world-gen

Generate deterministic procedural worlds with biome and tile assignment in Python.

1|Updated Jun 6, 2026
One-click install
npx skills add https://github.com/diadia0000/ForWanna --skill world-gen
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: world-gen
Source: https://github.com/diadia0000/ForWanna/tree/main/.claude/skills/world/WorldGen
Command: npx skills add https://github.com/diadia0000/ForWanna --skill world-gen

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, random, and includes scripts (resource) components.

What problem does it solve?

This Skill solves the challenge of reconstructing deterministic procedural world generation for games or simulations, providing precise control over the creation of procedural landscapes.

Core Features & Use Cases

  • Deterministic World Generation: Rebuilds worlds based on specific seeds and parameters, ensuring reproducibility.
  • Biome and Tile Assignment: Assigns biomes and tile types to world grids with detailed control over island layouts.
  • Resource Node Placement: Accurately places resource nodes based on distance and noise algorithms.
  • Use Case: Ideal for games requiring custom-generated worlds with predictable landscapes and resource distribution.

Quick Start

Use the world-gen skill to generate a new world with the seed '1234567890' and difficulty level 'easy'.

Frequently Asked Questions about world-gen

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate deterministic procedural worlds with reproducible seeds in Python?

Deterministic procedural world generation uses specific numeric seeds and parameters to ensure reproducible landscapes. By passing a fixed seed like '1234567890' into the generation algorithm, you can reliably rebuild identical world grids and resource distributions every time.

How does seeded randomness control biome and tile assignment in procedural generation?

Seeded randomness drives biome and tile assignment by providing a deterministic sequence of pseudo-random values. This sequence controls island layouts and biome boundaries, ensuring that the generated world grid remains perfectly reproducible across multiple simulation runs.

Can I use numpy and random to place resource nodes based on distance and noise?

Yes, you can use numpy and random to place resource nodes accurately. The procedural generation process relies on distance calculations and noise algorithms alongside these libraries to determine precise resource distribution across the generated world grid.

What is the best way to ensure reproducibility when generating game landscapes and resource distribution?

The best way to ensure reproducibility in landscape and resource generation is using deterministic procedural algorithms with fixed seeds. This approach locks the pseudo-random number generation, guaranteeing that specific seeds and parameters yield identical biomes and resource placements.

Do I need prior knowledge of procedural generation algorithms to use this world generation skill?

Yes, you need prior knowledge of procedural generation algorithms and seed-based randomness. The skill provides detailed control over biome assignment and resource node placement, but requires understanding these underlying concepts to configure parameters effectively for game development.