Procedural Generation logo

Procedural Generation

Community
jame581
procedural-generation

Use when implementing procedural generation — noise-based terrain, BSP dungeons, cellular automata caves, wave function collapse, and seeded randomness in Godot 4.3+

Overview

Publisherjame581
RepositoryGodotPrompter
Skill nameprocedural-generation
Stars
808
Forks
47
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by jame581 on GitHub. Read the source before you install it.

Installation

Install the Procedural Generation 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/jame581/GodotPrompter/tree/master/skills/procedural-generation
  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/jame581/GodotPrompter.git /tmp/GodotPrompter
mkdir -p .claude/skills
cp -r /tmp/GodotPrompter/skills/procedural-generation .claude/skills/procedural-generation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Procedural Generation 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 Procedural Generation 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 Procedural Generation 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 Generation in Godot 4.3+

All examples target Godot 4.3+ with no deprecated APIs. GDScript is shown first, then C#.

Related skills: 2d-essentials for TileMapLayer usage, 3d-essentials for 3D terrain meshes, math-essentials for vectors and transforms, godot-optimization for chunk loading and performance.


1. Seeded Randomness

Always use seeds for reproducible generation. This enables shareable seeds, replay, and deterministic testing.

GDScript

gdscript
# RandomNumberGenerator — per-instance, seedable
var rng := RandomNumberGenerator.new()

func generate_level(level_seed: int) -> void:
    rng.seed = level_seed

    var width: int = rng.randi_range(20, 40)
    var height: int = rng.randi_range(15, 30)
    var enemy_count: int = rng.randi_range(3, 8)
    var treasure_chance: float = rng.randf_range(0.05, 0.15)

# AVOID: Global randf()/randi() — not reproducible across calls
# USE: rng.randf(), rng.randi(), rng.randf_range(), rng.randi_range()

C#

csharp
private RandomNumberGenerator _rng = new();

public void GenerateLevel(ulong levelSeed)
{
    _rng.Seed = levelSeed;

    int width = _rng.RandiRange(20, 40);
    int height = _rng.RandiRange(15, 30);
    int enemyCount = _rng.RandiRange(3, 8);
    float treasureChance = _rng.RandfRange(0.05f, 0.15f);
}

Tip: Generate a seed from a string for shareable level codes: var seed: int = "MyLevel".hash()


2. Noise-Based Generation (FastNoiseLite)

FastNoiseLite for height maps, biome distribution, 2D terrain. Key params: noise_type (Perlin / Simplex / Cellular / Value), frequency (lower = larger features), seed. For terrain, sample noise at each tile coord, threshold the value to pick a tile.

See references/noise-generation.md for the basic noise-map recipe, noise-type reference table, and 2D terrain + TileMapLayer walkthrough.


3. BSP Dungeon Generation

Binary Space Partitioning recursively splits a rectangle into smaller rectangles, carves a room inside each leaf, connects siblings with corridors. Produces grid-aligned room-based dungeons (think roguelike).

See references/bsp-dungeons.md for the full recursive partition + room placement + corridor connection algorithm in GDScript + C#.


4. Cellular Automata (Cave Generation)

Fill a grid with random walls/floors at ~45% density, then iterate "a cell becomes a wall if ≥ 5 of 8 neighbors are walls" 4-5 times. The result is organic cave shapes — no straight corridors.

See references/cellular-automata.md for the full GDScript + C# implementation with TileMapLayer integration.


5. Wave Function Collapse (WFC)

WFC is a constraint solver: given a tile set with adjacency rules, pick the lowest-entropy cell, collapse it to a valid tile, propagate constraints, repeat. Produces tile-rule-respecting output but is non-trivial to implement.

See references/wave-function-collapse.md for concept overview and a simplified GDScript + C# implementation.


6. Common Pitfalls

SymptomCauseFix
Same level every timeNot seeding the RNGSet rng.seed before generation
Different results on different platformsUsing global randf() / randi()Use a dedicated RandomNumberGenerator instance
Noise looks blockyFrequency too highLower frequency (try 0.01–0.05)
Caves are all wall or all floorfill_chance too extreme or too few iterationsUse fill_chance 0.40–0.50 and 4–6 iterations
BSP rooms overlapSplit position too close to edgeEnsure min_room_size buffer in split calculation
WFC contradiction (no valid tile)Adjacency rules too restrictiveAdd more allowed neighbors or implement backtracking
Generation takes too longProcessing entire map in one frameUse await get_tree().process_frame to spread across frames, or use a thread

7. Implementation Checklist

  • All generation uses a seedable RandomNumberGenerator, never global randf()/randi()
  • Seeds are stored with save data so levels can be reproduced
  • FastNoiseLite frequency and octaves are tuned for the game's tile/world scale
  • Large generation is spread across frames or run on a thread to avoid freezing
  • Generated TileMapLayer content uses terrain autotiling when possible (not hardcoded tile coords)
  • BSP dungeons verify all rooms are connected before finalizing
  • Cave generation runs a flood-fill to ensure reachability between key points
  • Player spawn point is validated to be on a floor tile, not inside a wall

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 Procedural Generation AI skill do?

Use when implementing procedural generation — noise-based terrain, BSP dungeons, cellular automata caves, wave function collapse, and seeded randomness in Godot 4.3+

Why use Procedural Generation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jame581/GodotPrompter/tree/master/skills/procedural-generation. 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 Procedural Generation?

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 Procedural Generation?

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

Is the Procedural Generation AI skill free?

Yes. It is published on GitHub by jame581 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 👇