Introduction

Skynet improves an artifact that you already have. You give it a starting version, a set of cases and a metric. Skynet proposes new versions, runs each one on your cases, scores the output with your metric, and keeps the best version.

What Skynet optimizes

Skynet works on four artifact kinds:

Every run uses the same contract. Your run code defines run(artifact, input). Your metric defines score(input, output, expected=None). The metric returns a score and written feedback. The optimizer reads the feedback to make the next version better.

One product, many engines

Skynet is Optimize Anything. The artifact kind decides which engines can work on it:

• Prompt, Code and Text artifacts use the native engines: GEPA, Best-of-N, Meta-Harness, AutoResearch, AutoSaddler and ShinkaEvolve.
• DSPy program artifacts use the DSPy optimizers: DSPy GEPA, DSPy MIPROv2 and DSPy BootstrapFewShot.

Auto picks for you. On a DSPy program, Auto uses DSPy GEPA. On other artifact kinds, Auto combines five native engines. Read How optimization works and Engines.

How a run works

1. You describe the task and set the starting version.
2. You give the cases. Skynet splits them into train, validation and test sets.
3. You select an optimizer, the models and a budget.
4. You write the run code and the metric. A contract check runs them on sample cases.
5. Skynet runs the optimization and shows the results.

Skynet selects versions on the validation cases. It reports the final score on the test cases. The engines never see the test cases.

Where to start