Is this skill only meant for enterprise-level applications?

No, it is equally useful for small projects to identify fundamental logic errors and state management issues that only appear under specific timing or resource constraints.

What is the Scale Game skill for Claude Code?

Scale Game is a problem-solving framework that instructs Claude to test software designs at extreme scales—such as 1000x more data or 0% uptime—to reveal hidden architectural flaws.

Can this skill help with database optimization?

Yes, by simulating billion-row datasets or extreme read/write speeds, it helps Claude recommend the right indexing, partitioning, and caching strategies.

How does Scale Game improve my software architecture?

By forcing the AI to consider extreme scenarios, it identifies where synchronous calls, memory management, or basic error handling will fail in high-volume production environments.

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

Use it during the design phase when you are uncertain about scalability, when edge cases are unclear, or when you need to validate that a local solution will survive production traffic.

Scale Game Problem Solving

Name: Scale Game Problem Solving
Author: GGPrompts

byGGPrompts

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セキュリティとテスト

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

概要

Scale Game is a diagnostic framework designed for Claude Code that helps developers identify hidden bottlenecks and scalability issues by mentally simulating extreme scenarios. By testing logic against 1000x changes in volume, speed, or duration, this skill forces the AI to look beyond local development assumptions and uncover where systems will realistically fail in production. It is an essential tool for validating new architectures, debugging intermittent concurrency issues, and preparing applications for high-traffic environments.

主な機能

Identifies algorithmic complexity limits through extreme volume simulation.
Validates production readiness by exposing the limitations of 'works in dev' logic.
Reveals hidden asynchronous requirements by testing extreme latency scenarios.
Uncovers potential memory leaks and state growth issues over simulated long durations.
Evaluates error handling adequacy by simulating 100% failure rate environments.
2 GitHub stars

ユースケース

Testing the resilience of error-handling logic in high-concurrency event-driven systems.
Validating a new microservices architecture against global network latency and concurrency.
Hardening database schemas and query logic for billion-row datasets early in the lifecycle.

概要

主な機能

Identifies algorithmic complexity limits through extreme volume simulation.
Validates production readiness by exposing the limitations of 'works in dev' logic.
Reveals hidden asynchronous requirements by testing extreme latency scenarios.
Uncovers potential memory leaks and state growth issues over simulated long durations.
Evaluates error handling adequacy by simulating 100% failure rate environments.
2 GitHub stars

ユースケース

Testing the resilience of error-handling logic in high-concurrency event-driven systems.
Validating a new microservices architecture against global network latency and concurrency.
Hardening database schemas and query logic for billion-row datasets early in the lifecycle.