smithery/neversight

webgpu

Build WebGPU render and compute pipelines with portable best practices.

Installation

$ npx skills add smithery/neversight --skill webgpu

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Skill metadata

Parsed from SKILL.md frontmatter.

Version0.1.0
LicenseMIT

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,310 B
  • docs SUMMARY.md 85 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

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.