Rules

8 rules available in the Claude directory

Advanced prompt for detecting deviations, fraud, or faults in trajectories from drones, sports, or logistics using ML and stats.

Creative prompt for building fair, secure, and scalable ML pipelines with emphasis on ethics, monitoring, and production readiness.

System prompt for designing reproducible ML experiments, tracking metrics, and managing artifacts with tools like MLflow and Weights & Biases.

Comprehensive system prompt for developing, training, and deploying production-ready machine learning models using best practices.

Adapted guidelines for writing high-performance JAX code in Python, optimized for Claude Code CLI's long context and tool integration.

Design production ML workflows with PyTorch, MLflow, Ray for scalable training and serving.

Expert prompt for deploying production ML pipelines in Python with MLflow, Scikit-learn, and Ray, using Claude's tools for experiment tracking.

Production ML pipelines with PyTorch, MLflow, and Kubeflow for scalable model deployment.