/optimize — Autonomous Optimization v2

What It Does

Runs an autonomous optimization loop against any target. The agent modifies the target, measures the result, keeps improvements, discards failures, and repeats until it stops climbing. Two modes: metric mode for code targets that produce a number (latency, bundle size), and eval mode for skills, prompts, or agents judged by LLM-as-judge binary evals.

The Problem

Tuning a thing for a measurable outcome is slow, boring, manual work. You change a file, run the measurement, eyeball whether it got better, keep or revert, then do it again — dozens of times. People give up after a few rounds and settle for "good enough" far short of the real ceiling. The targets without a clean number (a skill's quality, a prompt's effectiveness) are worse: there's no easy way to tell if a change actually helped. This skill runs that whole loop for you and only keeps changes that measurably win.

How It Works

Two modes drive the same hill-climb loop:

• Metric mode — code targets with a shell command that produces a number (the original).
• Eval mode — skills, prompts, agents, or any text target judged by LLM-as-judge binary evals.

Inspired by Karpathy's autoresearch and extended with LLM-as-judge evaluation.