> browse/registry

> azure-quantum

Expert knowledge for Azure Quantum development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using QDK/qdk.azure, hybrid jobs, IonQ/PASQAL/Quantinuum/Rigetti targets, QIR/OpenQASM, or Resource Estimator, and other Azure Quantum related development tasks. Not for Azure HPC Cache (use azure-hpc-cache), Azure Batch (use azure-batch), Azure Databricks (use

> data-scientist

Expert 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.

> azure-open-datasets

Expert knowledge for Azure Open Datasets development including limits & quotas. Use when handling non-Spark dataset downloads, throttling behavior, quota limits, retry logic, or rate-limit workarounds, and other Azure Open Datasets related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks), Azure Machine Learning (use azure-machine-learning).

> azure-custom-vision

Expert knowledge for Azure AI Custom Vision development including best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when exporting Custom Vision models, calling prediction APIs, using ONNX/TensorFlow, managing CMK/RBAC, or Smart Labeler, and other Azure AI Custom Vision related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-le

> azure-databricks

Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Unity Catalog, Lakeflow, Lakebase, Delta Sharing, Databricks SQL, or Model Serving workloads, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azu

> azure-local

Expert knowledge for Azure Local development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when planning Azure Local racks/SDN, Arc/Insights, Defender, BitLocker, VM migration, or multi-rack DR, and other Azure Local related development tasks. Not for Microsoft Foundry Local (use microsoft-foundry-local), Azure Stack Edge (use azure-stack-edge), Azure Kubern

> azure-personalizer

Expert knowledge for Azure AI Personalizer development including troubleshooting, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when tuning exploration/apprentice mode, single vs multi-slot calls, model export, quotas, or local inference SDK, and other Azure AI Personalizer related development tasks. Not for Azure AI services (use microsoft-foundry-tools), Azure AI Search (use azure-cognitive-search), Azure AI Metrics Advisor (use azure-metric

> azure-document-intelligence

Expert knowledge for Azure AI Document Intelligence development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AnalyzeDocument/Markdown APIs, custom models, containers/Docker, SAS/managed identity, or VNets, and other Azure AI Document Intelligence related development tasks. Not for Azure AI services (use microsoft-foundry-tools), Azure AI Search (

> azure-microsoft-foundry

Expert knowledge for Microsoft Foundry development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Microsoft Foundry applications. Not for Azure AI Foundry Local (use azure-ai-foundry-local), Azure Foundry Classic (use azure-foundry-classic), Azure Machine Learning (use azure-machine-learning), Azure Databricks (use azur

> azure-ai-ml-py

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".

> training-machine-learning-models

This skill trains machine learning models using automated workflows. It analyzes datasets, selects appropriate model types (classification, regression, etc.), configures training parameters, trains the model with cross-validation, generates performance metrics, and saves the trained model artifact. Use this skill when the user requests to "train" a model, needs to evaluate a dataset for machine learning purposes, or wants to optimize model performance. The skill supports common frameworks like s

> ml-pipeline-workflow

Build 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.

> ml-engineer

Build 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.

> building-automl-pipelines

This skill empowers Claude to build AutoML pipelines using the automl-pipeline-builder plugin. It is triggered when the user requests the creation of an automated machine learning pipeline, specifies the use of AutoML techniques, or asks for assistance in automating the machine learning model building process. The skill analyzes the context, generates code for the ML task, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this

> azure-foundry-classic

Expert knowledge for Azure Foundry Classic development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure Foundry Classic applications. Not for Azure AI Foundry Local (use azure-ai-foundry-local), Microsoft Foundry (use azure-microsoft-foundry), Azure Machine Learning (use azure-machine-learning).

> azure-business-process-tracking

Expert knowledge for Azure Business Process Tracking development including deployment. Use when creating CI/CD pipelines, automating builds, running tests, and deploying tracking solutions via DevOps tools, and other Azure Business Process Tracking related development tasks. Not for Azure Monitor (use azure-monitor), Azure Logic Apps (use azure-logic-apps), Azure Data Factory (use azure-data-factory), Azure Machine Learning (use azure-machine-learning).