Azure Managed Lustre Agent Skill

Expert knowledge for Azure Managed Lustre development including troubleshooting, best practices, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when deploying AML with AKS/Linux, Blob import/export, AzCopy POSIX migration, quotas, or Azure Monitor alerts, and other Azure Managed Lustre related development tasks. Not for Azure HPC Cache (use azure-hpc-cache), Azure NetApp Files (use azure-netapp-files), Azure Virtual Machines (use azure-virtual-machines).

2k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

681

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-managed-lustre

The instruction itself

10 sections, as written by the author

Azure Managed Lustre Skill

This skill provides expert guidance for Azure Managed Lustre. Covers troubleshooting, best practices, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. 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), 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 | L35-L40 | Diagnosing and resolving Azure Managed Lustre cluster deployment failures and performance issues, including common error causes, metrics analysis, and tuning/optimization steps. |

| Best Practices | L41-L46 | Guidance on tuning Azure Managed Lustre performance via optimal file/directory layout, client striping, and network setup (NICs, throughput, latency, and scaling). |

| Architecture & Design Patterns | L47-L52 | Designing resilient Azure Managed Lustre deployments, including regional redundancy/failover strategies and tiered checkpoint architectures for performance and data protection. |

| Limits & Quotas | L53-L57 | Configuring and managing user, group, and project storage quotas in Azure Managed Lustre, including setup steps, commands, and best practices for capacity control. |

| Security | L58-L67 | Securing Azure Managed Lustre: secure boot, firewalls/NSGs, CMK-based encryption, root squash access controls, and virtual network encryption configuration and validation. |

| Configuration | L68-L76 | Configuring AML file systems: network/storage prerequisites, auto-mount via fstab, ARM/Bicep deployment, and setting up monitoring, metrics, and alerts with Azure Monitor. |

| Integrations & Coding Patterns | L77-L88 | Patterns for connecting AML to Linux, AKS, and Terraform, plus configuring Blob import/export, auto-sync, and POSIX data migration via AzCopy. |

Troubleshooting

| Topic | URL |

|-------|-----|

| Troubleshoot Azure Managed Lustre cluster deployment issues | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/troubleshoot-deployment |

| Diagnose and fix Azure Managed Lustre performance issues | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/troubleshoot-performance |

Best Practices

| Topic | URL |

|-------|-----|

| Optimize file and directory layouts for Azure Managed Lustre performance | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/optimize-file-layouts |

| Optimize Azure Managed Lustre performance with network configuration | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/optimize-performance |

Architecture & Design Patterns

| Topic | URL |

|-------|-----|

| Design regional redundancy and failover recovery for Azure Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/amlfs-region-recovery |

| Design tiered checkpoint architecture with Azure Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/tiered-checkpoints |

Limits & Quotas

| Topic | URL |

|-------|-----|

| Configure user, group, and project quotas in Azure Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/lustre-quotas |

Security

| Topic | URL |

|-------|-----|

| Configure Secure Boot for Azure Managed Lustre clients | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/client-secure-boot |

| Use Azure Firewall with Azure Managed Lustre in hub-spoke networks | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/configure-firewall |

| Configure NSG rules for Azure Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/configure-network-security-group |

| Use customer-managed keys to encrypt Azure Managed Lustre data | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/customer-managed-encryption-keys |

| Configure root squash security settings for Azure Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/root-squash-configure-settings |

| Enable and validate virtual network encryption for Azure Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/vnet-encryption |

Configuration

| Topic | URL |

|-------|-----|

| Configure network and storage prerequisites for Azure Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/amlfs-prerequisites |

| Configure fstab for automatic Azure Managed Lustre mounts | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/automount-clients-fstab |

| Define ARM/Bicep templates for Azure Managed Lustre file systems | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/create-file-system-resource-manager |

| Configure monitoring and alerts for Azure Managed Lustre with Azure Monitor | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/monitor-file-system |

| Reference metrics and logs for Azure Managed Lustre monitoring | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/monitor-file-system-reference |

Integrations & Coding Patterns

| Topic | URL |

|-------|-----|

| Set up Azure Managed Lustre auto-export to Blob Storage | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/auto-export |

| Configure Azure Managed Lustre auto-import from Blob Storage | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/auto-import |

| Integrate Azure Blob Storage with Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/blob-integration |

| Connect Linux clients to Azure Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/connect-clients |

| Provision Azure Managed Lustre with Terraform configuration | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/create-aml-file-system-terraform |

| Create Azure Blob import jobs for Azure Managed Lustre | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/create-import-job |

| Configure export jobs from Azure Managed Lustre to Blob Storage | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/export-with-archive-jobs |

| Migrate POSIX data to Azure Managed Lustre via AzCopy and Blob | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/migrate-data-from-linux |

| Use Azure Managed Lustre with AKS via the CSI driver | https://learn.microsoft.com/en-us/azure/azure-managed-lustre/use-csi-driver-kubernetes |

Other skills for the same job

different authors, same section of the catalogue

Azure Kubernetes Automatic Readiness

by microsoft

vendor ×3

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.

17k tokens

How to use it

Copy the folder

Take microsoftdocs/azure-managed-lustre from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.