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 skills add kensaurus/cursor-kenji --skill design-generative-artgit clone --depth 1 https://github.com/kensaurus/cursor-kenjiWrote 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/kensaurus/cursor-kenji/design-generative-art)<a href="https://agentmods.dev/skills/kensaurus/cursor-kenji/design-generative-art"><img src="https://agentmods.dev/badge/skills/kensaurus/cursor-kenji/design-generative-art.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 | $0.00067 | $0.03674 |
| Opus 5 | $0.00034 | $0.01837 |
| Sonnet 5 | $0.00013 | $0.00735 |
| Haiku 4.5 | $0.00007 | $0.00367 |
Grade A, and why
design-generative-art 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Algorithmic Art Skill
Degree of freedom: MIXED. Algorithm and palette [HIGH freedom]; existing
pipeline inventory and seed reproducibility [LOW freedom — run exactly].
Create generative, procedural, and mathematical art using code. Transform algorithms into visual experiences.
How to reason
- Inventory — existing p5 / canvas / noise utilities
- Seed — deterministic RNG; same seed = same pixels
- Parameterize — density, palette, scale, complexity
- Validate — resolution-independent; reduced-motion on loops
Worked example
Inventory: no p5; one
getContext('2d')chart canvas — do not hijack it. Seed:mulberry32(42)drives a newsrc/components/art/flow field. Parameterize:density,palette(ocean),scale; slider re-renders from the same seed. Validate: seed 42 twice matches;prefers-reduced-motionstops the RAF loop.
Self-critique before reporting
- No pipeline clash — did not overwrite an existing canvas/WebGL path
- Reproducible — same seed paints identical output
- Controls matter — each param changes the picture, not just a label
- Right owner — data charts →
data-visualization; UI illustration →design-frontend; WebGL/3D →enhance-web-web3d
CRITICAL: Check Existing First [LOW freedom — run exactly]
Before creating ANY generative art, verify:
- Check for existing creative coding setup:
cat package.json | grep -i "p5\|three\|canvas\|pixi\|paper"
ls -la src/components/art/ src/components/generative/ 2>/dev/null
- Check for existing canvas/WebGL usage:
rg "Canvas|useFrame|getContext.*2d|WebGL" --type tsx -l
- Check for existing noise/random utilities:
rg "simplex\|perlin\|noise\|seedrandom" --type ts
Why: Don't conflict with existing rendering pipelines or duplicate utility code.
Core Principles [HIGH freedom]
1. Seeded Randomness
Every piece should be reproducible with a seed:
// Deterministic random number generator
function mulberry32(seed: number) {
return function() {
let t = seed += 0x6D2B79F5
t = Math.imul(t ^ t >>> 15, t | 1)
t ^= t + Math.imul(t ^ t >>> 7, t | 61)
return ((t ^ t >>> 14) >>> 0) / 4294967296
}
}
// Usage
const rng = mulberry32(42) // Same seed = same output
const value = rng() // 0-1 deterministic random
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.
- 3d ago First seen · 476 lines · 67 tokens per session scan A 888c5758a658
design-generative-art is a skill published in the GitHub repository kensaurus/cursor-kenji (9 stars, last pushed 8d ago), licensed MIT. It adds 67 tokens to every session and 3,674 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 2026-09-03.
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