> browse/registry

> axiom-ios-ml

Use when deploying ANY machine learning model on-device, converting models to CoreML, compressing models, or implementing speech-to-text. Covers CoreML conversion, MLTensor, model compression (quantization/palettization/pruning), stateful models, KV-cache, multi-function models, async prediction, SpeechAnalyzer, SpeechTranscriber.

> azure-data-science-vm

Expert knowledge for Azure Data Science Virtual Machines development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when managing DSVM images/tools, IaC deployment (Bicep/ARM), Key Vault secrets, MLflow, or GPU/Jupyter issues, and other Azure Data Science Virtual Machines related development tasks. Not for Azure Virtual Machines (use azure-virtual-machines), Azure Machine Learning (use azure

> azure-health-data-services

Expert knowledge for Azure Health Data Services development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using FHIR, DICOM, MedTech, de-identification APIs, bulk data flows, or Synapse/ADF/Logic Apps integrations, and other Azure Health Data Services related development tasks. Not for Azure Health Bot (use azure-health-bot), Azure Data Factory (use azu

> azure-ai-foundry-local

Expert knowledge for Azure AI Foundry Local development including troubleshooting, best practices, decision making, configuration, and integrations & coding patterns. Use when building, debugging, or optimizing Azure AI Foundry Local applications. Not for Azure AI services (use azure-ai-services), Azure Machine Learning (use azure-machine-learning), Azure AI Vision (use azure-ai-vision), Azure AI Document Intelligence (use azure-document-intelligence).

> azure-machine-learning

Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Azure ML pipelines, AutoML, managed online/batch endpoints, prompt flow, or MLflow deployments, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse

> 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-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-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-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

> 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.

> 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

> 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).