Build AR experiences on iOS using RealityKit for rendering and ARKit for world
Works with
tracking. Covers RealityView, entity management, raycasting, scene
understanding, and gesture-based interactions. Targets Swift 6.2 / iOS 26+.
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionrealitykit-arExecute the skills CLI command in your project's root directory to begin installation:
Fetches realitykit-ar from dpearson2699/swift-ios-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate realitykit-ar. Access via /realitykit-ar in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Build AR experiences on iOS using RealityKit for rendering and ARKit for world
tracking. Covers RealityView, entity management, raycasting, scene
understanding, and gesture-based interactions. Targets Swift 6.2 / iOS 26+.
NSCameraUsageDescription to Info.plistRealityViewCameraContent (iOS 18+, macOS 15+)AR features require devices with an A9 chip or later. Always verify support before presenting AR UI.
import ARKit
guard ARWorldTrackingConfiguration.isSupported else {
showUnsupportedDeviceMessage()
return
}
| Type | Platform | Role |
|---|---|---|
RealityView |
iOS 18+, visionOS 1+ | SwiftUI view that hosts RealityKit content |
RealityViewCameraContent |
iOS 18+, macOS 15+ | Content displayed through the device camera |
Entity |
All | Base class for all scene objects |
ModelEntity |
All | Entity with a visible 3D model |
AnchorEntity |
All | Tethers entities to a real-world anchor |
RealityView is the SwiftUI entry point for RealityKit. On iOS, it provides
RealityViewCameraContent which renders through the device camera for AR.
import SwiftUI
import RealityKit
struct ARExperienceView: View {
var body: some View {
RealityView { content in
// content is RealityViewCameraContent on iOS
let sphere = ModelEntity(
mesh: .generateSphere(radius: 0.05),
materials: [SimpleMaterial(
color: .blue,
isMetallic: true
)]
)
sphere.position = [0, 0, -0.5] // 50cm in front of camera
content.add(sphere)
}
}
}
Use the update closure to respond to SwiftUI state changes:
struct PlacementView: View {
@State private var modelColor: UIColor = .red
var body: some View {
RealityView { content in
let box = ModelEntity(
mesh: .generateBox(size: 0.1),
materials: [SimpleMaterial(
color: .red,
isMetallic: false
)]
)
box.name = "colorBox"
box.position = [0, 0, -0.5]
content.add(box)
} update: { content in
if let box = content.entities.first(
where: { $0.name == "colorBox" }
) as? ModelEntity {
box.model?.materials = [SimpleMaterial(
color: modelColor,
isMetallic: false
)]
}
}
Button("Change Color") {
modelColor = modelColor == .red ? .green : .red
}
}
}
Load 3D models asynchronously to avoid blocking the main thread:
RealityView { content in
if let robot = try? await ModelEntity(named: "robot") {
robot.position = [0, -0.2, -0.8]
robot.scale = [0.01, 0.01, 0.01]
content.add(robot)
}
}
// Box
let box = ModelEntity(
mesh: .generateBox(size: [0.1, 0.2, 0.1], cornerRadius: 0.005),
materials: [SimpleMaterial(color: .gray, isMetallic: true)]
)
// Sphere
let sphere = ModelEntity(
mesh: .generateSphere(radius: 0.05),
materials: [SimpleMaterial(color: .blue, roughness: 0.2, isMetallic: true)]
)
// Plane
let plane = ModelEntity(
mesh: .generatePlane(width: 0.3, depth: 0.3),
materials: [SimpleMaterial(color: .green, isMetallic: false)]
)
Entities use an ECS (Entity Component System) architecture. Add components to give entities behavior:
let box = ModelEntity(
mesh: .generateBox(size: 0.1),
materials: [SimpleMaterial(color: .red, isMetallic: false)]
)
// Make it respond to physics
box.components.set(PhysicsBodyComponent(
massProperties: .default,
material: .default,
mode: .dynamic
))
// Add collision shape for interaction
box.components.set(CollisionComponent(
shapes: [.generateBox(size: [0.1, 0.1, 0.1])]
))
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Keeps context tight: realitykit-ar is the kind of skill you can hand to a new teammate without a long onboarding doc.
realitykit-ar fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: realitykit-ar is focused, and the summary matches what you get after install.
Registry listing for realitykit-ar matched our evaluation — installs cleanly and behaves as described in the markdown.
realitykit-ar is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
realitykit-ar reduced setup friction for our internal harness; good balance of opinion and flexibility.
realitykit-ar has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in realitykit-ar — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: realitykit-ar is focused, and the summary matches what you get after install.
We added realitykit-ar from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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