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Agent skill

Tuning Hyperparameters

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization.

MITAuto-check passedData & Analytics

Install Tuning Hyperparameters

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill tuning-hyperparameters -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace tuning-hyperparameters --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/tuning-hyperparameters .claude/skills/tuning-hyperparameters && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tuning-hyperparameters
GitHub stars
2.8k
Token cost
~1.2k tokens
SKILL.md length
542 words
Files
6 (incl. scripts, references, assets)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization.

  • Works in 4 steps: Analyzing Requirements: Claude analyzes… → Generating Code: Claude generates Python… → Executing Search: The generated code is… → …
  • Asked to tune hyperparameters
  • SKILL.md covers Overview, How It Works, When to Use This Skill and Examples, plus 7 more sections
  • With relevant phrases based on skill purpose

What it does

Tuning Hyperparameters is an agent skill from jeremylongshore/tons-of-skills-marketplace. 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.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/hyperparameter_space_template.json` and `references/README.md`). Compatibility notes: Designed for Claude Code

It sits in Data & Analytics, covering Machine learning. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Asked to tune hyperparameters
  • With relevant phrases based on skill purpose

Example prompts

  • “tune hyperparameters”
  • “optimize model”
  • “/tuning-hyperparameters”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(cmd:*)

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Analyzing Requirements: Claude analyzes the user's request to determine the model, the hyperparameters to tune, the search strategy, and…
  2. Generating Code: Claude generates Python code using appropriate ML libraries (e.g., scikit-learn, Optuna) to implement the specified…
  3. Executing Search: The generated code is executed to perform the hyperparameter search. The plugin iterates through different…
  4. Reporting Results: Claude reports the best hyperparameter configuration found during the search, along with the corresponding performance…

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(cmd:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Tuning Hyperparameters loads about 1.2k tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 77 tokens; SKILL.md has 542 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 542 words, ~1,167 tokens.

Download SKILL.mdSave it as .claude/skills/tuning-hyperparameters/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
tuning-hyperparameters
description
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.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(cmd:*)
compatibility
Designed for Claude Code
version
1.21.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
ai, ml, performance

Hyperparameter Tuner

Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization to maximize performance.

Overview

This skill empowers Claude to fine-tune machine learning models by automatically searching for the optimal hyperparameter configurations. It leverages different search strategies (grid, random, Bayesian) to efficiently explore the hyperparameter space and identify settings that maximize model performance.

How It Works

  1. Analyzing Requirements: Claude analyzes the user's request to determine the model, the hyperparameters to tune, the search strategy, and the evaluation metric.
  2. Generating Code: Claude generates Python code using appropriate ML libraries (e.g., scikit-learn, Optuna) to implement the specified hyperparameter search. The code includes data loading, preprocessing, model training, and evaluation.
  3. Executing Search: The generated code is executed to perform the hyperparameter search. The plugin iterates through different hyperparameter combinations, trains the model with each combination, and evaluates its performance.
  4. Reporting Results: Claude reports the best hyperparameter configuration found during the search, along with the corresponding performance metrics. It also provides insights into the search process and potential areas for further optimization.

When to Use This Skill

This skill activates when you need to:

  • Optimize the performance of a machine learning model.
  • Automatically search for the best hyperparameter settings.
  • Compare different hyperparameter search strategies.
  • Improve model accuracy, precision, recall, or other relevant metrics.

Examples

Example 1: Optimizing a Random Forest Model

User request: "Tune hyperparameters of a Random Forest model using grid search to maximize accuracy on the iris dataset. Consider n_estimators and max_depth."

The skill will:

  1. Generate code to perform a grid search over the specified hyperparameters (n_estimators, max_depth) of a Random Forest model using the iris dataset.
  2. Execute the grid search and report the best hyperparameter combination and the corresponding accuracy score.
Example 2: Using Bayesian Optimization

User request: "Optimize a Gradient Boosting model using Bayesian optimization with Optuna to minimize the root mean squared error on the Boston housing dataset."

The skill will:

  1. Generate code to perform Bayesian optimization using Optuna to find the best hyperparameters for a Gradient Boosting model on the Boston housing dataset.
  2. Execute the optimization and report the best hyperparameter combination and the corresponding RMSE.
Show full SKILL.md (182 more words)Show less

Best Practices

  • Define Search Space: Clearly define the range and type of values for each hyperparameter to be tuned.
  • Choose Appropriate Strategy: Select the hyperparameter search strategy (grid, random, Bayesian) based on the complexity of the hyperparameter space and the available computational resources. Bayesian optimization is generally more efficient for complex spaces.
  • Use Cross-Validation: Implement cross-validation to ensure the robustness of the evaluation metric and prevent overfitting.

Integration

This skill integrates seamlessly with other Claude Code plugins that involve machine learning tasks, such as data analysis, model training, and deployment. It can be used in conjunction with data visualization tools to gain insights into the impact of different hyperparameter settings on model performance.

Prerequisites

  • Appropriate file access permissions
  • Required dependencies installed

Instructions

  1. Invoke this skill when the trigger conditions are met
  2. Provide necessary context and parameters
  3. Review the generated output
  4. Apply modifications as needed

Output

The skill produces structured output relevant to the task.

Error Handling

  • Invalid input: Prompts for correction
  • Missing dependencies: Lists required components
  • Permission errors: Suggests remediation steps

Resources

  • Project documentation
  • Related skills and commands

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references, assets) in skills/.curated/tuning-hyperparameters of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • assets/README.md
  • assets/hyperparameter_space_template.json
  • assets/visualization_template.html
  • references/README.md
  • scripts/README.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Tuning Hyperparameters next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tuning Hyperparameters compared with similar skills
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Tuning Hyperparameters this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.2kAutomated safety check: PassMIT
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Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill188—~4kAutomated safety check: PassMIT
Senior Data ScientistRaidriar7170/hermes-skilleval1255 repos~1.4kAutomated safety check: PassMIT
Geomlitalo-goncalves/geoML109—~4.9kAutomated safety check: PassGPL-3.0
QuantMind Training Config Generatorqusong0627/QuantMind1.7k—~1.5kAutomated safety check: PassAGPL-3.0

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Categories

Questions about Tuning Hyperparameters

What does Tuning Hyperparameters do?

Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. Tuning Hyperparameters is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization.

When should I use Tuning Hyperparameters?

Tuning Hyperparameters fits situations like: asked to tune hyperparameters; with relevant phrases based on skill purpose.

How do I install Tuning Hyperparameters in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill tuning-hyperparameters -a claude-code`. Or copy the skill folder (skills/.curated/tuning-hyperparameters in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/tuning-hyperparameters in your project. Claude Code loads it when a task matches its description.

How do I install Tuning Hyperparameters in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill tuning-hyperparameters -a codex`. Or copy the skill folder (skills/.curated/tuning-hyperparameters in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/tuning-hyperparameters in your project. Codex loads it when a task matches its description.

Can I use Tuning Hyperparameters in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill tuning-hyperparameters -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tuning-hyperparameters, .gemini/skills/tuning-hyperparameters, .github/skills/tuning-hyperparameters and .opencode/skills/tuning-hyperparameters in your project.

What does Tuning Hyperparameters need to run?

SKILL.md names no scripts, command-line tools or credentials: Tuning Hyperparameters is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Tuning Hyperparameters access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Tuning Hyperparameters safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Tuning Hyperparameters use?

Tuning Hyperparameters is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tuning Hyperparameters use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16 tokens, read only when the agent opens those files.

What are the alternatives to Tuning Hyperparameters?

Skills that share tags, products or a category with Tuning Hyperparameters: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars) and Geoml (italo-goncalves/geoML, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tuning Hyperparameters?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.