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SKILL verified MIT Self-run

Scale Game

skill-obra-superpowers-skills-scale-game · by obra

Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales

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Install

$ agentstack add skill-obra-superpowers-skills-scale-game

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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Archived

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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How agent discovery & health will work →
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About

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.