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Rules
7 rules available in the Claude directory
Machine Learning
JAX Best Practices: High-Performance Python Coding for Machine Learning with JIT and Vectorization
Unlock JAX best practices to supercharge your Python code for ML workloads. Learn functional programming, JIT compilation, vmap vectorization, and pure functions for optimal speed and compatibility.
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Claude DirectoryDataScience
Senior Data Science Engineer
Comprehensive system prompt for developing production-ready data science pipelines, models, and analyses using best practices.
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Claude DirectoryJAX
JAX ML Model Specialist
Specialized prompt for architecting scalable ML models with JAX, Flax, and Optax, optimized for research workflows.
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Claude DirectoryJupyter
Interactive Jupyter ML Experimenter
Creative prompt for designing reproducible ML experiment trackers and hyperparameter tuners in Jupyter, harnessing Claude Code CLI's context for hyperparameter sweeps.
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Claude DirectoryPython
Python Data Science Specialist
Specialized prompt for building efficient data pipelines, analysis, and ML workflows in Python.
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Claude Directorypython
Reproducible ML Pipelines in Python
Comprehensive rules for building production-ready, reproducible machine learning pipelines using modern Python tools.
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Claude Directorypython
Python ML Pipeline Pro
Comprehensive guide for building reproducible ML pipelines with scikit-learn, PyTorch, and MLOps tools in Claude Code.
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Claude Directory