Manage Azure Container Registry via the az acr CLI including registries, images, cloud builds, ACR Tasks, authentication, tokens, geo-replication, and networking. Use when working with ACR, az acr commands, pushing/importing/purging container images in Azure, or when the user mentions Azure Container Registry.
npx skills add https://github.com/github/awesome-copilot --skill azure-container-registry-cli
Manage Azure Container Registry (ACR) resources using the az acr command group of the Azure CLI.
CLI: az acr ships with core Azure CLI — no extension required (the acrtransfer extension is only needed for export/import pipelines).
# Install Azure CLI
brew install azure-cli # macOS
curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash # Linux
winget install Microsoft.AzureCLI # Windows
# Sign in and select subscription
az login
az account set --subscription {subscription-id}
# Create a registry (SKU: Basic | Standard | Premium)
az acr create --resource-group {rg} --name {registry} --sku Standard
# Authenticate Docker/Podman against the registry
az acr login --name {registry}
# Build and push in the cloud — no local Docker needed
az acr build --registry {registry} --image app:v1 .
# Copy an image from another registry without pull/push
az acr import --name {registry} --source mcr.microsoft.com/hello-world:latest
# List repositories and tags
az acr repository list --name {registry} --output table
az acr repository show-tags --name {registry} --repository app --orderby time_desc
# Diagnose registry connectivity and configuration
az acr check-health --name {registry} --yes
az acr build / ACR Tasks over local docker build + docker push: builds run in Azure, work without a local daemon, and integrate with triggers.az acr import to move images between registries: it is server-side, faster, and requires no local storage.AcrPull/AcrPush, or Container Registry Repository Reader/Writer on ABAC-enabled registries), repository-scoped tokens, or managed identities.az acr
├── create / delete / list / show / update # Registry lifecycle
├── login # Docker credential helper (or --expose-token)
├── check-health / check-name / show-usage # Diagnostics & quota
├── build # Cloud image build (quick task)
├── run # Run a command / multi-step task once
├── task # ACR Tasks (triggers, timers, logs, runs)
├── agentpool # Dedicated task agent pools (Premium)
├── import # Server-side image copy into the registry
├── repository # List/show/delete/untag repos & tags, lock images
├── manifest # Manifest metadata, delete, OCI referrers
├── credential # Admin user credentials (avoid in production)
├── token / scope-map # Repository-scoped tokens (Premium)
├── replication # Geo-replication (Premium)
├── network-rule # IP network rules
├── private-endpoint-connection # Private Link approvals
├── config # content-trust, retention, soft-delete, ...
├── cache / credential-set # Artifact cache (pull-through cache) rules
├── webhook # Push/delete event webhooks
├── connected-registry # On-premises / IoT connected registries
└── export-pipeline / import-pipeline / pipeline-run # acrtransfer extension
Read the relevant reference file based on the user's task. Each file contains complete command syntax and examples for its domain.
| File | When to read | Covers |
|---|---|---|
| references/auth-and-security.md | Login failures, permissions, CI/CD or AKS pull access | az acr login (incl. --expose-token), Entra RBAC roles, service principals, managed identities, --attach-acr for AKS, repository-scoped tokens & scope maps, admin user, content trust |
| references/build-and-tasks.md | Building images in Azure, automation, CI triggers | az acr build, az acr run, multi-step task YAML, az acr task (git/base-image/timer triggers, logs, runs), agent pools |
| references/images-and-artifacts.md | Managing repos, tags, cleanup, storage costs | az acr import, repository & manifest commands, untag vs delete, purge (acr purge), image locking, retention policy, soft delete, artifact cache, show-usage |
| references/networking-and-geo.md | Multi-region, private access, edge scenarios | Geo-replication, zone redundancy, private endpoints, network rules, dedicated data endpoints, connected registries, registry transfer pipelines |
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 github/azure-container-registry-cli 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.
The instructions reference brew.
Without those the skill loads but fails at the first command.