Scale Game

GitHub

一种通过极端缩放测试(如1000倍增减)来暴露系统根本缺陷和极限的设计思维技能,用于验证架构鲁棒性。

.claude/skills/problem-solving/scale-game/SKILL.md mrgoonie/claudekit-skills

Trigger Scenarios

评估系统扩展性 识别架构潜在瓶颈 设计阶段压力测试思考

Install

npx skills add mrgoonie/claudekit-skills --skill Scale Game -g -y
More Options

Non-standard path

npx skills add https://github.com/mrgoonie/claudekit-skills/tree/main/.claude/skills/problem-solving/scale-game -g -y

Use without installing

npx skills use mrgoonie/claudekit-skills@Scale Game

指定 Agent (Claude Code)

npx skills add mrgoonie/claudekit-skills --skill Scale Game -a claude-code -g -y

安装 repo 全部 skill

npx skills add mrgoonie/claudekit-skills --all -g -y

预览 repo 内 skill

npx skills add mrgoonie/claudekit-skills --list

SKILL.md

Frontmatter
{
    "name": "Scale Game",
    "version": "1.1.0",
    "description": "Test at extremes (1000x bigger\/smaller, instant\/year-long) to expose fundamental truths hidden at normal scales",
    "when_to_use": "when uncertain about scalability, edge cases unclear, or validating architecture for production volumes"
}

Scale Game

Overview

Test your approach at extreme scales to find what breaks and what surprisingly survives.

Core principle: Extremes expose fundamental truths hidden at normal scales.

Quick Reference

Scale Dimension Test At Extremes What It Reveals
Volume 1 item vs 1B items Algorithmic complexity limits
Speed Instant vs 1 year Async requirements, caching needs
Users 1 user vs 1B users Concurrency issues, resource limits
Duration Milliseconds vs years Memory leaks, state growth
Failure rate Never fails vs always fails Error handling adequacy

Process

  1. Pick dimension - What could vary extremely?
  2. Test minimum - What if this was 1000x smaller/faster/fewer?
  3. Test maximum - What if this was 1000x bigger/slower/more?
  4. Note what breaks - Where do limits appear?
  5. Note what survives - What's fundamentally sound?

Examples

Example 1: Error Handling

Normal scale: "Handle errors when they occur" works fine At 1B scale: Error volume overwhelms logging, crashes system Reveals: Need to make errors impossible (type systems) or expect them (chaos engineering)

Example 2: Synchronous APIs

Normal scale: Direct function calls work At global scale: Network latency makes synchronous calls unusable Reveals: Async/messaging becomes survival requirement, not optimization

Example 3: In-Memory State

Normal duration: Works for hours/days At years: Memory grows unbounded, eventual crash Reveals: Need persistence or periodic cleanup, can't rely on memory

Red Flags You Need This

  • "It works in dev" (but will it work in production?)
  • No idea where limits are
  • "Should scale fine" (without testing)
  • Surprised by production behavior

Remember

  • Extremes reveal fundamentals
  • What works at one scale fails at another
  • Test both directions (bigger AND smaller)
  • Use insights to validate architecture early

Version History

  • 80113d8 Current 2026-08-20 08:14

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2026-08-20 08:14

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