scale-game
Expose architecture flaws by stress-testing components at extreme minimum and maximum scales.
Install
mkdir -p .claude/skills/scale-game && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2715" && unzip -o skill.zip -d .claude/skills/scale-game && rm skill.zipInstalls to .claude/skills/scale-game
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scalesKey capabilities
- →Test system volume at 1000x scale
- →Validate architectural limits
- →Identify concurrency bottlenecks
- →Expose memory leaks in long-running processes
How it works
It tests systems at extreme scales (time, volume, users) to reveal fundamental algorithmic and resource limits that are not visible during normal operation.
Inputs & outputs
When to use scale-game
- →Validate database scalability
- →Expose concurrency bottlenecks
- →Identify memory leaks in long-running processes
About this skill
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?)
- 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
When not to use it
- →When testing only for normal operating conditions
- →When the architecture is already proven at scale
Limitations
- →Requires identifying the correct dimension to test
- →Results may not reflect all production edge cases
How it compares
It proactively tests for failure at extremes rather than assuming the system will scale linearly based on normal usage.
Compared to similar skills
scale-game side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| scale-game (this skill) | 6 | 9mo | No flags | Intermediate |
| audit | 0 | 4mo | No flags | Intermediate |
| chrome-devtools | 41 | 7mo | Review | Intermediate |
| java-pro | 34 | 4mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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