Skills-Based Gaming State Legality Calculator

When an AI scores, judges, or generates the entries

The state map above answers the classic question: does skill or chance predominate. AI-judged and AI-generated competitions add a layer the map does not capture. If an AI model scores submissions, ranks players, selects winners, or generates the images or text that players compete with, the skill-versus-chance analysis turns on the exact mechanics, not on the label "skill game."

In an AI-judged competition, reducing randomness in the judge is not enough. The operator must also examine randomness in the generation layer: prompt-to-output variance, random seeds, model drift over time, scoring margin on close results, and whether skilled players reliably beat novices over repeated rounds. Making the AI judge deterministic does not help if the AI generation step still injects chance into the player's output.

Design features that tend to strengthen a skill argument include a scoring rubric published before entry, a fixed model version, low-temperature scoring, repeated rounds, and audit logs that capture the prompt, seed, model, and score. Features that tend to create a chance argument include an inconsistent model, a generator that returns different output from the same prompt, close-score noise, and model drift. Low-temperature judging helps, but it does not cure randomness on the generation side. The strongest evidence is empirical: whether experienced players reliably outperform novices over many rounds. This is general legal information, not legal advice, and the outcome depends on your specific facts.