Hyperparameter Tuning

Hyperparameter Tuning is an AI Engineering skill for Claude Code, published by seb1n in awesome-ai-agent-skills.

174 stars32 forkson seb1n/awesome-ai-agent-skillsAdded 2026/09/07+2% in starsRepository updated 2026/08/09

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Install in seconds

Install Hyperparameter Tuning

Copy Hyperparameter Tuning into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/seb1n/awesome-ai-agent-skills/tree/main/ai-ml-operations/hyperparameter-tuning ~/.claude/skills/hyperparameter-tuning

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/seb1n/awesome-ai-agent-skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: ai-ml-operations/hyperparameter-tuning/SKILL.md in seb1n/awesome-ai-agent-skills
Installs to: ~/.claude/skills/hyperparameter-tuning
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 3475 skills

What Hyperparameter Tuning does

Hyperparameter Tuning searches for better model settings using grid search, random search, Bayesian optimization, and Hyperband. Use it to tune models within a compute budget and compare results with cross-validation or validation splits.

Hyperparameter Tuning is cataloged under AI Engineering on DirSkills. Hyperparameter Tuning comes from a repository tagged agent-skills, ai-agent-skills, ai-agents, awesome-list and claude-code.

Documentation

README

Excerpt

Hyperparameter Tuning

This skill enables an AI agent to systematically search for optimal hyperparameter configurations for machine learning models. It covers defining search spaces, selecting search strategies (grid, random, Bayesian, Hyperband), running trials with cross-validation, applying early stopping to prune poor configurations, and analyzing results to identify the best-performing parameters. The agent balances exploration and exploitation to find strong configurations within a given computational budget.

Workflow

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Hyperparameter Tuning

What else does seb1n publish alongside Hyperparameter Tuning?
Hyperparameter Tuning is one of 25 skills that DirSkills catalogs from seb1n/awesome-ai-agent-skills, the repository it ships in. Its siblings there include API Design, API Integration and Agent Evaluation. Each one is a separate skill with its own page in this directory, installs the same way Hyperparameter Tuning does, and is maintained by seb1n in that same repository. The rest of the collection is listed on the seb1n/awesome-ai-agent-skills page.
How does Hyperparameter Tuning compare to other AI Engineering skills?
Hyperparameter Tuning ranks #3222 by stars among the 3475 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Hyperparameter Tuning against them. Open each page to compare what they document and how they install.