Overview

The Agent-Optimizer pattern, originating from Andrej Karpathy's autoresearch project, asks a deceptively simple question: what if an AI agent ran its own experiments indefinitely?

The insight is that most optimization work follows the same shape regardless of domain — you have something to improve, a way to measure improvement, rules about what you can change, and an isolated place to make changes. The agent's job is to run that loop faster and more systematically than a human would.

This skill makes that pattern available for any measurable goal: not just neural network research, but prompt tuning, email copy optimization, config parameters, CSS performance scores, query speed, or any other domain where "better" maps to a number.

The key shift: the human defines success criteria upfront. The agent handles the trial-and-error.

The Four Pillars

Every optimization project needs exactly these four things. If any pillar is missing, establish it before the loop begins.

1. Scalar Metric — The Score

A single number that goes up (or down) when things improve. Single means single — if you have multiple metrics, pick the one that matters most, or combine them into a weighted score.