What is the Simulation Foundations skill?

It is a meta-skill that directs Claude to the correct mathematical theories and sub-skills needed for building complex, stable simulation systems.

When should I use this skill instead of manual tuning?

Use this skill when your system requires stability guarantees, multiplayer determinism, or fast iteration that empirical 'trial-and-error' tuning cannot provide.

Does this work for multiplayer game development?

Yes, it includes specialized routing for chaos and sensitivity analysis to help identify desync sources and maintain lockstep networking determinism.

Which mathematical topics are covered by this router?

The router covers differential equations, state-space modeling, stability analysis, feedback control theory, numerical methods, and stochastic simulations.

Can it help with AI difficulty balancing?

Absolutely; it provides frameworks for using feedback control theory (PID) to create adaptive difficulty systems that track player performance smoothly.

Simulation Math Foundations

Name: Simulation Math Foundations
Author: tachyon-beep

bytachyon-beep

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Game Development

Routes developers to the optimal mathematical frameworks and simulation techniques for building stable physics, AI, and economic systems.

About

This meta-skill serves as a comprehensive entry point for mathematical simulation foundations, guiding Claude to implement rigorous theoretical models instead of relying on fragile empirical tuning. It provides structured routing logic across eight core domains—including differential equations, stability analysis, and feedback control—to help developers build predictable, high-performance systems for game physics, complex economies, and adaptive AI behavior. By prioritizing mathematical theory over 'magic number' adjustment, it ensures long-term stability and system determinism.

Key Features

5 GitHub stars
Implementation patterns for PID controllers and numerical integration methods
Analysis frameworks for managing stochastic processes and deterministic chaos
Support for ODEs, state-space modeling, and Lyapunov stability analysis
Comprehensive decision trees for ecosystem, economy, and multiplayer systems
Intelligent routing to specialized mathematical simulation sub-skills

Use Cases

Developing self-balancing game economies and resource flow systems
Implementing stable, high-performance physics engines and vehicle suspensions
Creating adaptive AI behaviors using feedback control and stochastic simulation

About

Key Features

5 GitHub stars
Implementation patterns for PID controllers and numerical integration methods
Analysis frameworks for managing stochastic processes and deterministic chaos
Support for ODEs, state-space modeling, and Lyapunov stability analysis
Comprehensive decision trees for ecosystem, economy, and multiplayer systems
Intelligent routing to specialized mathematical simulation sub-skills

Use Cases

Developing self-balancing game economies and resource flow systems
Implementing stable, high-performance physics engines and vehicle suspensions
Creating adaptive AI behaviors using feedback control and stochastic simulation