procgen-patterns

procgen-patterns is a skill for Claude Code, Codex from HermeticOrmus/LibreGameDev-Claude-Code. It costs 0 tokens per session (2,844 once invoked), scanned A, original, MIT.

A collection of procedural-generation patterns for creating game terrain, dungeons, and caves from rules or noise. Procedural generation means producing content with algorithms instead of placing every part by hand.

In plain words
What is it for?
Use it to generate noise-based terrain heightmaps, sample terrain heights, build BSP dungeons, or create caves with cellular automata in Godot.
Why use it?
It helps create varied game worlds while keeping the generation repeatable through settings such as a seed.

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/hermeticormus/libregamedev-claude-code/procgen-patterns
Any agent
npx skills add HermeticOrmus/LibreGameDev-Claude-Code --skill procgen-patterns
Clone the repo
git clone --depth 1 https://github.com/HermeticOrmus/LibreGameDev-Claude-Code

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for procgen-patterns

README.md
[![agentmods](https://agentmods.dev/badge/skills/hermeticormus/libregamedev-claude-code/procgen-patterns.svg)](https://agentmods.dev/skills/hermeticormus/libregamedev-claude-code/procgen-patterns)
Your own site
<a href="https://agentmods.dev/skills/hermeticormus/libregamedev-claude-code/procgen-patterns"><img src="https://agentmods.dev/badge/skills/hermeticormus/libregamedev-claude-code/procgen-patterns.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,844 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00000 $0.02844
Opus 5 $0.00000 $0.01422
Sonnet 5 $0.00000 $0.00569
Haiku 4.5 $0.00000 $0.00284

Measured 4d ago against content hash 3389d941f72b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

procgen-patterns scanned grade A with 0 findings 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 4d ago.

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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/procedural-generation/skills/procgen-patterns/SKILL.md · 302 lines

How it starts

The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Procgen Patterns

FastNoiseLite Terrain Generation

# Layered noise terrain heightmap - Godot 4
class_name TerrainGenerator extends Node
@export var width: int = 128
@export var height: int = 128
@export var seed: int = 0

@export var base_frequency: float = 0.01
@export var octaves: int = 5
@export var lacunarity: float = 2.0  # Frequency multiplier per octave
@export var gain: float = 0.5        # Amplitude multiplier per octave

func generate_heightmap() -> Image:
    var noise := FastNoiseLite.new()
    noise.noise_type = FastNoiseLite.TYPE_SIMPLEX
    noise.fractal_type = FastNoiseLite.FRACTAL_FBM
    noise.seed = seed
    noise.frequency = base_frequency
    noise.fractal_octaves = octaves
    noise.fractal_lacunarity = lacunarity
    noise.fractal_gain = gain

    var img := Image.create(width, height, false, Image.FORMAT_R8)
    for y in height:
        for x in width:
            var value := (noise.get_noise_2d(x, y) + 1.0) * 0.5  # Normalize to [0,1]
            img.set_pixel(x, y, Color(value, 0, 0))
    return img

func sample_height(world_pos: Vector2) -> float:
    # Used at runtime for placing objects at correct heights
    var noise := FastNoiseLite.new()
    noise.seed = seed
    noise.frequency = base_frequency
    return (noise.get_noise_2d(world_pos.x, world_pos.y) + 1.0) * 0.5

BSP Dungeon Generator

# Binary Space Partitioning dungeon - produces structured room layouts
class_name BSPDungeon extends RefCounted

class BSPNode:
    var rect: Rect2i
    var left: BSPNode
    var right: BSPNode
    var room: Rect2i  # Only set on leaf nodes

    func _init(r: Rect2i) -> void:
        rect = r

const MIN_LEAF_SIZE := 10
const MIN_ROOM_SIZE := 4

var _rng: RandomNumberGenerator
var _rooms: Array[Rect2i] = []
var _corridors: Array[Array] = []  # Array of [Vector2i, Vector2i] pairs

func generate(bounds: Rect2i, seed: int) -> void:
    _rng = RandomNumberGenerator.new()
    _rng.seed = seed
    _rooms.clear()
    _corridors.clear()

    var root := BSPNode.new(bounds)
    _split(root, 0)
    _create_rooms(root)
    _connect_rooms(root)

func _split(node: BSPNode, depth: int) -> void:
    if depth > 5:
        return
    var split_horizontal := _rng.randi() % 2 == 0
    var min_size := MIN_LEAF_SIZE * 2
    if node.rect.size.x < min_size and node.rect.size.y < min_size:
        return  # Too small to split

    if node.rect.size.x > node.rect.size.y:
        split_horizontal = false  # Force vertical split for wide nodes

    var split_pos: int
    if split_horizontal:
        split_pos = _rng.randi_range(MIN_LEAF_SIZE, node.rect.size.y - MIN_LEAF_SIZE)
        node.left = BSPNode.new(Rect2i(node.rect.position, Vector2i(node.rect.size.x, split_pos)))
        node.right = BSPNode.new(Rect2i(
            node.rect.position + Vector2i(0, split_pos),
            Vector2i(node.rect.size.x, node.rect.size.y - split_pos)
        ))
    else:
        split_pos = _rng.randi_range(MIN_LEAF_SIZE, node.rect.size.x - MIN_LEAF_SIZE)
        node.left = BSPNode.new(Rect2i(node.rect.position, Vector2i(split_pos, node.rect.size.y)))
        node.right = BSPNode.new(Rect2i(
            node.rect.position + Vector2i(split_pos, 0),
            Vector2i(node.rect.size.x - split_pos, node.rect.size.y)
        ))

    _split(node.left, depth + 1)
    _split(node.right, depth + 1)

func _create_rooms(node: BSPNode) -> void:
    if node.left == null and node.right == null:
        # Leaf: create a room with padding
        var padding := 1
        var max_w := node.rect.size.x - padding * 2
        var max_h := node.rect.size.y - padding * 2
        if max_w < MIN_ROOM_SIZE or max_h < MIN_ROOM_SIZE:
            return
        var w := _rng.randi_range(MIN_ROOM_SIZE, max_w)
        var h := _rng.randi_range(MIN_ROOM_SIZE, max_h)
        var x := node.rect.position.x + padding + _rng.randi_range(0, max_w - w)
        var y := node.rect.position.y + padding + _rng.randi_range(0, max_h - h)
        node.room = Rect2i(x, y, w, h)
        _rooms.append(node.room)
        return
    if node.left:
        _create_rooms(node.left)
    if node.right:
        _create_rooms(node.right)

func _connect_rooms(node: BSPNode) -> void:
    if node.left == null or node.right == null:
        return
    var left_room := _get_room(node.left)
    var right_room := _get_room(node.right)
    if left_room != Rect2i() and right_room != Rect2i():
        var start := left_room.get_center()
        var end := right_room.get_center()
        _corridors.append([start, end])  # L-shaped corridor between centers
    _connect_rooms(node.left)
    _connect_rooms(node.right)

func _get_room(node: BSPNode) -> Rect2i:
    if node.room != Rect2i():
        return node.room
    var left_room := Rect2i()
    var right_room := Rect2i()
    if node.left:
        left_room = _get_room(node.left)
    if node.right:
        right_room = _get_room(node.right)
    if left_room == Rect2i():
        return right_room
    return left_room

func get_rooms() -> Array[Rect2i]:
    return _rooms

Read the full file on GitHub · 302 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. 4d ago First seen · 302 lines · 0 tokens per session scan A 3389d941f72b

Subscribe to this mod's changes

procgen-patterns is a skill published in the GitHub repository HermeticOrmus/LibreGameDev-Claude-Code (6 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,844 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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