optuna-hyperparameter-tuner
Optuna integration skill for automated hyperparameter optimization with advanced search strategies, pruning, multi-objective optimization, and visualization capabilities.
npx skills add a5c-ai/babysitter --skill optuna-hyperparameter-tuner --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Optuna Hyperparameter Tuner Optimize hyperparameters using Optuna with advanced search strategies, pruning, and visualization. ## Overview This skill provides comprehensive capabilities for hyperparameter optimization using Optuna, the state-of-the-art hyperparameter optimization framework. It supports various samplers, pruners, multi-objective optimization, and integration with popular ML frameworks. ## Capabilities ### Search Strategies - Tree-structured Parzen Estimator (TPE) - default, efficient - CMA-ES - for continuous parameters - Grid search - exhaustive - Random search - baseline - NSGAII - multi-objective optimization - QMC (Quasi-Monte Carlo) - low-discrepancy sampling ### Pruning Strategies - Median pruning - early stop underperformers - Hyperband (ASHA) - aggressive resource allocation - Percentile pruning - threshold-based - Successive Halving - efficient resource use - Wilcoxon pruning - statistical comparison ### Multi-Objective Optimization - Pareto front optimization - Multiple objective functions - Constraint handling - Trade-off visualization ### Study Management - Study persistence (SQLite, PostgreSQL, MySQL) - Study resumption - Parallel/distributed optimiza
- Overview
- Capabilities
- Search Strategies
- Pruning Strategies
- Multi-Objective Optimization
- Study Management
- Visualization
- Prerequisites
- Installation
- Optional Dependencies
- Usage Patterns
- Basic Optimization
- With Pruning
- Scikit-learn Integration
pip install optuna>=3.0.0 Database backends pip install optuna[mysql] # MySQL support pip install optuna[postgresql] # PostgreSQL support Visualization pip install optuna-dashboard # Web dashboard pip install plotly # Interactive plots Framework integrations pip install optuna-integration[sklearn] pip install optuna-integration[pytorch]
What does the optuna-hyperparameter-tuner skill do?
Optuna integration skill for automated hyperparameter optimization with advanced search strategies, pruning, multi-objective optimization, and visualization capabilities.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill optuna-hyperparameter-tuner --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.