home / skills / jeremylongshore / claude-code-plugins-plus-skills / data-augmentation-pipeline

data-augmentation-pipeline skill

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/skills/07-ml-training/data-augmentation-pipeline

This skill helps you implement data augmentation pipelines with production-ready guidance, configurations, and validation for ML training.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill data-augmentation-pipeline

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: "data-augmentation-pipeline"
description: |
  Process data augmentation pipeline operations. Auto-activating skill for ML Training.
  Triggers on: data augmentation pipeline, data augmentation pipeline
  Part of the ML Training skill category. Use when working with data augmentation pipeline functionality. Trigger with phrases like "data augmentation pipeline", "data pipeline", "data".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Data Augmentation Pipeline

## Overview

This skill provides automated assistance for data augmentation pipeline tasks within the ML Training domain.

## When to Use

This skill activates automatically when you:
- Mention "data augmentation pipeline" in your request
- Ask about data augmentation pipeline patterns or best practices
- Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.

## Instructions

1. Provides step-by-step guidance for data augmentation pipeline
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with data augmentation pipeline"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml training concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| 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 |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Training** skill category.
Tags: ml, training, pytorch, tensorflow, sklearn

Overview

This skill automates common tasks for building and running data augmentation pipelines used in ML training. It guides pipeline design, generates production-ready code and configuration, and validates outputs against standard checks. The skill is auto-activated for requests mentioning data augmentation pipeline or related data pipeline topics. Use it to speed up data preparation, reduce manual errors, and standardize augmentation workflows.

How this skill works

The skill inspects your pipeline requirements, data schema, and augmentation goals, then recommends patterns and produces code snippets or config files for frameworks like PyTorch, TensorFlow, or scikit-learn. It outputs step-by-step instructions, validation checks (schema, shape, type), and suggestions for integration with training loops and experiment tracking. It also flags common configuration errors and offers remediation steps.

When to use it

Designing or iterating on a data augmentation strategy for training models
Generating augmentation code snippets or configuration for PyTorch, TensorFlow, or scikit-learn
Validating augmented data for schema, type, and distribution issues before training
Integrating augmentation steps into data pipelines or CI/CD for ML
Troubleshooting augmentation-related training failures or data drift

Best practices

Start with a small, reproducible augmentation script and add complexity incrementally
Maintain deterministic behavior for experiments by controlling random seeds and augment params
Validate augmented outputs with unit tests: schema, shapes, label consistency, and range checks
Use config-driven augmentation definitions to separate logic from parameters and enable reproducibility
Monitor distribution shifts introduced by augmentations and log stats to experiment tracking

Example use cases

Create a config and PyTorch Dataset + transform pipeline for image classification with randomized flips, crops, and color jitter
Generate TensorFlow data pipeline code that applies deterministic augmentations for training and lighter transforms for validation
Add pre-processing checks that ensure augmented samples preserve class labels and expected dimensions
Produce CI steps to run augmentation smoke tests and validate sample statistics before model training
Suggest hyperparameter ranges for augmentation intensity and integrate them into hyperparameter tuning jobs

FAQ

Can the skill produce ready-to-run code for my framework?

Yes. Provide the target framework (PyTorch, TensorFlow, scikit-learn), data shape, and sample schema; the skill outputs code and configuration tailored to those inputs.

How does the skill validate augmented data?

It runs checks for schema conformity, tensor shapes, dtype correctness, label alignment, and basic statistical properties; it also highlights likely causes of common errors and proposes fixes.