The recommended way to load GLTF/GLB models.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionr3f-loadersExecute the skills CLI command in your project's root directory to begin installation:
Fetches r3f-loaders from enzed/r3f-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 r3f-loaders. Access via /r3f-loaders 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.
Submit your Claude Code skill and start earning
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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import { Canvas } from '@react-three/fiber'
import { useGLTF, OrbitControls } from '@react-three/drei'
import { Suspense } from 'react'
function Model() {
const { scene } = useGLTF('/models/robot.glb')
return <primitive object={scene} />
}
export default function App() {
return (
<Canvas>
<ambientLight />
<Suspense fallback={null}>
<Model />
</Suspense>
<OrbitControls />
</Canvas>
)
}
The recommended way to load GLTF/GLB models.
import { useGLTF } from '@react-three/drei'
function Model() {
const gltf = useGLTF('/models/robot.glb')
// gltf contains:
// - scene: THREE.Group (the main scene)
// - nodes: Object of named meshes
// - materials: Object of named materials
// - animations: Array of AnimationClip
return <primitive object={gltf.scene} />
}
function Model() {
const { nodes, materials } = useGLTF('/models/robot.glb')
return (
<group>
{/* Use specific meshes */}
<mesh
geometry={nodes.Body.geometry}
material={materials.Metal}
position={[0, 0, 0]}
/>
<mesh
geometry={nodes.Head.geometry}
material={materials.Plastic}
position={[0, 1, 0]}
/>
</group>
)
}
Generate typed components using gltfjsx:
npx gltfjsx model.glb --types
// Generated component
import { useGLTF } from '@react-three/drei'
import { GLTF } from 'three-stdlib'
type GLTFResult = GLTF & {
nodes: {
Body: THREE.Mesh
Head: THREE.Mesh
}
materials: {
Metal: THREE.MeshStandardMaterial
Plastic: THREE.MeshStandardMaterial
}
}
export function Model(props: JSX.IntrinsicElements['group']) {
const { nodes, materials } = useGLTF('/model.glb') as GLTFResult
return (
<group {...props} dispose={null}>
<mesh geometry={nodes.Body.geometry} material={materials.Metal} />
<mesh geometry={nodes.Head.geometry} material={materials.Plastic} />
</group>
)
}
useGLTF.preload('/model.glb')
import { useGLTF } from '@react-three/drei'
function Model() {
// Drei automatically handles Draco if the file is Draco-compressed
const { scene } = useGLTF('/models/compressed.glb')
return <primitive object={scene} />
}
// Or specify Draco decoder path
useGLTF.setDecoderPath('https://www.gstatic.com/draco/versioned/decoders/1.5.6/')
import { useGLTF } from '@react-three/drei'
// Preload at module level
useGLTF.preload('/models/robot.glb'✓Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ 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.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.6★★★★★75 reviews- LLiam Rahman★★★★★Dec 28, 2024
Useful defaults in r3f-loaders — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- CCarlos Rao★★★★★Dec 24, 2024
r3f-loaders is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- CChaitanya Patil★★★★★Dec 20, 2024
r3f-loaders is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- NNia Okafor★★★★★Dec 20, 2024
We added r3f-loaders from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- CCarlos Patel★★★★★Dec 16, 2024
I recommend r3f-loaders for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- CCarlos Rahman★★★★★Dec 16, 2024
r3f-loaders reduced setup friction for our internal harness; good balance of opinion and flexibility.
- DDiya Ghosh★★★★★Dec 12, 2024
r3f-loaders reduced setup friction for our internal harness; good balance of opinion and flexibility.
- HHarper Huang★★★★★Dec 12, 2024
r3f-loaders has been reliable in day-to-day use. Documentation quality is above average for community skills.
- DDiya Lopez★★★★★Nov 23, 2024
We added r3f-loaders from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- CCamila Haddad★★★★★Nov 19, 2024
I recommend r3f-loaders for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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