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Typegpu

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boraoztunc
typegpu

TypeGPU and raw WebGPU adapter patterns for HyperFrames. Use when creating GPU-rendered compositions with TypeGPU, raw WebGPU, WGSL fragment shaders, compute pipelines, liquid glass effects, particle systems, or any canvas layer driven by navigator.gpu that responds to HyperFrames hf-seek events.

Overview

Publisherboraoztunc
Repositoryskills
Skill nametypegpu
Stars
422
Forks
63
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by boraoztunc on GitHub. Read the source before you install it.

Installation

Install the Typegpu AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/boraoztunc/skills/tree/main/typegpu
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/boraoztunc/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/typegpu .claude/skills/typegpu
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Typegpu in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Typegpu on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Typegpu is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

TypeGPU / WebGPU for HyperFrames

HyperFrames supports TypeGPU and raw WebGPU through its typegpu runtime adapter. The adapter does not own your pipeline. It publishes HyperFrames time and dispatches a seek event so your composition can render the exact GPU frame.

Contract

  • Initialize WebGPU asynchronously (await navigator.gpu.requestAdapter()), but register all GSAP tweens synchronously — before any await. The HyperFrames player reads the timeline immediately at page load.
  • Render from HyperFrames time, not performance.now().
  • Listen for the hf-seek event and re-render at exactly that time.
  • Guard against environments where WebGPU is unavailable — the adapter does not check for you.
  • For video renders, call await device.queue.onSubmittedWorkDone() after submitting GPU work to ensure the canvas is flushed before the frame is captured.

The adapter sets window.__hfTypegpuTime and dispatches new CustomEvent("hf-seek", { detail: { time } }) on each seek.

Basic Pattern

html
<canvas id="gpu-layer"></canvas>
<script>
  (async () => {
    if (!navigator.gpu) return;
    const adapter = await navigator.gpu.requestAdapter();
    if (!adapter) return;
    const device = await adapter.requestDevice();
    const canvas = document.getElementById("gpu-layer");
    canvas.width = 1920;
    canvas.height = 1080;
    const ctx = canvas.getContext("webgpu");
    const fmt = navigator.gpu.getPreferredCanvasFormat();
    ctx.configure({ device, format: fmt, alphaMode: "opaque" });

    // Build your pipeline, buffers, bind groups...
    const timeUniform = new Float32Array([0]);
    const timeBuf = device.createBuffer({
      size: 16,
      usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST,
    });

    function render(t) {
      timeUniform[0] = t;
      device.queue.writeBuffer(timeBuf, 0, timeUniform);
      const enc = device.createCommandEncoder();
      const pass = enc.beginRenderPass({
        colorAttachments: [
          {
            view: ctx.getCurrentTexture().createView(),
            loadOp: "clear",
            clearValue: { r: 0, g: 0, b: 0, a: 1 },
            storeOp: "store",
          },
        ],
      });
      pass.setPipeline(pipeline);
      pass.setBindGroup(0, bindGroup);
      pass.draw(3);
      pass.end();
      device.queue.submit([enc.finish()]);
    }

    render(0);
    window.addEventListener("hf-seek", (e) => render(e.detail.time));
  })();
</script>

Timeline Registration

GSAP tweens that drive text, captions, or HTML elements must be registered synchronously — before any await:

js
const tl = gsap.timeline({ paused: true });

// Caption tweens: synchronous, added before WebGPU init
gsap.set(".cap", { opacity: 0 });
tl.to("#cap-1", { opacity: 1, duration: 0.3 }, 1.0);
tl.to("#cap-1", { opacity: 0, duration: 0.2 }, 3.5);

window.__timelines["my-comp"] = tl;

// GPU-dependent tweens can go inside the async IIFE
(async () => {
  // ... WebGPU init ...
  const proxy = { value: 0 };
  tl.to(proxy, { value: 1, duration: 2, onUpdate: render }, 0.5);
})();

Video-Backed Effects (Liquid Glass, Distortion)

To use a <video> as the GPU input texture:

js
const videoEl = document.getElementById("aroll");

// Wait for video metadata before creating the texture
await new Promise((r) => {
  if (videoEl.readyState >= 1) r();
  else videoEl.addEventListener("loadedmetadata", r, { once: true });
});

// Create texture at the video's NATIVE resolution
const vw = videoEl.videoWidth,
  vh = videoEl.videoHeight;
const bgTex = device.createTexture({
  size: [vw, vh],
  format: "rgba8unorm",
  usage:
    GPUTextureUsage.COPY_DST | GPUTextureUsage.TEXTURE_BINDING | GPUTextureUsage.RENDER_ATTACHMENT,
});

function render(t) {
  try {
    device.queue.copyExternalImageToTexture({ source: videoEl }, { texture: bgTex }, [vw, vh]);
  } catch (_) {
    /* frame not decoded yet */
  }
  // ... draw ...
}

Render-mode caveat: headless Chrome may fail copyExternalImageToTexture for video elements. For production renders, pre-extract key frames via FFmpeg as PNGs and load them as image textures instead.

Frosted Blur via Downsample Pass

A single-pass Gaussian kernel is too weak for glass-like frosted blur. Use a two-pass approach:

  1. Pass 1 — Downsample: render the full-res texture to a small texture (1/6 resolution). Bilinear filtering during the downsample naturally averages pixels.
  2. Pass 2 — Glass composite: sample the small texture for the frosted interior (bilinear upscale = heavy smooth blur) and the full-res texture for sharp areas and chromatic refraction.

This matches TypeGPU's textureSampleBias mip-level approach without generating mipmaps.

Transparent vs Opaque Canvas

  • alphaMode: 'opaque' — the GPU canvas renders the full frame (video + effect). Use when the GPU pipeline handles all visual content.
  • alphaMode: 'premultiplied' — the GPU canvas is transparent where alpha = 0, letting HTML elements below show through. Use for overlays (particles, path animations) on top of a regular <video> element.

WGSL Full-Screen Triangle

The standard vertex shader for full-screen effects (no vertex buffer needed):

wgsl
struct Vo { @builtin(position) pos: vec4f, @location(0) uv: vec2f }

@vertex fn vs(@builtin(vertex_index) vi: u32) -> Vo {
  let ps = array<vec2f, 3>(vec2f(-1., -1.), vec2f(3., -1.), vec2f(-1., 3.));
  let ts = array<vec2f, 3>(vec2f(0., 1.), vec2f(2., 1.), vec2f(0., -1.));
  return Vo(vec4f(ps[vi], 0., 1.), ts[vi]);
}

Draw with pass.draw(3) — one triangle that covers the viewport.

Rounded-Rect SDF (Liquid Glass Pill)

wgsl
fn sdf_box(p: vec2f, half_size: vec2f, corner_radius: f32) -> f32 {
  let d = abs(p) - half_size + vec2f(corner_radius);
  return length(max(d, vec2f(0.))) + min(max(d.x, d.y), 0.) - corner_radius;
}

Use this to define inside/ring/outside zones for glass effects. Negative values are inside the shape.

Deterministic Rendering

  • No Math.random() — use a seeded PRNG.
  • No requestAnimationFrame for the render loop — render only in response to hf-seek.
  • No performance.now() for animation time — read window.__hfTypegpuTime or e.detail.time.
  • After GPU submit, call await device.queue.onSubmittedWorkDone() for render-mode frame capture.

Frequently asked questions

What does the Typegpu AI skill do?

TypeGPU and raw WebGPU adapter patterns for HyperFrames. Use when creating GPU-rendered compositions with TypeGPU, raw WebGPU, WGSL fragment shaders, compute pipelines, liquid glass effects, particle systems, or any canvas layer driven by navigator.gpu that responds to HyperFrames hf-seek events.

Why use Typegpu on TypingMind?

Because you install it once and use it with any model. Typegpu is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Typegpu in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/boraoztunc/skills/tree/main/typegpu. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Typegpu?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Typegpu?

As many as you like. As long as a model supports skills, you can use Typegpu with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Typegpu AI skill free?

Yes. It is published on GitHub by boraoztunc under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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