What specific dimensions are tested?

The skill evaluates Volume (number of items), Speed (latency), Users (concurrency), Duration (time-based growth), and Failure Rates (resilience).

When should I use this skill?

Use this skill when you are uncertain about production scalability, need to identify obscure edge cases, or want to validate a new architecture before implementation.

What is the Scale Game skill?

It is a specialized capability that allows Claude to analyze how software performs at extreme scales—such as 1 billion items or instant vs. year-long durations—to reveal hidden architectural flaws.

Does it actually run code at those scales?

No, it is a logical analysis framework that evaluates your code or design against extreme parameters to identify where algorithmic or architectural limits will realistically occur.

Scale Game Problem Solving

Name: Scale Game Problem Solving
Author: pathvanasoftware

bypathvanasoftware

Security & Testing

Exposes architectural weaknesses and fundamental truths by stress-testing software concepts at extreme theoretical scales.

About

The Scale Game skill empowers Claude to perform rigorous mental simulations of code and architecture by applying extreme constraints. By evaluating how a system behaves when data volume, user count, or time duration is increased or decreased by a factor of 1000, the skill reveals hidden bottlenecks like algorithmic complexity limits, memory leaks, and concurrency issues before they reach production. This systematic approach helps developers move beyond 'it works on my machine' to building robust, production-ready systems that can survive extreme real-world conditions.

Key Features

Uncovers hidden memory leaks and state growth issues over long durations
Validates error handling adequacy through extreme failure rate testing
Identifies algorithmic complexity limits through extreme volume simulation
Stress-tests logic across volume, speed, user count, and duration dimensions
Detects concurrency and resource limit problems at massive user scales
0 GitHub stars

Use Cases

Identifying potential memory leaks in long-running background processes
Optimizing API endpoints by simulating global network latency and high concurrency
Validating architectural designs for high-traffic production environments

About

Key Features

Uncovers hidden memory leaks and state growth issues over long durations
Validates error handling adequacy through extreme failure rate testing
Identifies algorithmic complexity limits through extreme volume simulation
Stress-tests logic across volume, speed, user count, and duration dimensions
Detects concurrency and resource limit problems at massive user scales
0 GitHub stars

Use Cases

Identifying potential memory leaks in long-running background processes
Optimizing API endpoints by simulating global network latency and high concurrency
Validating architectural designs for high-traffic production environments