optuna-study-creator

Optuna Study Creator - Auto-activating skill for ML Training. Triggers on: optuna study creator, optuna study creator Part of the ML Training skill category.

1.02x

Quality

Does it follow best practices?

Impact

96%

1.02x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/optuna-study-creator/SKILL.md

Evaluation results

90%

Automating Model Selection for a Churn Prediction System

Production-ready Optuna study with sklearn

Criteria

Without context

With context

Optuna study created

100%

Objective function defined

100%

Trial parameter suggestion

100%

Sklearn framework used

100%

Study direction set

100%

Reproducibility seed

75%

100%

Multiple trials run

100%

Best params accessed

100%

Results persisted

100%

Run log produced

100%

Error handling present

Without context: $0.2075 · 1m 15s · 18 turns · 19 in / 2,893 out tokens

With context: $0.4240 · 3m 22s · 26 turns · 26 in / 5,509 out tokens

100%

End-to-End Model Training Pipeline with Hyperparameter Search

Full ML pipeline with experiment tracking

Criteria

Without context

With context

Data preparation stage

100%

Model training in objective

100%

Optuna study used

100%

Three or more hyperparameters

100%

Experiment tracking CSV

100%

Trial callback or logging hook

60%

100%

Best config persisted

100%

Supported ML framework

100%

Study direction explicit

100%

Pipeline is sequential

100%

Output validation present

100%

Without context: $0.2344 · 1m 11s · 17 turns · 16 in / 3,123 out tokens

With context: $0.4591 · 1m 43s · 30 turns · 289 in / 5,008 out tokens

100%

Efficient Neural Network Architecture Search with Early Stopping

Optuna study with pruning and output validation

Criteria

Without context

With context

Optuna pruner used

100%

Trial pruning integrated

100%

PyTorch framework

100%

Architecture hyperparameters

100%

Learning rate tuned

100%

Pruned trial count reported

100%

Completed trial count reported

100%

Best trial details in summary

100%

Study direction explicit

100%

Production-ready output

100%

Optuna study object used

100%

Without context: $0.8858 · 4s · 2 turns · 4 in / 177 out tokens

With context: $0.3850 · 6m 18s · 27 turns · 286 in / 4,303 out tokens

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: f17dd51

Evaluated: 18 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Automating Model Selection for a Churn Prediction System End-to-End Model Training Pipeline with Hyperparameter Search Efficient Neural Network Architecture Search with Early Stopping

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.