Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/alecs5am/ralphy/typegpunpx skills add alecs5am/ralphy --skill typegpugit clone --depth 1 https://github.com/alecs5am/ralphyWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00064 | $0.01718 |
| Opus 5 | $0.00032 | $0.00859 |
| Sonnet 5 | $0.00013 | $0.00344 |
| Haiku 4.5 | $0.00006 | $0.00172 |
Grade A, and why
typegpu scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- typegpu — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 anyawait. The HyperFrames player reads the timeline immediately at page load. - Render from HyperFrames time, not
performance.now(). - Listen for the
hf-seekevent 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
<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>
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 175 lines · 64 tokens per session scan A 6a5036e107a0
typegpu is a skill published in the GitHub repository alecs5am/ralphy (128 stars, last pushed 7d ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,718 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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