design-generative-art

design-generative-art is a skill for Claude Code, Codex from kensaurus/cursor-kenji. It costs 67 tokens per session (3,674 once invoked), scanned A, original, MIT.

A guide for creating original algorithmic artwork with code using p5.js, Canvas, or SVG. It uses seeded randomness so the same seed can reproduce the same image.

In plain words
What is it for?
Use it for generative art, particle systems, flow fields, procedural visuals, and interactive art controls.
Why use it?
It helps make procedural visuals reproducible, adjustable, and safe to add alongside existing canvas-based features.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for generative art, particle systems, flow fields, procedural visuals, and…

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kensaurus/cursor-kenji/design-generative-art
Install

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.

Any agent
npx skills add kensaurus/cursor-kenji --skill design-generative-art
Clone the repo
git clone --depth 1 https://github.com/kensaurus/cursor-kenji

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for design-generative-art

README.md
[![agentmods](https://agentmods.dev/badge/skills/kensaurus/cursor-kenji/design-generative-art.svg)](https://agentmods.dev/skills/kensaurus/cursor-kenji/design-generative-art)
Your own site
<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>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,674 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 3d ago against content hash 888c5758a658, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

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.

skills/design-generative-art/SKILL.md · 476 lines

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

  1. Inventory — existing p5 / canvas / noise utilities
  2. Seed — deterministic RNG; same seed = same pixels
  3. Parameterize — density, palette, scale, complexity
  4. 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 new src/components/art/ flow field. Parameterize: density, palette (ocean), scale; slider re-renders from the same seed. Validate: seed 42 twice matches; prefers-reduced-motion stops 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:

  1. 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
  1. Check for existing canvas/WebGL usage:
rg "Canvas|useFrame|getContext.*2d|WebGL" --type tsx -l
  1. 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

Read the full file on GitHub · 476 lines

Changes

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.

  1. 3d ago First seen · 476 lines · 67 tokens per session scan A 888c5758a658

Subscribe to this mod's changes

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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