What is the Scale Game skill for Claude Code?

Scale Game is a methodology-driven skill that prompts the AI to analyze your code or architecture at extreme scales (1000x larger or smaller) to find fundamental truths and breaking points.

How does testing at 'minimum' scale help?

Testing at minimum scale (e.g., 1 item or 1 user) can reveal overhead inefficiencies, initialization bugs, or unnecessary complexity that isn't needed for the core logic.

What specific dimensions does this skill analyze?

It analyzes scale across five primary dimensions: Volume (items), Speed (latency), Users (concurrency), Duration (time-based growth), and Failure Rate (resilience).

When is the best time to use the Scale Game skill?

It is best used when you are uncertain about production scalability, when edge cases are unclear, or when you need to validate that a new architecture can handle real-world volume.

Scale Game

Name: Scale Game
Author: huydepzai121

byhuydepzai121

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セキュリティとテスト

Validates architectural robustness by simulating extreme operational scales to uncover hidden bottlenecks and system limits.

概要

Scale Game is a diagnostic framework for Claude Code designed to stress-test software architectures and logic before they reach production. By applying 'extreme scale' thinking—such as imagining systems with one billion users or operations spanning several years—this skill helps developers uncover fundamental flaws like memory leaks, concurrency issues, and algorithmic inefficiencies that often remain invisible at standard developmental scales. It provides a structured process to analyze volume, speed, and duration, ensuring that your code is fundamentally sound for any environment.

主な機能

Validation of state management and long-term persistence strategies
Bottleneck discovery for synchronous versus asynchronous workflow requirements
Identification of algorithmic complexity limits through 1000x scaling
2 GitHub stars
Multi-dimensional scale analysis including volume, users, and duration
Stress-test simulation for error handling and system resilience

ユースケース

Identifying potential memory leaks or state growth in long-running processes
Validating system architecture for high-volume production environments
Refining error-handling strategies by simulating extreme failure rates

概要

主な機能

Validation of state management and long-term persistence strategies
Bottleneck discovery for synchronous versus asynchronous workflow requirements
Identification of algorithmic complexity limits through 1000x scaling
2 GitHub stars
Multi-dimensional scale analysis including volume, users, and duration
Stress-test simulation for error handling and system resilience

ユースケース

Identifying potential memory leaks or state growth in long-running processes
Validating system architecture for high-volume production environments
Refining error-handling strategies by simulating extreme failure rates