hyperparameter-tuner
Hyperparameter Tuner - Auto-activating skill for ML Training. Triggers on: hyperparameter tuner, hyperparameter tuner Part of the ML Training skill category.
npx skills add majiayu000/claude-skill-registry --skill 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.
# Hyperparameter Tuner ## Purpose This skill provides automated assistance for hyperparameter tuner tasks within the ML Training domain. ## When to Use This skill activates automatically when you: - Mention "hyperparameter tuner" in your request - Ask about hyperparameter tuner patterns or best practices - Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking. ## Capabilities - Provides step-by-step guidance for hyperparameter tuner - Follows industry best practices and patterns - Generates production-ready code and configurations - Validates outputs against common standards ## Example Triggers - "Help me with hyperparameter tuner" - "Set up hyperparameter tuner" - "How do I implement hyperparameter tuner?" ## Related Skills Part of the **ML Training** skill category. Tags: ml, training, pytorch, tensorflow, sklearn
- Purpose
- When to Use
- Capabilities
- Example Triggers
- Related Skills
What does the hyperparameter-tuner skill do?
Hyperparameter Tuner - Auto-activating skill for ML Training. Triggers on: hyperparameter tuner, hyperparameter tuner Part of the ML Training skill category.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill 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 majiayu000/claude-skill-registry, a repository with 534 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.