# Generative Motion

> Make beautiful algorithmic animation — flow fields, particle systems with trails, strange attractors, slime-mold and boid simulations, reaction-diffusion, domain-warped noise, and shader-driven motion. Use this skill for generative art, creative coding, ambient backdrops, music visualisers, hero backgrounds, screensavers, generative wallpaper, data-driven art, or any request for a "cool animation…

- **Type:** Skill
- **Install:** `agentstack add skill-jangles-byte-atelier-generative-motion`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [jangles-byte](https://agentstack.voostack.com/s/jangles-byte)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [jangles-byte](https://github.com/jangles-byte)
- **Source:** https://github.com/jangles-byte/atelier/tree/main/skills/generative-motion

## Install

```sh
agentstack add skill-jangles-byte-atelier-generative-motion
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Generative Motion

The gap between a generative sketch and a generative *artwork* is almost never the
algorithm — the algorithms are public and short. It is art direction: what the colour
means, where the density sits, how slowly it evolves, and what you left out.

A tutorial renders the algorithm. A piece renders a decision.

## Workflow

1. **Write the intent first**, in two or three sentences: what the system *is* (embers on
   a thermal, ink in water, a colony finding food), what the viewer should feel, and the
   one property that earns colour. Skipping this is what produces rainbow noise.
   The `design-direction` skill's philosophy format applies directly.
2. **Pick the system** from [references/systems.md](references/systems.md) — each entry
   has the equations, the parameter ranges that actually look good, and its failure mode.
   Its numbers are machine-checked: `validate/attractors.py` and `validate/gray_scott.py`
   verify every attractor seed and reaction–diffusion pair on CPU in seconds. Run them
   after changing any value, and use them to find your own.
3. **Build the field and the motion** with
   [references/noise-and-fields.md](references/noise-and-fields.md) — noise, fbm, domain
   warping, curl. Most beautiful motion is a field being sampled.
4. **Decide CPU or GPU early** with [references/gpu-and-shaders.md](references/gpu-and-shaders.md).
   This is architectural, not an optimisation — some systems simply do not express
   themselves below a population the CPU cannot reach, and porting later means rewriting.
5. **Render it well** with [references/rendering.md](references/rendering.md) — trails via
   accumulation, additive blending, envelopes so nothing pops.
6. **Map the field to the screen** with [references/density-and-tone.md](references/density-and-tone.md).
   Accumulation buffers are unbounded and displays are not; this mapping is where
   technically-correct pieces most often look wrong.
7. **Finish it** with [references/post-processing.md](references/post-processing.md) — bloom,
   grain, vignette, grading. The cheapest quality per line of code in the discipline.
8. **Art-direct it** against [references/art-direction.md](references/art-direction.md) —
   the checklist that separates a piece from a screensaver.
9. **Watch it.** Record with `../motion/scripts/capture-motion.py` and look at the result
   over a long run, not one frame. Generative work fails slowly: it looks great at 4
   seconds and turns to grey mush at 60. When something is wrong, work
   [references/diagnostic.md](references/diagnostic.md) rather than tuning at random.

## Which reference to load

| Situation | Load |
|---|---|
| Choosing a system; attractors, boids, physarum, reaction-diffusion, differential growth | `references/systems.md` |
| Noise, fbm, domain warping, curl fields, flow fields | `references/noise-and-fields.md` |
| Trails, blending, buffers, performance at scale | `references/rendering.md` |
| Going GPU: WebGL2 GPGPU, ping-pong, the float-extension minefield, shader systems | `references/gpu-and-shaders.md` |
| 3D: raymarched SDFs, smooth blending, domain repetition, instancing, camera | `references/three-dimensional.md` |
| Flat white, thin outlines, or any density-to-colour mapping | `references/density-and-tone.md` |
| Bloom, grain, vignette, aberration, feedback, colour grading | `references/post-processing.md` |
| It runs but looks like a tutorial | `references/art-direction.md` |
| It's black / saturating / dying / clumping / 4fps / grey mush | `references/diagnostic.md` |

## Non-negotiables

- **Colour is earned.** Map it to a property the system actually has — velocity,
  curvature, age, density, divergence — never to a rainbow of hue over time. If colour
  encodes nothing, the piece reads as a demo.
- **Seed the randomness.** A composition you like must be recoverable; ship the seed.
- **Delta-time everything**, clamped. A field that runs at double speed on a 120Hz display
  is a broken piece, not a fast one.
- **Evolve slower than feels right.** Field evolution that reads as "alive" in the editor
  usually reads as "busy" on the wall. Halve it, then halve it again.
- **Density needs negative space.** A frame at uniform particle density is noise. Vary it,
  or crop into it.
- **Verify parameters before quoting them.** In attractors and reaction–diffusion alike, a
  value just outside the viable band produces *nothing* rather than something worse, so a
  plausible-looking number is not a usable one.
- **Long-run test.** Run it for a minute before shipping — trails saturate, particles
  clump in attractor basins, and energy either dies or explodes.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [jangles-byte](https://github.com/jangles-byte)
- **Source:** [jangles-byte/atelier](https://github.com/jangles-byte/atelier)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-jangles-byte-atelier-generative-motion
- Seller: https://agentstack.voostack.com/s/jangles-byte
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.