Expert knowledge for Azure Cloud Hsm development including troubleshooting, best practices, limits & quotas, security, and integrations & coding patterns. Use when configuring Cloud HSM auth/network, PKCS#11 cert storage, key lifecycle, quotas, or cluster sync issues, and other Azure Cloud Hsm related development tasks. Not for Azure Dedicated HSM (use azure-dedicated-hsm), Azure Payment Hsm (use azure-payment-hsm), Azure Key Vault (use azure-key-vault), Azure Attestation (use azure-attestation).
npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-cloud-hsm
This skill provides expert guidance for Azure Cloud Hsm. Covers troubleshooting, best practices, limits & quotas, security, and integrations & coding patterns. 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 |
|----------|-------|-------------|
| Troubleshooting | L33-L38 | Diagnosing and fixing Azure Cloud HSM cluster issues, including user/key synchronization problems, common error codes, connectivity failures, and operational faults. |
| Best Practices | L39-L43 | Guidance on secure key lifecycle management, HSM partition/role design, access control, network and operational hardening, and compliance-oriented security practices for Azure Cloud HSM deployments. |
| Limits & Quotas | L44-L49 | Service capacity limits (objects, transactions), quotas, and which cryptographic algorithms and key sizes are supported by Azure Cloud HSM |
| Security | L50-L60 | Security setup for Cloud HSM: auth methods, compliance, network hardening, user management, and configuring/streaming operation logs to Event Hubs. |
| Integrations & Coding Patterns | L61-L65 | Using PKCS#11 with Azure Cloud HSM to store, access, and manage X.509 certificates, including configuring certificate storage and integration patterns for apps and services. |
| Topic | URL |
|-------|-----|
| Fix user and key sync issues in Azure Cloud HSM clusters | https://learn.microsoft.com/en-us/azure/cloud-hsm/synchronize-users-keys |
| Troubleshoot common Azure Cloud HSM errors and issues | https://learn.microsoft.com/en-us/azure/cloud-hsm/troubleshoot |
| Topic | URL |
|-------|-----|
| Apply key management best practices in Cloud HSM | https://learn.microsoft.com/en-us/azure/cloud-hsm/key-management |
| Topic | URL |
|-------|-----|
| Review Azure Cloud HSM service object and transaction limits | https://learn.microsoft.com/en-us/azure/cloud-hsm/service-limits |
| Review supported algorithms and key sizes in Azure Cloud HSM | https://learn.microsoft.com/en-us/azure/cloud-hsm/supported-algorithms |
| Topic | URL |
|-------|-----|
| Configure authentication methods for Azure Cloud HSM | https://learn.microsoft.com/en-us/azure/cloud-hsm/authentication |
| Understand security, compliance, and usage for Azure Cloud HSM | https://learn.microsoft.com/en-us/azure/cloud-hsm/faq |
| Secure Azure Cloud HSM network with endpoints, DNS, and NSGs | https://learn.microsoft.com/en-us/azure/cloud-hsm/network-security |
| Apply security best practices to Azure Cloud HSM deployments | https://learn.microsoft.com/en-us/azure/cloud-hsm/secure-cloud-hsm |
| Route Azure Cloud HSM logs to Event Hubs | https://learn.microsoft.com/en-us/azure/cloud-hsm/tutorial-configure-event-hub |
| Configure and query Azure Cloud HSM operation logs | https://learn.microsoft.com/en-us/azure/cloud-hsm/tutorial-operation-event-logging |
| Implement secure user management in Azure Cloud HSM | https://learn.microsoft.com/en-us/azure/cloud-hsm/user-management |
| Topic | URL |
|-------|-----|
| Use PKCS#11 API to manage certificates in Azure Cloud HSM | https://learn.microsoft.com/en-us/azure/cloud-hsm/pkcs-api-certificate-storage |
| Configure PKCS#11 certificate storage with Azure Cloud HSM | https://learn.microsoft.com/en-us/azure/cloud-hsm/tutorial-certificate-storage |
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-cloud-hsm 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.