best-of-n-solving

name	best-of-n-solving
description	Solve a hard problem by trying multiple approaches in parallel using isolated git worktrees. Each attempt runs in its own branch, and the best solution is selected. Use for complex refactors, tricky bugs, or architectural decisions where multiple strategies could work.

Best-of-N Problem Solving

Use this skill when facing a hard problem with multiple possible approaches — complex refactors, tricky bugs, performance optimization, or architectural decisions where you're not sure which strategy will work best.

How It Works

Cursor's best-of-n-runner subagent type creates isolated git worktrees — each attempt gets its own branch and working directory. Multiple approaches run in parallel without interfering with each other. You compare the results and pick the winner.

Steps

1. Identify the approaches — before launching, define 2-3 distinct strategies. For example, if optimizing a slow database query:

   • Approach A: Add a composite index and rewrite the query
   • Approach B: Denormalize the schema with a materialized view
   • Approach C: Add application-level caching with Redis

2. Launch parallel runners — use the Task tool with subagent_type: "best-of-n-runner" for each approach. Launch them all in a single message so they run concurrently:

   Task 1: { subagent_type: "best-of-n-runner", prompt: "Approach A: ..." }
   Task 2: { subagent_type: "best-of-n-runner", prompt: "Approach B: ..." }
   Task 3: { subagent_type: "best-of-n-runner", prompt: "Approach C: ..." }
   

   Each runner gets its own branch and worktree. Include clear success criteria in the prompt (e.g., "run the tests and report if they pass", "measure the query time").

3. Compare results — when all runners complete, evaluate:

   • Which approach passes all tests?