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-learning), Azure AI Foundry Local (use microsoft-foundry-local).
npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-custom-vision
This skill provides expert guidance for Azure AI Custom Vision. Covers best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
> IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g., L35-L120), use read_file with the specified lines. For categories with file links (e.g., security.md), use read_file on the linked reference file
> IMPORTANT for Agent: If metadata.generated_at is more than 3 months old, suggest the user pull the latest version from the repository. If mcp_microsoftdocs tools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.| Category | Lines | Description |
|----------|-------|-------------|
| Best Practices | L34-L39 | Improving Custom Vision model quality with better data collection/labeling strategies and using Smart Labeler to speed and automate image annotation |
| Decision Making | L40-L45 | Guidance on selecting the best Custom Vision domain for your scenario and planning migrations from Custom Vision to other Azure or third‑party vision services. |
| Limits & Quotas | L46-L50 | Details on Custom Vision usage limits per pricing tier, including training/prediction quotas, project and image caps, and how limits affect model training and deployment. |
| Security | L51-L57 | Managing Custom Vision security: encryption with customer-managed keys, secure data handling/export/deletion, and configuring Azure RBAC roles and permissions. |
| Integrations & Coding Patterns | L58-L68 | Using Custom Vision models and APIs in apps: exporting via SDK, running ONNX/TensorFlow in Windows ML/Python, calling classification/detection APIs, and integrating with Azure Storage. |
| Deployment | L69-L73 | Deploying Custom Vision models: copying/backing up projects across regions and exporting models for offline, edge, and mobile (TensorFlow, ONNX, iOS/Android) use. |
| Topic | URL |
|-------|-----|
| Apply Custom Vision data strategies to improve models | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/getting-started-improving-your-classifier |
| Speed up Custom Vision labeling with Smart Labeler | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/suggested-tags |
| Topic | URL |
|-------|-----|
| Plan migration from Custom Vision to alternative services | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/migration-options |
| Choose the right Custom Vision domain for your project | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/select-domain |
| Topic | URL |
|-------|-----|
| Review Custom Vision limits and quotas by tier | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/limits-and-quotas |
| Topic | URL |
|-------|-----|
| Configure customer-managed keys for Custom Vision encryption | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/encrypt-data-at-rest |
| View, export, and delete Custom Vision data securely | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/export-delete-data |
| Configure Azure RBAC roles for Custom Vision projects | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/role-based-access-control |
| Topic | URL |
|-------|-----|
| Integrate Custom Vision ONNX models with Windows ML apps | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/custom-vision-onnx-windows-ml |
| Run exported Custom Vision TensorFlow models in Python | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/export-model-python |
| Export Custom Vision models programmatically with SDK | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/export-programmatically |
| Use Custom Vision SDK for image classification | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/quickstarts/image-classification |
| Call Custom Vision object detection APIs with SDK | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/quickstarts/object-detection |
| Integrate Custom Vision with Azure Storage queues and blobs | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/storage-integration |
| Use Custom Vision prediction API to test images | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/use-prediction-api |
| Topic | URL |
|-------|-----|
| Copy and back up Custom Vision projects across regions | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/copy-move-projects |
| Export Custom Vision models for offline and mobile use | https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/export-your-model |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take microsoftdocs/azure-custom-vision from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.