Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. Finds best parameter configurations to maximize performance. Use when asked to "tune hyperparameters" or "optimize model". Trigger with relevant phrases based on skill purpose.
5.8
Rating
0
Installs
Machine Learning
Category
This hyperparameter tuning skill provides a solid foundation with clear examples and good task knowledge. The description adequately covers what the skill does (grid/random/Bayesian search for ML hyperparameter optimization), and the workflow is well-explained. The structure is reasonable with separate Python scripts for different search strategies referenced in the scripts/ directory. However, the skill addresses a relatively standard ML task that modern CLI agents with access to scikit-learn/Optuna documentation could handle reasonably well, limiting novelty. The SKILL.md includes some generic boilerplate sections (Prerequisites, Instructions, Error Handling) that add clutter without specific details. Overall, this is a competent skill that would save time on hyperparameter tuning tasks, though it's not highly novel or uniquely complex.
jeremylongshore
Skill Author
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