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scale-game
Imported from https://github.com/GGPrompts/my-gg-plugins.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Stars
9
Hot score
84
Updated
March 19, 2026
Overall rating
C3.9
Composite score
3.9
Best-practice grade
C64.0
Install command
npx @skill-hub/cli install ggprompts-my-gg-plugins-scale-game
Repository
GGPrompts/my-gg-plugins
Skill path: skills/problem-solving/scale-game
Imported from https://github.com/GGPrompts/my-gg-plugins.
Open repositoryBest for
Primary workflow: Ship Full Stack.
Technical facets: Full Stack.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: GGPrompts.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install scale-game into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/GGPrompts/my-gg-plugins before adding scale-game to shared team environments
- Use scale-game for development workflows
Works across
Claude CodeCodex CLIGemini CLIOpenCode
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
--- 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