---
name: webgpu-7cc5a7647148
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`.



---
Source attribution: NeverSight
Original project: https://github.com/NeverSight/skills_feed/blob/8db54595422f40924a0e2c3365b2f40e9aba0ad4/data/skills-md/cazala/webgpu-skill/webgpu/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: NeverSight
Original project: https://github.com/NeverSight/skills_feed/blob/8db54595422f40924a0e2c3365b2f40e9aba0ad4/data/skills-md/cazala/webgpu-skill/webgpu/SKILL.md
Attribution basis: Source repository record

Fields of work: Development & Engineering
Roles: Frontend Developer, Backend Developer, ML / AI Engineer
Specialties: Backend
Tools: Webgpu
Country applicability: Country not specified
Classification basis: Source category, role and specialization labels
Instruction SHA-256: 4921fc020a102e60f41ae8368061ea833cae5279ebbfbffcd333108d6157f508