Azure Hpc Cache

Organization

MicrosoftDocs

azure-hpc-cache

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).

Overview

Publisher	MicrosoftDocs
Repository	`Agent-Skills`
Skill name	`azure-hpc-cache`
Stars	778
Forks	126
Bundled files	Instructions only
License	CC-BY-4.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by MicrosoftDocs on GitHub. Read the source before you install it.

Installation

Install the Azure Hpc Cache AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/MicrosoftDocs/Agent-Skills/tree/main/skills/azure-hpc-cache
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/MicrosoftDocs/Agent-Skills.git /tmp/Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/Agent-Skills/skills/azure-hpc-cache .claude/skills/azure-hpc-cache

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Hpc Cache in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Azure Hpc Cache on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Azure Hpc Cache is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Azure HPC Cache Skill

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.

How to Use This Skill

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](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:

Preferred: Use mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.
Fallback: Use fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.

Category Index

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

Troubleshooting

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

Best Practices

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

Decision Making

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

Architecture & Design Patterns

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

Limits & Quotas

Topic	URL
Request Azure HPC Cache quota increases	https://learn.microsoft.com/en-us/azure/hpc-cache/increase-quota

Security

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

Configuration

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

Integrations & Coding Patterns

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

Deployment

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

Frequently asked questions

What does the Azure Hpc Cache AI skill do?

Why use Azure Hpc Cache on TypingMind?

Because you install it once and use it with any model. Azure Hpc Cache is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Azure Hpc Cache in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MicrosoftDocs/Agent-Skills/tree/main/skills/azure-hpc-cache. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Azure Hpc Cache?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Azure Hpc Cache?

As many as you like. As long as a model supports skills, you can use Azure Hpc Cache with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Azure Hpc Cache AI skill free?

Yes. It is published on GitHub by MicrosoftDocs under the CC-BY-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Azure HPC Cache Skill

How to Use This Skill

Category Index

Troubleshooting

Best Practices

Decision Making

Architecture & Design Patterns

Limits & Quotas

Security

Configuration

Integrations & Coding Patterns

Deployment

Frequently asked questions

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