↓
Runtime
   ↓
Application State
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For example, imagine a voice assistant character.

The .riv file may contain:

Idle
Listening
Thinking
Speaking
Success
Error
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The application may need inputs such as:

activity
emotion
isSpeaking
audioLevel
lookX
lookY
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If the AI coding agent does not understand that contract, it may try to solve the problem by:

  • swapping animations manually
  • creating unnecessary state logic
  • assuming input names
  • resetting the State Machine incorrectly
  • controlling visual layers directly from application code
  • rebuilding logic already handled inside Rive

That creates fragile integrations.

What the Rive Skill Does

The project organizes Rive guidance into three main areas.

1. Authoring

Guidance around:

Artboards
Timelines
Rigging
Components
Layouts
Assets
Naming
Structure
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The goal is to help an AI agent reason about how the .riv file itself should be organized.

2. Interaction

Guidance around:

State Machines
Listeners
Data Binding
View Models
Converters
Runtime-controlled values
Interaction architecture
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This is where Rive becomes more than a linear animation.

Instead of thinking:

Play animation A
then animation B
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the agent can think in terms of:

Application State
      ↓
Rive Inputs / Data
      ↓
Character / UI Behavior
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3. Integration

Guidance around:

Runtimes
Renderers
Scripting
Rive MCP
Performance
Accessibility
Developer Handoff
Debugging
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This is particularly important because most real Rive problems happen outside the animation timeline.

Why I Did Not Put Everything in One Huge SKILL.md

One large instruction file sounds convenient.

But it creates another problem:

context overload.

An AI agent working on a Flutter runtime bug does not need every detail about character rigging.

An agent helping with a Rive character rig does not necessarily need the full web renderer discussion.

So the repository uses focused references.

The structure currently looks like this:

rive-skill/
├── SKILL.md
├── agents/
│   └── openai.yaml
├── assets/
│   ├── rive-skill-icon.svg
│   └── rive-skill-community-cover.png
├── evals/
│   └── behavioral-cases.md
├── references/
│   ├── editor-authoring.md
│   ├── interaction-data.md
│   ├── scripting-ai-mcp.md
│   ├── runtimes-performance-accessibility.md
│   ├── runtime-integration-patterns.md
│   ├── runtime-handoff-checklist.md
│   └── official-docs-map.md
├── scripts/
│   ├── build-portable.ps1
│   └── validate-skill.ps1
└── rive-instructions.md
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This means the main skill can stay relatively focused while deeper guidance remains available when needed.

SKILL.md vs rive-instructions.md

The repository includes two useful ways to consume the guidance.

SKILL.md

Best for tools that support agent skills directly.

For example:

Codex
Agent Skills-compatible clients
Agent workflows with skill discovery
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rive-instructions.md

Best for prompt-based workflows.

For example:

ChatGPT
Cursor
Gemini
Other AI coding assistants
Custom agents
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The portable file bundles the deeper references so the model does not lose important specialist instructions.

Install the Rive Skill in Codex

For a personal skill available across projects:

cp -r rive-skill ~/.codex/skills/rive
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For a project-specific installation:

cp -r rive-skill .codex/skills/rive
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Then invoke it explicitly as:

$rive
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or allow Codex to select it when the request matches the skill description.

Example: Without a Rive Skill

Imagine prompting an AI coding agent:

My Rive animation is not responding correctly in React.
Fix the State Machine integration.
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Without enough project context, the agent might immediately start rewriting runtime code.

But the actual problem could be:

Wrong artboard name
Wrong State Machine name
Wrong input type
Wrong input path
Default state mismatch
Nested input path issue
Data Binding mismatch
Old runtime syntax
Missing event listener
Incorrect lifecycle handling
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The correct debugging process is not:

Guess
↓
Rewrite
↓
Try again
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It should be closer to:

Verify Runtime
↓
Verify Asset
↓
Verify Artboard
↓
Verify State Machine
↓
Verify Inputs
↓
Verify Runtime Contract
↓
Verify Application State
↓
Only then change implementation
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This debugging discipline is one of the things the skill is designed to encourage.

Example: AI Character State Machine

Suppose an app contains an interactive Rive mascot.

The runtime contract might be:

Artboard:
Mascot

State Machine:
MascotController
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Inputs:

activity
0 = idle
1 = listening
2 = thinking
3 = speaking

emotion
0 = neutral
1 = happy
2 = concerned

isPending
boolean

audioLevel
0.0 → 1.0
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A weak implementation may create application code like:

if speaking:
    play speaking animation

if happy:
    play happy animation
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But that can conflict immediately.

What if the character is:

Speaking + Happy + Looking Left
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A better Rive architecture allows independent systems to coexist.

Conceptually:

Activity → body state
Emotion → expression
AudioLevel → speaking intensity
LookX/Y → eye/head direction
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The application sends data.

Rive decides how the animation behaves.

State Machines Are Not Just Animation Playlists

This distinction is important.

A Rive State Machine should not always be treated as:

Animation A
→ Animation B
→ Animation C
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It can represent interactive product logic.

For example:

Button
├── Default
├── Hover
├── Press
├── Loading
├── Success
└── Error
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Or a mascot:

Mascot
├── Idle
├── Listening
├── Thinking
├── Speaking
├── Celebrate
├── Sleep
└── Error
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Or an onboarding component:

Step
├── Locked
├── Available
├── Active
└── Complete
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The AI agent needs to understand the animation as a component, not simply as a video.

Why Runtime Contracts Matter

One of the most useful things you can give a developer is a small contract.

For example:

FILE
assistant.riv

ARTBOARD
Assistant

STATE MACHINE
AssistantController

INPUTS

activity: number
0 idle
1 listening
2 thinking
3 speaking

isPending: boolean

confirmed: trigger
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Then document:

DEFAULT STATE
Idle

EXPECTED RESTING APPEARANCE
Blue

LOOPING STATES
Idle
Listening
Pending

ONE-SHOT STATES
Confirmed
Success
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This tiny document can save hours of back-and-forth.

The Rive Skill includes developer-handoff guidance because animation architecture and integration architecture are connected.

Semantic Naming Matters More Than It Looks

Compare this:

Animation 1
Animation 2
Boolean 1
Trigger
Shape 43
Group 21
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with:

Idle
Listening
isPending
triggerConfirmed
Mouth
Head
LeftArm
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The second version is not just easier for humans.

It is also easier for AI agents.

AI tools perform better when your project exposes semantic structure.

Meaningful names improve:

Debugging
Documentation
Runtime integration
AI-assisted coding
Future maintenance
Team handoff
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AI Makes Good Naming Even More Important

Before AI coding agents, bad naming mostly frustrated developers.

Now it also reduces the quality of machine reasoning.

For example:

input1 = true
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tells an AI almost nothing.

But:

isPending = true
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provides intent.

Likewise:

triggerConfirmed.fire()
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contains much more useful semantic information than:

trigger3.fire()
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As software development becomes increasingly AI-assisted, semantic animation architecture becomes more valuable.

Rive Data Binding and View Models

Modern Rive workflows increasingly involve application data, not only animation triggers.

A character may need values like:

username
progress
theme
level
emotion
avatarVariant
notificationCount
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Instead of hardcoding visual decisions in application code, those values can participate in a more structured Rive data architecture.

The skill therefore includes guidance around:

Data Binding
View Models
View Model Instances
Converters
Runtime-controlled properties
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This is one of the areas where generic AI answers can become outdated quickly, so version-sensitive behavior should be verified against current Rive documentation.

Why Freshness Matters

A static AI answer has a hidden problem:

It may have been correct once.
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Rive runtimes change.

Documentation changes.

Feature support changes.

APIs change.

Platform behavior changes.

That means a production-focused skill should distinguish between:

Stable architectural guidance
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and:

Version-sensitive technical claims
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The Rive Skill includes an official documentation map and uses a research date so current behavior can be checked instead of blindly trusting remembered APIs.

Rive MCP and AI-Assisted Workflows

Another interesting direction is Rive MCP.

As design and development tools expose more structured interfaces to AI agents, an agent may eventually be able to inspect more of a Rive project directly instead of reasoning only from screenshots and descriptions.

That creates workflows like:

Developer Request
      ↓
AI Agent
      ↓
Inspect Rive Structure
      ↓
Understand State Machine
      ↓
Check Runtime Contract
      ↓
Suggest / Apply Integration
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But access to tools does not automatically mean good decisions.

An AI agent still needs rules about:

What to inspect first
What should not be changed
Which contract must be preserved
What requires current documentation
What counts as verified evidence
What should be handed back to the developer
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That is another reason I think skill files are useful.

Validation Matters Too

The repository does not rely only on manually reading the skill.

It also includes deterministic validation.

You can run:

./scripts/build-portable.ps1
./scripts/validate-skill.ps1
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And optionally include live documentation-link checks:

./scripts/validate-skill.ps1 -CheckExternalLinks
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The validation workflow checks things such as:

Frontmatter
Reference routing
Required resources
Agent metadata
Portable-file drift
Behavioral evaluation configuration
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That matters because instruction repositories can slowly become inconsistent as they grow.

Behavioral Evaluations

There is also a behavioral test manifest.

The idea is to test whether the agent makes the right kind of decision in situations such as:

No tool access
Partial MCP failure
Runtime uncertainty
Contract preservation
Current documentation verification
Evidence labeling
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This is different from testing whether the model can memorize one API.

The goal is to test whether it behaves safely and usefully when working on a real Rive project.

The Skill Is Open Source

The project is released under the MIT License.

That means developers, animators and AI-tool builders can inspect it, adapt it and contribute improvements.

Repository:

👉 github.com/uianimation/rive-skill

Direct SKILL.md:

👉 Download SKILL.md

If you work with Rive regularly, starring the repository also makes it easier to find again as the guidance evolves.

Who This Is For

I think this project is useful for several groups.

Rive Animators

Especially if you hand .riv files to developers and want AI coding tools to understand your file structure correctly.

Developers

If you use:

React
Flutter
React Native
Web
iOS
Android
Game engines
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and regularly integrate Rive assets.

AI Coding Agent Users

Especially developers using:

Codex
ChatGPT
Cursor
Gemini
Custom coding agents
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for Rive-related implementation work.

Teams Building Interactive Mascots

Characters usually involve more complex contracts:

Idle
Expressions
Gestures
Lip Sync
Reactions
Progress
State Machines
Data Binding
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Structured guidance becomes even more useful there.

What I Want This Project to Become

I do not want this repository to become a giant collection of copied Rive documentation.

Rive already has documentation.

The useful layer is:

Official Rive Documentation
          +
Production Experience
          +
Architecture Rules
          +
Debugging Order
          +
AI Agent Instructions
          =
Rive Skill
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The skill should tell the AI:

What should I think about?

What should I verify?

What should I preserve?

Where should I look for current information?

What should a production-ready result contain?

That is more useful than duplicating every SDK reference.

Example Prompts to Try

After adding the skill to your AI workflow, you could try prompts like:

$rive Review this Rive React integration and identify why the State Machine input is not updating.
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$rive Design a runtime contract for this interactive mascot before I hand the .riv file to the Flutter developer.
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$rive Review my artboard, State Machine and input naming strategy for developer handoff.
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$rive Help me structure a Rive character with idle, listening, thinking, speaking and emotion layers without creating state explosion.
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$rive Diagnose this Rive integration without changing the existing runtime contract.
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$rive Check whether this Rive API example is still current before recommending it.
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Rive + AI Is Bigger Than Code Generation

The interesting part is not simply:

AI can write Rive integration code.

The larger opportunity is that AI can potentially help across the whole workflow:

Brief
↓
Architecture
↓
Character / UI Structure
↓
Rigging Decisions
↓
Interaction Design
↓
State Machine Design
↓
Runtime Contract
↓
Integration
↓
Debugging
↓
Optimization
↓
QA
↓
Handoff
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But to do that reliably, the agent needs domain-specific context.

That is what this project is experimenting with.

Need a Human Rive Animator Too?

AI can help developers reason about Rive implementation.

But there are still projects where you need someone to actually design, rig and animate the asset.

If you need:

  • Rive character animation
  • interactive mascots
  • character rigging
  • UI animation
  • State Machines
  • responsive Rive components
  • Data Binding setup
  • developer-ready .riv handoff
  • debugging and optimization

you can also visit:

👉 riveanimator.com

The goal there is production-ready Rive work that can actually move into development rather than stopping at a visual demo.

Contributing

If you find something outdated, incomplete or incorrect, contributions are welcome.

Good contributions should ideally:

Solve a concrete Rive problem
Reference current official Rive information
Avoid duplicating existing rules
Improve AI decision-making
Improve production handoff
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Repository:

github.com/uianimation/rive-skill

Final Thoughts

AI coding agents are becoming part of everyday software development.

That means the quality of the instructions we give them matters.

For Rive, I do not think the best solution is to ask a general-purpose model to remember every runtime detail forever.

A better approach is:

Strong base model
+
Current official documentation
+
Domain-specific skill
+
Clear runtime contract
+
Good semantic naming
+
Verification
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That combination can make AI-assisted Rive work much more reliable.

If you use Rive with Codex, ChatGPT, Cursor, Gemini or another coding agent, try the project and let me know what is missing.

⭐ GitHub:
https://github.com/uianimation/rive-skill

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