Scale Game
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
Install
npx skills add https://github.com/VoDaiLocz/kilo-kit-mcp/tree/main/skills/problem-solving/scale-game
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install vodailocz-kilo-kit-mcp@llmmart
git clone https://github.com/VoDaiLocz/kilo-kit-mcp.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole vodailocz/kilo-kit-mcp collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
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
Files (kilo-kit-mcp)
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SKILL.md 2.3 KB
--- 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 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
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