cazala

webgpu

6
0
# Install this skill:
npx skills add cazala/webgpu-skill

Or install specific skill: npx add-skill https://github.com/cazala/webgpu-skill

# Description

Build WebGPU render and compute pipelines with portable best practices.

# SKILL.md


name: webgpu
description: Build WebGPU render and compute pipelines with portable best practices.
version: 0.1.0
license: MIT
tags: [webgpu, wgsl, gpu, shaders, compute, rendering, performance]


WebGPU Skill

This skill helps any agent design, implement, and debug WebGPU applications and GPU compute pipelines. It is framework-agnostic and focuses on reusable WebGPU/WGSL patterns.

What this skill covers

  • WebGPU initialization, device setup, and surface configuration
  • Compute pipelines, workgroup sizing, and storage buffer layout
  • Render pipelines, render passes, and post-processing patterns
  • GPU/CPU synchronization and safe readback strategies
  • Performance and debugging practices
  • Architecture patterns: modular passes, phase-based simulation, and capability handling
  • Use cases: rendering, compute, ML training/inference, grid simulations, and systems modeling

Core principles

  • Choose a capability strategy: fallback runtime, reduced mode, or fail fast.
  • Avoid full GPU readbacks in hot paths; use localized queries or small readback buffers.
  • Structure simulation with phases (state, apply, integrate, constrain, correct) to keep WGSL cohesive.
  • Use spatial grids or other spatial indexing for neighbor queries and high particle counts.
  • Build modular passes so render and compute stages stay composable and testable.

How to use this skill

When asked to build a WebGPU feature:

  1. Confirm the target platform and WebGPU support expectations.
  2. Propose a resource layout (buffers, textures, bind groups) with a simple data model.
  3. Sketch the pipeline graph (compute vs render passes) and dependencies.
  4. Provide minimal working code and scale up with performance constraints.
  5. Choose a capability strategy when WebGPU is unavailable.

Deliverable checklist

  • Clean WebGPU init and error handling
  • A buffer layout with alignment notes (16-byte struct alignment for WGSL)
  • A pass graph with clear read/write ownership (ping-pong textures if needed)
  • Explicit notes on readback and when it is safe
  • Optional fallback or reduced mode for critical functionality

Quick reference

See REFERENCE.md for a compact WebGPU cheat sheet and docs/ for deeper patterns, including docs/use-cases.md and docs/simulation-patterns.md.

# README.md

WebGPU Skill

This repository contains a WebGPU skill. Skills are reusable capabilities for AI agents, packaged so they can be installed and reused across different tools and workflows. This one is framework-agnostic and focuses on reusable WebGPU/WGSL patterns, orchestration, and performance guidance across a wide range of GPU workloads.

Supported agents

This skill is designed to work with the major agents that support skills, including Claude Code, Codex, Cursor, Cline, and GitHub Copilot, plus other compatible agents listed by the skills ecosystem.

Contents

  • SKILL.md: Skill metadata and overview
  • REFERENCE.md: Quick reference for core WebGPU patterns
  • docs/: Concept guides and patterns
  • examples/: Small runnable snippets
  • templates/: Starter templates

Highlights

  • Compute + render pipeline patterns
  • Orchestration and phase-based simulation guidance
  • Practical performance notes and debugging tips
  • Uniform packing and resource layout advice

Installation

Install with the CLI:

npx skills add cazala/webgpu-skill

Refer to SKILL.md for the full entry point.

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.