generative-motion

A guide for creating algorithm-based animations such as particle systems, flowing fields, simulations, and shader-driven motion. It focuses on turning these systems into directed artwork with intentional colour, movement, and composition.

In plain words
What is it for?
Use it to make generative art, creative coding projects, ambient backgrounds, music visualisers, and animated hero sections.
Why use it?
It helps avoid animations that look like uncontrolled visual noise. It provides system choices, useful parameter ranges, and checks for some simulations.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/jangles-byte/atelier/generative-motion
Any agent
npx skills add jangles-byte/atelier --skill generative-motion
Clone the repo
git clone --depth 1 https://github.com/jangles-byte/atelier

Made for: Claude Code, Codex.

Per session 150 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,276 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
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 $0.00150 $0.01276
Opus 5 $0.00075 $0.00638
Sonnet 5 $0.00030 $0.00255
Haiku 4.5 $0.00015 $0.00128

Measured 2d ago against content hash d3d5f660b32b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

generative-motion scanned grade A with 1 finding 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 2d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (validate/attractors.py, validate/gray_scott.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

warping, curl. Most beautiful motion is a field being sampled.
skills/generative-motion/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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 — 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 — 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. 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 — trails via accumulation, additive blending, envelopes so nothing pops.
  6. Map the field to the screen with 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 — bloom, grain, vignette, grading. The cheapest quality per line of code in the discipline.
  8. Art-direct it against 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 rather than tuning at random.

Read the full file on GitHub · 76 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. 2d ago First seen · 76 lines · 150 tokens per session scan A d3d5f660b32b

Subscribe to this mod's changes

generative-motion is a skill published in the GitHub repository jangles-byte/atelier (2 stars, last pushed 1mo ago), licensed MIT. It adds 150 tokens to every session and 1,276 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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