From jeremylongshore-claude-code-plugins-plus-skills
Guides data augmentation pipeline operations for ML training, including data preparation, model training, hyperparameter tuning, and experiment tracking.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:data-augmentation-pipelineThis 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 data augmentation pipeline tasks within the ML Training domain.
This skill provides automated assistance for data augmentation pipeline tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with data augmentation pipeline" 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-reasoningBuilds end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Build efficient data pipelines with TensorFlow's tf.data API for ML training, including dataset creation from tensors/generators, normalization, augmentation, batching, shuffling, prefetching, and caching.
Orchestrates end-to-end ML pipelines from data ingestion through model deployment, covering DAG workflows, data validation, training, validation, and deployment automation.