python-development
claudecodexModern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices
16 skills · 1 command · 3 agents
0
claudecodex
machine-learning-ops
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
/
Plugin machine-learning-ops from marketplace claude-code-workflows in milkandginnn/Syxx-Dimensions. Commands read from the repo's manifest — 5 days ago.
What it installs
Skills (2)
ml-pipeline-workflowBuild 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.
recsys-pipeline-architectDesign composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X For You algorithm. Use when building any system that picks "the top K items for a (user, context)" — content feeds, search
Slash commands (1)
/machine-learning-ops:ml-pipeline
Subagents (3)
data-scientistExpert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
ml-engineerBuild production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
mlops-engineerBuild comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
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