Design generative art
Create algorithmic art using p5.js, Canvas API, or SVG with seeded randomness and interactive parameters. Use when user requests generative art, procedural art, flow fields, particle systems, creative coding, noise patterns, mathematical visualizations, or asks for "art from code", "generate visuals", or "interactive animation".From its SKILL.md
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SKILL.md
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Algorithmic Art Skill
Create generative, procedural, and mathematical art using code. Transform algorithms into visual experiences.
CRITICAL: Check Existing First
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
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
2. Parameterized Generation
Make art controllable via parameters:
interface ArtParams {
seed: number
density: number // 0-1
palette: string[]
scale: number
speed: number
complexity: number // 0-1
}
3. Resolution Independence
Design for any canvas size:
// Normalize coordinates to 0-1 range
const nx = x / width
const ny = y / height
// Then scale to canvas
const px = nx * canvas.width
const py = ny * canvas.height
Techniques
Flow Fields
function createFlowField(cols: number, rows: number, seed: number) {
const rng = mulberry32(seed)
const field: number[][] = []
for (let y = 0; y < rows; y++) {
field[y] = []
for (let x = 0; x < cols; x++) {
// Perlin-like noise using layered sine waves
const angle = Math.sin(x * 0.1) * Math.cos(y * 0.1) * Math.PI * 2
+ rng() * 0.5
field[y][x] = angle
}
}
return field
}
function drawFlowField(ctx: CanvasRenderingContext2D, field: number[][], params: ArtParams) {
const cellW = ctx.canvas.width / field[0].length
const cellH = ctx.canvas.height / field.length
// Spawn particles and follow flow
for (let i = 0; i < params.density * 1000; i++) {
let x = rng() * ctx.canvas.width
let y = rng() * ctx.canvas.height
ctx.beginPath()
ctx.moveTo(x, y)
ctx.strokeStyle = params.palette[Math.floor(rng() * params.palette.length)]
ctx.globalAlpha = 0.3
for (let step = 0; step < 100; step++) {
const col = Math.floor(x / cellW)
const row = Math.floor(y / cellH)
if (col < 0 || col >= field[0].length || row < 0 || row >= field.length) break
const angle = field[row][col]
x += Math.cos(angle) * params.scale
y += Math.sin(angle) * params.scale
ctx.lineTo(x, y)
}
ctx.stroke()
}
}
Recursive Subdivision
function subdivide(
ctx: CanvasRenderingContext2D,
x: number, y: number, w: number, h: number,
depth: number, maxDepth: number, rng: () => number,
palette: string[]
) {
if (depth >= maxDepth || rng() < 0.15) {
// Draw leaf
ctx.fillStyle = palette[Math.floor(rng() * palette.length)]
ctx.globalAlpha = 0.6 + rng() * 0.4
ctx.fillRect(x + 1, y + 1, w - 2, h - 2)
return
}
// Random split direction and position
const horizontal = rng() > 0.5
const split = 0.3 + rng() * 0.4 // 30-70% split
if (horizontal) {
const splitY = y + h * split
subdivide(ctx, x, y, w, splitY - y, depth + 1, maxDepth, rng, palette)
subdivide(ctx, x, splitY, w, y + h - splitY, depth + 1, maxDepth, rng, palette)
} else {
const splitX = x + w * split
subdivide(ctx, x, y, splitX - x, h, depth + 1, maxDepth, rng, palette)
subdivide(ctx, splitX, y, x + w - splitX, h, depth + 1, maxDepth, rng, palette)
}
}
Circle Packing
interface Circle {
x: number; y: number; r: number; color: string
}
function circlePacking(
width: number, height: number,
maxCircles: number, maxRadius: number,
rng: () => number, palette: string[]
): Circle[] {
const circles: Circle[] = []
let attempts = 0
const maxAttempts = maxCircles * 50
while (circles.length < maxCircles && attempts < maxAttempts) {
attempts++
const candidate = {
x: rng() * width,
y: rng() * height,
r: 2,
color: palette[Math.floor(rng() * palette.length)]
}
// Grow until collision
let valid = true
while (valid && candidate.r < maxRadius) {
candidate.r += 1
for (const other of circles) {
const dist = Math.hypot(candidate.x - other.x, candidate.y - other.y)
if (dist < candidate.r + other.r + 2) {
candidate.r -= 1
valid = false
break
}
}
// Check bounds
if (candidate.x - candidate.r < 0 || candidate.x + candidate.r > width ||
candidate.y - candidate.r < 0 || candidate.y + candidate.r > height) {
candidate.r -= 1
valid = false
}
}
if (candidate.r > 2) circles.push(candidate)
}
return circles
}
L-Systems (Fractal Trees/Plants)
interface LSystem {
axiom: string
rules: Record<string, string>
angle: number
length: number
iterations: number
}
const fractalTree: LSystem = {
axiom: 'F',
rules: { 'F': 'FF+[+F-F-F]-[-F+F+F]' },
angle: 25,
length: 4,
iterations: 4,
}
function generateLSystem(system: LSystem): string {
let current = system.axiom
for (let i = 0; i < system.iterations; i++) {
current = current.split('').map(c => system.rules[c] || c).join('')
}
return current
}
function drawLSystem(ctx: CanvasRenderingContext2D, system: LSystem, startX: number, startY: number) {
const instructions = generateLSystem(system)
const stack: { x: number; y: number; angle: number }[] = []
let x = startX, y = startY, angle = -90 // Start pointing up
ctx.beginPath()
ctx.moveTo(x, y)
for (const char of instructions) {
switch (char) {
case 'F':
const nx = x + Math.cos(angle * Math.PI / 180) * system.length
const ny = y + Math.sin(angle * Math.PI / 180) * system.length
ctx.lineTo(nx, ny)
x = nx; y = ny
break
case '+': angle += system.angle; break
case '-': angle -= system.angle; break
case '[': stack.push({ x, y, angle }); break
case ']':
const state = stack.pop()!
x = state.x; y = state.y; angle = state.angle
ctx.moveTo(x, y)
break
}
}
ctx.stroke()
}
React Component Pattern
'use client'
import { useEffect, useRef, useState, useCallback } from 'react'
interface GenerativeArtProps {
seed?: number
width?: number
height?: number
palette?: string[]
className?: string
}
export function GenerativeArt({
seed = Date.now(),
width = 800,
height = 600,
palette = ['#264653', '#2a9d8f', '#e9c46a', '#f4a261', '#e76f51'],
className,
}: GenerativeArtProps) {
const canvasRef = useRef<HTMLCanvasElement>(null)
const [currentSeed, setCurrentSeed] = useState(seed)
const render = useCallback(() => {
const canvas = canvasRef.current
if (!canvas) return
const ctx = canvas.getContext('2d')!
const rng = mulberry32(currentSeed)
// Clear
ctx.fillStyle = '#1a1a2e'
ctx.fillRect(0, 0, width, height)
// Your generative algorithm here
drawFlowField(ctx, createFlowField(40, 30, currentSeed), {
seed: currentSeed,
density: 0.8,
palette,
scale: 2,
speed: 1,
complexity: 0.7,
})
}, [currentSeed, width, height, palette])
useEffect(() => { render() }, [render])
return (
<div className={className}>
<canvas
ref={canvasRef}
width={width}
height={height}
className="rounded-lg"
style={{ maxWidth: '100%', height: 'auto' }}
/>
<div className="flex gap-2 mt-4">
<button
onClick={() => setCurrentSeed(Date.now())}
className="px-4 py-2 bg-primary text-primary-foreground rounded-lg"
>
Regenerate
</button>
<input
type="number"
value={currentSeed}
onChange={(e) => setCurrentSeed(Number(e.target.value))}
className="px-3 py-2 border rounded-lg w-32"
aria-label="Seed value"
/>
</div>
</div>
)
}
Color Palettes
// Curated palettes for generative art
const PALETTES = {
// Warm
sunset: ['#ff6b6b', '#feca57', '#ff9ff3', '#54a0ff', '#5f27cd'],
autumn: ['#d35400', '#e67e22', '#f39c12', '#2c3e50', '#ecf0f1'],
// Cool
ocean: ['#0c2461', '#1e3799', '#4a69bd', '#6a89cc', '#82ccdd'],
forest: ['#1b4332', '#2d6a4f', '#40916c', '#52b788', '#74c69d'],
// Monochrome
ink: ['#000000', '#1a1a1a', '#333333', '#4d4d4d', '#666666'],
paper: ['#f5f0e8', '#ede4d4', '#e5d9c0', '#ddc9a3', '#d4ba87'],
// Vibrant
neon: ['#ff00ff', '#00ffff', '#ff0066', '#66ff00', '#ffff00'],
candy: ['#ff6f91', '#ff9671', '#ffc75f', '#f9f871', '#d4fc79'],
// Japanese-inspired
wabi: ['#2c1810', '#5c3a2e', '#b5651d', '#daa06d', '#f5deb3'],
sakura: ['#ffb7c5', '#ff69b4', '#c71585', '#8b008b', '#4a0028'],
}
Animation Loop
function animatedArt(canvas: HTMLCanvasElement, params: ArtParams) {
const ctx = canvas.getContext('2d')!
let frame = 0
let animationId: number
function loop() {
frame++
const t = frame * params.speed * 0.01
// Semi-transparent overlay for trails
ctx.fillStyle = 'rgba(0, 0, 0, 0.02)'
ctx.fillRect(0, 0, canvas.width, canvas.height)
// Animated elements
for (let i = 0; i < 50; i++) {
const x = canvas.width / 2 + Math.cos(t + i * 0.5) * 200
const y = canvas.height / 2 + Math.sin(t * 0.7 + i * 0.3) * 200
const r = 2 + Math.sin(t + i) * 1
ctx.beginPath()
ctx.arc(x, y, r, 0, Math.PI * 2)
ctx.fillStyle = params.palette[i % params.palette.length]
ctx.globalAlpha = 0.8
ctx.fill()
}
animationId = requestAnimationFrame(loop)
}
loop()
return () => cancelAnimationFrame(animationId)
}
Export & Sharing
// Export canvas as PNG
function exportPNG(canvas: HTMLCanvasElement, filename: string) {
const link = document.createElement('a')
link.download = `${filename}-${Date.now()}.png`
link.href = canvas.toDataURL('image/png')
link.click()
}
// Export as SVG (for vector output)
function exportSVG(svgElement: SVGSVGElement, filename: string) {
const serializer = new XMLSerializer()
const svgString = serializer.serializeToString(svgElement)
const blob = new Blob([svgString], { type: 'image/svg+xml' })
const link = document.createElement('a')
link.download = `${filename}-${Date.now()}.svg`
link.href = URL.createObjectURL(blob)
link.click()
}
Related Skills
enhance-web-web3d— WebGL, Three.js, shaders for 3D generative artdesign-motion— Animation patterns for interactive piecesdesign-canvas— Print-quality visual design philosophydata-visualization— Data-driven generative compositions
Validation
After creating algorithmic art:
- Reproducibility → Same seed produces identical output
- Performance → 60fps for animated pieces
- Resolution → Looks good at target export size
- Palette → Colors work together harmoniously
- Parameters → Controls produce meaningful visual changes
- Export → PNG/SVG export works correctly
- Accessibility → Animated art respects
prefers-reduced-motion
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.