A technical resource for diagnosing and resolving WebGL performance bottlenecks like low FPS and memory leaks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install webgl-perf-debug
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install webgl-perf-debug using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is WebGL Performance Debugger?
The WebGL Performance Debugger is a specialized guide within the Openclaw Skills ecosystem designed to help developers optimize 3D web applications. It focuses on identifying high GPU usage, rendering stutters, and memory leaks often found in Three.js or raw WebGL projects. By following the structured profiling and optimization techniques provided, developers can ensure their applications run smoothly at 60 FPS across various hardware configurations.
This resource bridges the gap between raw GPU metrics and actionable code changes. It provides clear patterns for using modern tools like Chrome DevTools alongside programmatic optimizations such as geometry merging and efficient shader writing to ensure peak performance for any Openclaw Skills user working on high-performance graphics.
WebGL Performance Debugger Use Cases
- Three.js scenes dropping below 30 frames per second during user interaction.
- Excessive GPU memory consumption causing browser tab crashes or mobile performance issues.
- Noticeable stuttering or lag during the loading and uploading of large textures to the GPU.
- High Draw Call counts leading to CPU-bound rendering bottlenecks in complex scenes.
How WebGL Performance Debugger Works
- Profiling: Open Chrome DevTools Performance tab and record 5-10 seconds of interaction to analyze Frame, GPU, and JS execution times.
- Metric Evaluation: Compare captured Draw Calls, Triangle counts, and GPU times against established health thresholds.
- Texture Optimization: Implement Basis Universal or KTX2 compression to reduce texture memory footprint by 50-75%.
- Draw Call Reduction: Batch objects using geometry merging or use InstancedMesh for repetitive objects to minimize CPU-to-GPU overhead.
- Shader Optimization: Refactor GLSL code to avoid expensive branches (if-else) by using mix and step functions.
- Memory Management: Implement strict disposal patterns for geometries and materials to prevent cumulative memory leaks.
WebGL Performance Debugger Setup
To monitor and debug WebGL performance within your development environment, integrate the following monitoring snippets into your Three.js project:
# Ensure Three.js is part of your project dependencies
npm install three
// Monitor GPU stats in real-time within your render loop
function animate() {
requestAnimationFrame(animate);
renderer.render(scene, camera);
// Log performance metrics to console or UI
console.log('Draw Calls:', renderer.info.render.calls);
console.log('Textures in Memory:', renderer.info.memory.textures);
}
WebGL Performance Debugger Data Schema & Taxonomy
The skill organizes WebGL performance data into the following schema to help Openclaw Skills users track application health:
| Metric Category | Target Value | Warning Threshold | Description |
|---|---|---|---|
| Frame Time | < 16ms | > 33ms | Total time to render one frame (target 60fps) |
| GPU Time | < 10ms | > 20ms | Time spent processing tasks on the GPU |
| Draw Calls | < 100/frame | > 300/frame | Total draw commands sent to the GPU per frame |
| Triangles | < 100k/frame | > 500k/frame | Total polygon complexity of the rendered scene |
WebGL Performance Debugger Advanced Features
- Basis Universal Support: Detailed workflow for KTX2 texture loading to drastically reduce VRAM usage.
- Instanced Rendering: Strategies for rendering thousands of identical objects in a single draw call using InstancedMesh.
- Shader Branch Optimization: Advanced GLSL techniques to replace conditional logic with hardware-accelerated mathematical functions.
- GPU Memory Lifecycle: Automated disposal hooks to ensure geometry and material memory is released immediately after removal from the scene.
SKILL.md
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