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Threejs Procedural Fields

Community
scottstts
threejs-procedural-fields

Build coherent procedural scalar and vector fields for Three.js materials and geometry. Use for terrain, planets, wear, biomes, clouds, water masks, displacement, roughness, normals, domain warping, and any visual where several channels must derive from shared causes.

Overview

Publisherscottstts
RepositoryThreejs-Awesome-Graphics-Agent-Skills
Skill namethreejs-procedural-fields
Stars
894
Forks
98
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by scottstts on GitHub. Read the source before you install it.

Installation

Install the Threejs Procedural Fields 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/scottstts/Threejs-Awesome-Graphics-Agent-Skills/tree/main/skills/threejs-procedural-fields
  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/scottstts/Threejs-Awesome-Graphics-Agent-Skills.git /tmp/Threejs-Awesome-Graphics-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/Threejs-Awesome-Graphics-Agent-Skills/skills/threejs-procedural-fields .claude/skills/threejs-procedural-fields
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Threejs Procedural Fields 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 Threejs Procedural Fields 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 Threejs Procedural Fields 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.

Procedural Fields

Do not start by stacking noise. Start by defining the fields the object physically or stylistically needs.

Field contract

Before shader code, write a field bundle:

text
coordinates
  → macro form
  → meso structure
  → derived causes
  → material channels

Example:

text
sphereDirection
  → warpedDirection
  → elevation + ridges + craterDepth
  → slope + cavity + latitude + moisture
  → biome + color + roughness + bump

Required workflow

  1. Choose coordinates that remain stable under camera and object motion.
  2. Lock real or perceptual scale for each frequency band.
  3. Create named primary fields. Never hide the whole look in one expression.
  4. Derive secondary fields from causes: slope from normals, shore from sea-level distance, wear from exposure, dirt from cavity.
  5. Reuse the same fields across color, roughness, normal, displacement, emission, and scattering.
  6. Add debug output for every named field.
  7. Filter high-frequency fields by derivatives, tessellation density, or camera distance.

Read references/field-stack-recipes.md before implementation. It records sphere, terrain, water, and structured-placement field contracts plus common parity defects.

Read the procedural planet surface for a shared CPU/GLSL field bundle whose height, continents, climate, biomes, roughness, and normals remain independently inspectable.

Non-negotiable rules

  • Independent noise per channel produces visual soup. Share structure.
  • Domain warp the coordinates, not every result.
  • Warp spherical coordinates tangentially, then renormalize.
  • Use different frequency bands for silhouette, regions, surface breakup, and micro-normal.
  • Do not displace geometry with frequencies the mesh cannot represent.
  • Keep categorical masks broad enough to avoid isolated “bubble” regions.
  • Parameter names must describe perception: ridgeWidth, coastBlend, cavityDarkening, not noise3Amount.

Routing boundary

Use this skill when the shared field model is the task. Use $threejs-procedural-materials when the task is channel assembly and material response, and $threejs-procedural-planets when the deliverable is a complete planetary body.

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 Threejs Procedural Fields AI skill do?

Build coherent procedural scalar and vector fields for Three.js materials and geometry. Use for terrain, planets, wear, biomes, clouds, water masks, displacement, roughness, normals, domain warping, and any visual where several channels must derive from shared causes.

Why use Threejs Procedural Fields on TypingMind?

Because you install it once and use it with any model. Threejs Procedural Fields 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 Threejs Procedural Fields in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scottstts/Threejs-Awesome-Graphics-Agent-Skills/tree/main/skills/threejs-procedural-fields. 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 Threejs Procedural Fields?

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 Threejs Procedural Fields?

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

Is the Threejs Procedural Fields AI skill free?

Yes. It is published on GitHub by scottstts 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.

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