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mrgoonie

problem-solving

@mrgoonie/problem-solving
mrgoonie
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Updated 5/6/2026
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Scale Game: Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales

Installation

$npx agent-skills-cli install @mrgoonie/problem-solving
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Details

Path.claude/skills/problem-solving/scale-game/SKILL.md
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Scoped Name@mrgoonie/problem-solving

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

npx agent-skills-cli list

Skill Instructions


name: Scale Game 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 version: 1.1.0

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 DimensionTest At ExtremesWhat It Reveals
Volume1 item vs 1B itemsAlgorithmic complexity limits
SpeedInstant vs 1 yearAsync requirements, caching needs
Users1 user vs 1B usersConcurrency issues, resource limits
DurationMilliseconds vs yearsMemory leaks, state growth
Failure rateNever fails vs always failsError 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