How do I control the shape of the generated tree?

The tree shape is determined by the frontier's pop strategy. Use a standard stack pop (DFS) for long, winding paths or a queue popleft (BFS) for short, bushy branches.

Is this skill appropriate for procedural game design?

Yes, it is ideal for generating dungeon layouts, maze puzzles, and connected world maps where you need to guarantee that all areas are reachable.

Can this skill be used to find the shortest path in a maze?

While this skill is designed for tree and maze generation, the included patterns demonstrate how to use BFS on the resulting structure to solve the maze and find the shortest path.

Does this skill support graphs with cycles?

No, this pattern specifically generates trees, which are acyclic graphs. It ensures every node is connected without forming any loops or cycles.

Tree & Maze Generation Patterns

Name: Tree & Maze Generation Patterns
Author: jimmc414

byjimmc414

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Desarrollo de Videojuegos

Generates complex spanning trees, random mazes, and connected graph structures using configurable frontier-based exploration.

This skill implements the 'Norvig Pattern' for tree and maze generation, allowing Claude to produce diverse connected structures by manipulating frontier exploration strategies. By swapping the pop strategy, the skill can generate long winding paths via DFS, short bushy branches via BFS, or uniform random spanning trees. It is particularly useful for developers working on procedural content generation, network topology modeling, or any scenario requiring a cycle-free graph that visits every node exactly once.

Características Principales

01Configurable exploration strategies including DFS, BFS, and Random

02Frontier-based traversal patterns for algorithmic efficiency

03Procedural maze generation logic for grid-based structures

041 GitHub stars

05Spanning tree generation for guaranteed node coverage

06Lightweight edge-set representation for easy integration

Casos de Uso

01Building random spanning trees for network connectivity simulations

02Creating procedurally generated levels and dungeons for games

03Implementing grid-based graph coverage algorithms without cycles

Características Principales

01Configurable exploration strategies including DFS, BFS, and Random

02Frontier-based traversal patterns for algorithmic efficiency

03Procedural maze generation logic for grid-based structures

041 GitHub stars

05Spanning tree generation for guaranteed node coverage

06Lightweight edge-set representation for easy integration

Casos de Uso

01Building random spanning trees for network connectivity simulations

02Creating procedurally generated levels and dungeons for games

03Implementing grid-based graph coverage algorithms without cycles