Particle System logo

Particle System

Organization
iart-ai
particle-system

This skill should be used when the user asks to "build a particle system", "make confetti/snow/smoke/sparks", "create a connected-dot/constellation network background", "add a flow-field or curl-noise particle effect", "render thousands of GPU particles with Three.js Points", or "animate emitters with forces". Covers per-particle integration, forces, flow fields, burst/continuous emission, spatial-grid connected dots, and GPU points + shaders.

Overview

Publisheriart-ai
Repositorywebgl-animation-skills
Skill nameparticle-system
Stars
51
Forks
5
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by iart-ai on GitHub. Read the source before you install it.

Installation

Install the Particle System AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/iart-ai/webgl-animation-skills/tree/main/skills/particle-system
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/iart-ai/webgl-animation-skills.git /tmp/webgl-animation-skills
mkdir -p .claude/skills
cp -r /tmp/webgl-animation-skills/skills/particle-system .claude/skills/particle-system
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Particle System in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Particle System on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Particle System is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Particle System

Drive many small elements with simple per-particle rules to get emergent, organic motion. Use 2D canvas for hundreds, GPU Points for thousands.

When to use

  • Particle/constellation backgrounds and ambient motion.
  • Celebratory bursts: confetti, sparks. Weather: snow, rain. Volumetric: smoke.
  • Flow-field / curl-noise swirls and data-driven point clouds.
  • Connected-dot networks (lines between nearby particles).

Core loop: integrate per particle

Each particle holds state and is advanced every frame: accumulate forces into acceleration, integrate velocity and position, age it, respawn when dead. Scale by dt for frame-rate independence.

js
class Particle {
  constructor() { this.reset(); }
  reset() {
    this.x = Math.random() * W; this.y = Math.random() * H;
    this.vx = 0; this.vy = 0;
    this.life = 1; this.size = 1 + Math.random() * 2;
  }
  step(dt, forces) {
    let ax = 0, ay = 0;
    for (const f of forces) { const [fx, fy] = f(this); ax += fx; ay += fy; }
    this.vx += ax * dt; this.vy += ay * dt;
    this.vx *= 0.99; this.vy *= 0.99;            // drag
    this.x += this.vx * dt; this.y += this.vy * dt;
    this.life -= dt * 0.2;
    if (this.life <= 0) this.reset();
  }
}

Prefer semi-implicit Euler (update velocity first, then position with the new velocity, as above) — it is stable for the spring/drag forces particles use. Use a fixed or clamped dt (Math.min(dt, 1/30)) so a stalled tab does not explode the simulation.

Forces

A force is a function returning an acceleration [fx, fy]. Compose a list.

js
const gravity = () => [0, 400];                  // constant downward
const drag = (p) => [-p.vx * 0.5, -p.vy * 0.5];  // proportional resistance
function attract(tx, ty, strength) {             // pull toward a point (e.g. mouse)
  return (p) => {
    const dx = tx - p.x, dy = ty - p.y;
    const d2 = dx*dx + dy*dy + 100;              // +100 softens the singularity
    const f = strength / d2;
    return [dx * f, dy * f];
  };
}

Repulsion is attract with negative strength. Springs toward a home position give "settle back" effects.

Flow fields / curl noise (organic swirl)

Sample a noise field to derive a velocity direction per particle. Use the noise value as an angle:

js
// `noise2D` from a library (e.g. simplex-noise's createNoise2D), range -1..1
function flowField(noise2D, scale = 0.002, speed = 60) {
  return (p) => {
    const angle = noise2D(p.x * scale, p.y * scale) * Math.PI * 2;
    return [Math.cos(angle) * speed - p.vx, Math.sin(angle) * speed - p.vy];
  };
}

True curl noise is divergence-free (no sources/sinks → fluid-like). Compute the curl of a potential by finite differences:

js
function curl(noise2D, x, y, eps = 1e-2) {
  const n1 = noise2D(x, y + eps), n2 = noise2D(x, y - eps);
  const n3 = noise2D(x + eps, y), n4 = noise2D(x - eps, y);
  return [ (n1 - n2) / (2*eps), -(n3 - n4) / (2*eps) ];  // (dN/dy, -dN/dx)
}

Add time to the noise input (noise2D(x*scale, y*scale + t)) to make the field evolve.

Emission: burst vs continuous

  • Burst (confetti, sparks): spawn N particles at once at a point with randomized angle/speed within a cone, then let gravity + drag take over. No respawn — remove when dead.
  • Continuous (snow, smoke): spawn a steady rate; respawn dead particles at the top/source.

Randomize within a range for natural spread: angle = base + (Math.random()-0.5)*spread; speed = min + Math.random()*(max-min).

js
function burst(x, y, n = 120) {
  const out = [];
  for (let i = 0; i < n; i++) {
    const a = Math.random() * Math.PI * 2;
    const s = 200 + Math.random() * 400;
    out.push({ x, y, vx: Math.cos(a)*s, vy: Math.sin(a)*s - 200,  // upward bias
               life: 1, size: 4 + Math.random()*4,
               color: `hsl(${Math.random()*360},90%,60%)`,
               rot: Math.random()*6.28, vr: (Math.random()-0.5)*10 });
  }
  return out;
}

Confetti reads as confetti because of rotation + flat rectangles + gravity + air drag, not round dots. Snow reads as snow from slow fall + gentle horizontal sine sway + size-varied depth.

Connected-dot network without O(n²)

Naively checking every pair is O(n²) and dies past ~300 particles. Use a uniform spatial grid: bin particles by cell, only compare against the 8 neighboring cells.

js
function connect(ctx, parts, radius) {
  const cell = radius, cols = Math.ceil(W / cell);
  const grid = new Map();
  const key = (cx, cy) => cx + cy * cols;
  for (const p of parts) {
    const cx = (p.x / cell) | 0, cy = (p.y / cell) | 0;
    (grid.get(key(cx, cy)) ?? grid.set(key(cx, cy), []).get(key(cx, cy))).push(p);
  }
  for (const p of parts) {
    const cx = (p.x / cell) | 0, cy = (p.y / cell) | 0;
    for (let oy = -1; oy <= 1; oy++) for (let ox = -1; ox <= 1; ox++) {
      const bucket = grid.get(key(cx+ox, cy+oy)); if (!bucket) continue;
      for (const q of bucket) {
        if (q === p) continue;
        const dx = p.x - q.x, dy = p.y - q.y, d = Math.hypot(dx, dy);
        if (d < radius) {
          ctx.globalAlpha = 1 - d / radius;     // fade line with distance
          ctx.beginPath(); ctx.moveTo(p.x, p.y); ctx.lineTo(q.x, q.y); ctx.stroke();
        }
      }
    }
  }
  ctx.globalAlpha = 1;
}

This is O(n) for evenly distributed particles. Each pair is found twice; halve work by only checking forward neighbors if needed.

GPU particles: Three.js Points + shader

For thousands+, push all positions into a BufferGeometry and render as Points. Animate in the vertex shader for true GPU scale.

js
const N = 50000;
const pos = new Float32Array(N * 3);
for (let i = 0; i < N * 3; i++) pos[i] = (Math.random() - 0.5) * 20;
const geo = new THREE.BufferGeometry();
geo.setAttribute('position', new THREE.BufferAttribute(pos, 3));
const mat = new THREE.ShaderMaterial({
  uniforms: { u_time: { value: 0 }, u_size: { value: 6 } },
  transparent: true, depthWrite: false, blending: THREE.AdditiveBlending,
  vertexShader: `
    uniform float u_time, u_size;
    void main(){
      vec3 p = position;
      p.y += sin(u_time + position.x) * 0.5;          // animate on GPU
      vec4 mv = modelViewMatrix * vec4(p, 1.0);
      gl_PointSize = u_size * (10.0 / -mv.z);          // perspective size
      gl_Position = projectionMatrix * mv;
    }`,
  fragmentShader: `
    void main(){
      float d = length(gl_PointCoord - 0.5);
      if (d > 0.5) discard;                            // round, soft points
      gl_FragColor = vec4(1.0, 0.8, 0.4, smoothstep(0.5, 0.0, d));
    }`,
});
scene.add(new THREE.Points(geo, mat));
// loop: mat.uniforms.u_time.value = clock.getElapsedTime();

AdditiveBlending + depthWrite: false gives the glowing-particle look. discard on gl_PointCoord distance makes square points round. For per-particle data (life, seed), add custom attributes and read them in the shader.

Deliver & verify (standalone HTML)

Packaged helper (scripts/): scripts/seek-shot.sh anim.html 0 1.5 3 freezes the ?t=N harness and screenshots each moment; scripts/contact-sheet.sh sheet.png frame-*.png tiles them for one-glance review. See scripts/README.md.

For a self-contained particle effect (constellation background, confetti burst, flow field, GPU points) the deliverable is one HTML file that opens directly in a browser — canvas 2D inline, or Three.js from a CDN via an importmap for GPU Points, one render loop, no build step. A single file is the right tier; don't reach for a bundler when one file does the job.

Output contract:

  • One .html: for canvas, the simulation + 2D draw loop in one inline <script>; for GPU points, importmap pins three to a CDN with the Points setup inline.
  • Drive the sim from one accumulated time (sum of clamped dt, or clock.getElapsedTime() / u_time for GPU). No Date.now() scattered per particle.
  • Seed the RNG — replace bare Math.random() with a seeded PRNG (e.g. mulberry32) so spawn positions, angles, and bursts reproduce frame-for-frame.

Seek/freeze harness — advance to a fixed time, render ONE frame for screenshots. ?t=N re-seeds, steps the sim deterministically to N seconds with a fixed timestep, renders once, and stops the loop.

html
<script>
  let rng = mulberry32(1234);                 // fixed seed → reproducible
  const particles = spawn(() => rng());
  function render() { /* draw particles to canvas / renderer.render(...) */ }
  const t = new URLSearchParams(location.search).get("t");
  if (t !== null) {
    const step = 1 / 60, end = parseFloat(t);
    for (let s = 0; s < end; s += step) update(step);  // fixed-step to t
    render();                                  // one frozen frame
    window.__ready = true;
  } else {
    let prev = performance.now();
    (function loop(now){ update(Math.min((now-prev)/1000, 1/30)); prev = now;
      render(); requestAnimationFrame(loop); })(prev);
  }
</script>

Verify loop — render → freeze → screenshot → check: open at three instants — start, mid, settle (?t=0, ?t=<mid>, ?t=<end>; for a burst, t≈0 spawn / t≈0.5 spread / t≈1.5 settle) — screenshot each, and check both fidelity (matches the brief) and artifacts: a blank canvas = parse/init error (check the console), particles escaping the frame (clamp/wrap missing), NaN positions (everything vanishes), all particles bunched at the origin (RNG not wired). For GPU points, WebGL needs a GPU context; Playwright/Chromium supplies one (swiftshader) headless.

bash
npx playwright screenshot --wait-for-timeout=600 "file://$PWD/particles.html?t=1.0" frame-mid.png

Before you finish:

  1. Canvas renders particles — not blank, no console/WebGL errors, no CDN 404s.
  2. ?t=N freezes a reproducible frame (seeded RNG + fixed timestep → same N → same pixels).
  3. Screenshotted at start / mid / settle — matches the brief, no escaped/NaN/origin-bunched particles.
  4. Disposed and leak-free if embedded in an SPA (cancel the rAF loop; for GPU, dispose geometry/material/renderer).
  5. prefers-reduced-motion honored — fewer particles or a static field where motion is decorative.

Quick reference

EffectRecipe
Confettiburst + gravity + drag + rotating rects
Snowcontinuous top spawn + slow fall + sine sway
Smokecontinuous + upward + grow size + fade alpha
Sparksshort-life burst + additive + fast fade
Flow fieldnoise angle → velocity, evolve with time
Curl noisecurl of noise potential (divergence-free)
Constellationspatial grid, link within radius, fade by distance
1000s+Three.js Points + ShaderMaterial, animate in vertex shader

Reference files

  • references/particle-recipes.md — Complete canvas confetti, snow, and smoke systems; mouse attraction/repulsion; full simplex flow-field and curl-noise field with a rendered streaming look; the spatial-grid connected-dot background end to end; and a GPU Points system with per-particle life/seed attributes, additive glow, and respawn in the shader.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Particle System AI skill do?

This skill should be used when the user asks to "build a particle system", "make confetti/snow/smoke/sparks", "create a connected-dot/constellation network background", "add a flow-field or curl-noise particle effect", "render thousands of GPU particles with Three.js Points", or "animate emitters with forces". Covers per-particle integration, forces, flow fields, burst/continuous emission, spatial-grid connected dots, and GPU points + shaders.

Why use Particle System on TypingMind?

Because you install it once and use it with any model. Particle System is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Particle System in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/iart-ai/webgl-animation-skills/tree/main/skills/particle-system. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Particle System?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Particle System?

As many as you like. As long as a model supports skills, you can use Particle System with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Particle System AI skill free?

Yes. It is published on GitHub by iart-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇