How does it help with GPU memory issues?

The skill identifies frequent CPU-GPU transfers, missing gradient accumulation for large batches, and suggests the use of mixed precision to prevent Out-of-Memory (OOM) errors.

Can it detect data leakage in my preprocessing steps?

Yes, it specifically looks for patterns like calling fit_transform on test data or improper validation splits that lead to over-optimistic results.

Which ML frameworks does this skill support?

The ML Reviewer skill supports PyTorch, TensorFlow, Keras, and scikit-learn, including MLOps tools like Hydra, Weights & Biases, and MLflow.

Does this skill provide code examples for fixes?

Yes, when it identifies an issue like missing gradient clipping or improper DataLoader settings, it provides 'Good' vs 'Bad' code comparisons for easy implementation.

ML Code Reviewer

Name: ML Code Reviewer
Author: physics91

byphysics91

Ciencia de Datos y ML

Reviews machine learning and deep learning code to optimize model architecture, training loops, and data pipelines for PyTorch and TensorFlow.

Acerca de

The ML Code Reviewer is a specialized skill for Claude Code designed to audit and improve machine learning projects. It provides domain-specific guidance for popular frameworks like PyTorch, TensorFlow, and scikit-learn, identifying critical issues such as data leakage, missing inference modes, and GPU bottlenecks. Whether you are transitioning a research script to production or troubleshooting training performance, this skill ensures your ML code follows industry best practices for efficiency, reproducibility, and MLOps standards.

Características Principales

Validation of MLOps patterns including experiment tracking and seed setting
Detection of critical ML bugs like data leakage and missing model.eval() calls
0 GitHub stars
Framework-specific auditing for PyTorch, TensorFlow, Keras, and scikit-learn
Automated data pipeline checks for OOM risks and preprocessing efficiency
GPU performance optimization and memory leak identification

Casos de Uso

Reviewing deep learning training scripts for performance and correctness before long-running jobs
Debugging non-reproducible experiments and inconsistent model inference results
Optimizing data loading pipelines to resolve CPU bottlenecks and maximize GPU utilization

Acerca de

Características Principales

Validation of MLOps patterns including experiment tracking and seed setting
Detection of critical ML bugs like data leakage and missing model.eval() calls
0 GitHub stars
Framework-specific auditing for PyTorch, TensorFlow, Keras, and scikit-learn
Automated data pipeline checks for OOM risks and preprocessing efficiency
GPU performance optimization and memory leak identification

Casos de Uso

Reviewing deep learning training scripts for performance and correctness before long-running jobs
Debugging non-reproducible experiments and inconsistent model inference results
Optimizing data loading pipelines to resolve CPU bottlenecks and maximize GPU utilization