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Use Scale Game to stress-test [architecture or approach] at 1,000x smaller and larger scales across volume, users, duration, and failures; identify what breaks, what survives, and the resulting design priorities.
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[architecture or approach]
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SKILL.md
name: Scale Gamedescription: Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scaleswhen_to_use: when uncertain about scalability, edge cases unclear, or validating architecture for production volumesversion: 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 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
Pick dimension - What could vary extremely?
Test minimum - What if this was 1000x smaller/faster/fewer?
Test maximum - What if this was 1000x bigger/slower/more?
Note what breaks - Where do limits appear?
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?)