I built an opensource skill that runs N implementations in parallel, has each one reviewed blind by a separate agent, then a judge picks the winner or synthesizes the best parts of each.

Each slot can use a different skill (CE:work in one vs superpowers:test-driven-development) and harness (CC vs. Codex). Or put different emphasis on each slot (functional vs. robustness). Also works for non-coding tasks (writing) and you can create custom slot-machines.

The main insight is simple enough: AI agents are probabilistic. The same spec produces different code every time; different designs, different bugs, different quality.So running parallel implementations can increase quality if we can judge effectively.

There is a real tradeoff here of increased time and tokens for quality but for production code this is often worth it; particularly on long running overnight loops on subscription plans. Also a simple way to learn which skills and harnesses actually work best for your codebase.

Appreciate any feedback / PRs. This is early!