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Build WebGPU render and compute pipelines with portable best practices.
--- 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`.--- 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`.
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