hyperparameter-tuner

Quick Review

The skill has a clear, simple structure appropriate for its scope, but severely lacks specificity. The description and capabilities are too generic—they describe what a hyperparameter tuning skill *should* do without providing concrete details about which tuning methods (grid search, random search, Bayesian optimization, etc.), frameworks (Optuna, Ray Tune, Hyperopt), or workflows are supported. There's no actual task knowledge, code examples, or step-by-step procedures provided. While hyperparameter tuning is valuable and complex enough to warrant a dedicated skill (good novelty), the current implementation offers no practical guidance beyond activation triggers. To improve: specify supported tuning strategies, provide concrete implementation patterns, include example configurations, and detail the actual steps the skill takes when invoked.