Expert knowledge for Azure Table Storage development including best practices, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when designing partition/row keys, tuning throughput, configuring metrics/logs, or scripting tables via PowerShell, and other Azure Table Storage related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure Blob Storage (use azure-blob-storage), Azure Queue Storage (use azure-queue-storage), Azure Files (use azure-files).
npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-table-storage
This skill provides expert guidance for Azure Table Storage. Covers best practices, architecture & design patterns, limits & quotas, security, configuration, 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 |
|----------|-------|-------------|
| Best Practices | L34-L38 | Guidance on designing scalable table schemas, partition/row key strategies, throughput optimization, and performance tuning patterns for Azure Table storage. |
| Architecture & Design Patterns | L39-L48 | Designing Azure Table Storage schemas: partition/row key strategies, query-optimized models, handling relationships, efficient updates, and common design patterns/anti-patterns. |
| Limits & Quotas | L49-L53 | Scalability limits, throughput targets, and performance constraints for Azure Table Storage, including partition key design, request rates, and capacity planning. |
| Security | L54-L59 | Managing access to Azure Table data using Microsoft Entra ID and Azure RBAC, including assigning roles and configuring identity-based authorization. |
| Configuration | L60-L64 | Configuring Azure Table Storage monitoring: enabling metrics and logs, understanding available telemetry, and setting up alerts for performance, availability, and diagnostics. |
| Integrations & Coding Patterns | L65-L68 | Using Azure PowerShell to manage Table storage: create/delete tables, insert/query/update/delete entities, and script common data operations. |
| Topic | URL |
|-------|-----|
| Apply performance and scalability best practices for Azure Table storage | https://learn.microsoft.com/en-us/azure/storage/tables/storage-performance-checklist |
| Topic | URL |
|-------|-----|
| Design scalable, cost-efficient schemas in Azure Table storage | https://learn.microsoft.com/en-us/azure/storage/tables/table-storage-design |
| Design Azure Table storage for efficient data modification | https://learn.microsoft.com/en-us/azure/storage/tables/table-storage-design-for-modification |
| Design Azure Table storage schemas optimized for queries | https://learn.microsoft.com/en-us/azure/storage/tables/table-storage-design-for-query |
| Apply Azure Table storage design guidelines for efficient access | https://learn.microsoft.com/en-us/azure/storage/tables/table-storage-design-guidelines |
| Model relationships in Azure Table storage designs | https://learn.microsoft.com/en-us/azure/storage/tables/table-storage-design-modeling |
| Use Azure Table storage design and anti-patterns effectively | https://learn.microsoft.com/en-us/azure/storage/tables/table-storage-design-patterns |
| Topic | URL |
|-------|-----|
| Azure Table storage scalability and performance limits | https://learn.microsoft.com/en-us/azure/storage/tables/scalability-targets |
| Topic | URL |
|-------|-----|
| Assign Azure RBAC roles for Azure Table data access | https://learn.microsoft.com/en-us/azure/storage/tables/assign-azure-role-data-access |
| Authorize Azure Table storage with Microsoft Entra ID and RBAC | https://learn.microsoft.com/en-us/azure/storage/tables/authorize-access-azure-active-directory |
| Topic | URL |
|-------|-----|
| Reference monitoring metrics and logs for Azure Table storage | https://learn.microsoft.com/en-us/azure/storage/tables/monitor-table-storage-reference |
| Topic | URL |
|-------|-----|
| Use PowerShell cmdlets for Azure Table storage operations | https://learn.microsoft.com/en-us/azure/storage/tables/table-storage-how-to-use-powershell |
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-table-storage 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.