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:
Confirm the target platform and WebGPU support expectations. Propose a resource layout (buffers, textures, bind groups) with a simple data model. Sketch the pipeline graph (compute vs render passes) and dependencies. Provide minimal working code and scale up with performance constraints. 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.