Expert knowledge for Azure HPC Cache development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring HPC Cache namespaces, NFS/Blob targets, client access, data ingest scripts, or cache failover, and other Azure HPC Cache related development tasks. Not for Azure Managed Lustre (use azure-managed-lustre), Azure NetApp Files (use azure-netapp-files), Azure Batch (use azure-batch), Azure Virtual Machines (use azure-virtual-machines).
npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-hpc-cache
This skill provides expert guidance for Azure HPC Cache. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, 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 |
|----------|-------|-------------|
| Troubleshooting | L37-L42 | Diagnosing and resolving Azure HPC Cache issues with Blob storage firewalls and NFS storage targets, including connectivity, access, and configuration problems. |
| Best Practices | L43-L51 | Guidance on optimizing Azure HPC Cache: client load balancing, efficient data movement and manual copy to Blob targets, NFS-on-Blob considerations, and priming caches for better hit rates. |
| Decision Making | L52-L57 | Guidance on when Azure HPC Cache is appropriate, comparing usage models, workload patterns, performance needs, and deciding if/when to adopt it for your architecture. |
| Architecture & Design Patterns | L58-L63 | Designing Azure HPC Cache namespaces across multiple back-end storage systems, and planning regional redundancy, high availability, and failover strategies for cached workloads. |
| Limits & Quotas | L64-L68 | How to request and manage Azure HPC Cache quota increases, including limits on cache instances, capacities, and the support process for raising quotas. |
| Security | L69-L76 | Configuring HPC Cache security: client access policies, directory/AD integration and extended groups, customer-managed encryption keys, and overall cache security settings. |
| Configuration | L77-L90 | Configuring and operating Azure HPC Cache: CLI setup, networking/DNS/NTP, storage targets and namespaces, mounting NFS clients, lifecycle management, metrics, and environment prerequisites. |
| Integrations & Coding Patterns | L91-L98 | Scripts and patterns for ingesting data (msrsync, parallelcp), controlling write-back with flush_file.py, and integrating Azure HPC Cache with Azure NetApp Files. |
| Deployment | L99-L103 | Creating Azure HPC Cache instances via portal/CLI, and recreating or moving existing caches to a different region while preserving configuration and data paths |
| Topic | URL |
|-------|-----|
| Work around Blob firewall issues for HPC Cache targets | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-blob-firewall-fix |
| Troubleshoot NFS storage target issues in Azure HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/troubleshoot-nas |
| Topic | URL |
|-------|-----|
| Load balance client connections across Azure HPC Cache IPs | https://learn.microsoft.com/en-us/azure/hpc-cache/client-load-balancing |
| Optimize data movement to Azure HPC Cache Blob targets | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-ingest |
| Manually copy data into Azure HPC Cache Blob targets | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-ingest-manual |
| Apply NFS Blob storage considerations with Azure HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/nfs-blob-considerations |
| Prime Azure HPC Cache to improve cache hit rates | https://learn.microsoft.com/en-us/azure/hpc-cache/prime-cache |
| Topic | URL |
|-------|-----|
| Choose Azure HPC Cache usage models | https://learn.microsoft.com/en-us/azure/hpc-cache/cache-usage-models |
| Decide when Azure HPC Cache fits your workload | https://learn.microsoft.com/en-us/azure/hpc-cache/usage-scenarios |
| Topic | URL |
|-------|-----|
| Design Azure HPC Cache aggregated namespaces | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-namespace |
| Design regional redundancy and failover for Azure HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-region-recovery |
| Topic | URL |
|-------|-----|
| Request Azure HPC Cache quota increases | https://learn.microsoft.com/en-us/azure/hpc-cache/increase-quota |
| Topic | URL |
|-------|-----|
| Define client access policies for Azure HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/access-policies |
| Configure customer-managed encryption keys for Azure HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/customer-keys |
| Configure directory services and extended groups for HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/directory-services |
| Understand security configuration for Azure HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-security-info |
| Topic | URL |
|-------|-----|
| Set up Azure HPC Cache namespace paths | https://learn.microsoft.com/en-us/azure/hpc-cache/add-namespace-paths |
| Prepare Azure CLI environment for managing HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/az-cli-prerequisites |
| Configure networking, NTP, DNS, and snapshots for HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/configuration |
| Configure storage targets for Azure HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-add-storage |
| Modify Azure HPC Cache storage target settings | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-edit-storage |
| Operate and update Azure HPC Cache instances | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-manage |
| Mount Azure HPC Cache on NFS clients | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-mount |
| Verify environment prerequisites for Azure HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-prerequisites |
| Manage Azure HPC Cache storage target lifecycle | https://learn.microsoft.com/en-us/azure/hpc-cache/manage-storage-targets |
| Monitor Azure HPC Cache metrics and reports | https://learn.microsoft.com/en-us/azure/hpc-cache/metrics |
| Topic | URL |
|-------|-----|
| Use flush_file.py to control HPC Cache write-back | https://learn.microsoft.com/en-us/azure/hpc-cache/custom-flush-script |
| Ingest data to HPC Cache using msrsync | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-ingest-msrsync |
| Use parallelcp script to ingest data for HPC Cache | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-ingest-parallelcp |
| Integrate Azure HPC Cache with Azure NetApp Files | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-netapp |
| Topic | URL |
|-------|-----|
| Create Azure HPC Cache instances via portal or CLI | https://learn.microsoft.com/en-us/azure/hpc-cache/hpc-cache-create |
| Recreate or move Azure HPC Cache to another region | https://learn.microsoft.com/en-us/azure/hpc-cache/move-resource |
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-hpc-cache 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.