From jeremylongshore-claude-code-plugins-plus-skills
Guides feature engineering operations for ML training, including data preparation, transformation, and preprocessing code generation using scikit-learn, PyTorch, and TensorFlow.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:feature-engineering-helperThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for feature engineering helper tasks within the ML Training domain.
This skill provides automated assistance for feature engineering helper tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with feature engineering helper" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin ejentum-reasoningGenerates Python code to create, select, and transform features for ML models. Activates on requests for feature engineering, scaling, encoding, or importance analysis.
Expert ML engineer skill for building production-ready ML systems. Covers model serving, feature engineering, distributed training, and monitoring with PyTorch/TensorFlow.
Guides feature importance analysis for ML training tasks like model evaluation and dataset auditing.