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/athevon/genjutsu/canvas-generativenpx skills add AThevon/genjutsu --skill canvas-generativegit clone --depth 1 https://github.com/AThevon/genjutsuWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/athevon/genjutsu/canvas-generative)<a href="https://agentmods.dev/skills/athevon/genjutsu/canvas-generative"><img src="https://agentmods.dev/badge/skills/athevon/genjutsu/canvas-generative.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00030 | $0.02426 |
| Opus 5 | $0.00015 | $0.01213 |
| Sonnet 5 | $0.00006 | $0.00485 |
| Haiku 4.5 | $0.00003 | $0.00243 |
Grade A, and why
canvas-generative scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to canvas-generative — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Canvas Generative
Algorithmic and generative art with Canvas 2D. Concise rules here. Deep-dive and reference implementations in
references/.
Canvas 2D Setup
DPR-Aware Sizing
Every canvas must be sharp on Retina/HiDPI displays. Set the buffer size to the physical pixel size, scale down with CSS.
function setupCanvas(canvas, width, height) {
const dpr = window.devicePixelRatio || 1;
canvas.width = width * dpr;
canvas.height = height * dpr;
canvas.style.width = `${width}px`;
canvas.style.height = `${height}px`;
const ctx = canvas.getContext('2d');
ctx.scale(dpr, dpr);
return ctx;
}
Resize Handler
function handleResize(canvas, ctx, draw) {
const ro = new ResizeObserver(([entry]) => {
const { width, height } = entry.contentRect;
const dpr = window.devicePixelRatio || 1;
canvas.width = width * dpr;
canvas.height = height * dpr;
ctx.scale(dpr, dpr);
draw(); // re-render after resize
});
ro.observe(canvas.parentElement);
return () => ro.disconnect();
}
Animation Loop (RAF)
let animId;
let prevTime = 0;
function loop(time) {
const dt = Math.min((time - prevTime) / 1000, 0.1); // cap delta to avoid spiral of death
prevTime = time;
update(dt);
render(ctx);
animId = requestAnimationFrame(loop);
}
// Start
animId = requestAnimationFrame(loop);
// Stop
cancelAnimationFrame(animId);
Noise
| Type | Characteristics | Best For |
|---|---|---|
| Perlin | Smooth, grid-aligned bias, cheaper | Terrain, clouds, gentle organic textures |
| Simplex | No grid artifacts, better gradients, slightly costlier | Flow fields, organic motion, seamless tiling |
| Worley (Cellular) | Distance-to-nearest-point, cell-like | Voronoi patterns, caustics, cracks, cell textures |
Usage rules:
- Always scale input coordinates (divide by a
noiseScalefactor) -- raw pixel coords produce visual noise - Use octaves (fractal Brownian motion) for detail: sum multiple noise calls at increasing frequency and decreasing amplitude
- Seed your noise for reproducibility
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 326 lines · 30 tokens per session scan A 9f6747e5e7c3
canvas-generative is a skill published in the GitHub repository AThevon/genjutsu (325 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 2,426 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to canvas-generative, differing in 0 lines, and is treated as a copy.
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