React and Next.js performance optimization guide with 64 prioritized rules across 8 categories. Organized by impact level, from critical waterfalls and bundle optimization down to advanced patterns, each rule includes incorrect/correct code examples and explanations Covers eight domains: async patterns, bundle size, server-side caching, client-side data fetching, re-render optimization, rendering performance, JavaScript efficiency, and advanced patterns Designed for use during component writing, code review, refactoring, and performance audits on React and Next.js applications Each rule has a prefix code (e.g., async-parallel , bundle-barrel-imports ) for easy reference in automated tooling and documentation
Guidance and reference material for instrumenting webapps with Azure Application Insights. Covers SDK setup, telemetry patterns, and configuration for ASP.NET Core and Node.js applications hosted in Azure Distinguishes between this skill (reference and guidance) and azure-prepare (actual implementation); invoke azure-prepare when the user wants to add instrumentation to their project Provides auto-instrumentation guidance for C# ASP.NET Core apps in Azure App Service, plus manual instrumentation paths for creating App Insights resources via Bicep templates or Azure CLI Includes language-specific code modification guides for ASP.NET Core, Node.js, and Python, plus quick references for OpenTelemetry SDKs and exporters
Microsoft Entra ID app registration, OAuth 2.0 configuration, and MSAL integration for secure application authentication. Covers app registration setup, authentication configuration, API permissions, and client credential management across web apps, SPAs, mobile apps, and daemon services Includes step-by-step workflows for first-time registration, console application authentication, and service-to-service credential flows Provides Azure CLI commands, MSAL library examples for .NET, JavaScript, Python, and Java, plus security best practices for secret rotation and token validation Distinguishes scope clearly: handles identity and authentication setup but excludes Azure RBAC, Key Vault secrets management, and resource security
Unified access to Azure blob storage, file shares, queues, tables, and data lake services. Supports five storage service types: Blob Storage for objects and backups, File Shares for SMB access, Queue Storage for async messaging, Table Storage for NoSQL key-value data, and Data Lake for big data analytics MCP server tools enable listing storage accounts, containers, and blobs, plus downloading and uploading blob content directly Configurable access tiers (hot, cool, cold, archive) and redundancy options (LRS, ZRS, GRS, GZRS) for cost and durability optimization CLI fallback available via az storage commands when MCP is not enabled; SDK references provided for Python, TypeScript, Java, Rust, and Go
Prepare Azure applications for deployment with infrastructure-as-code, configuration, and containerization. Generates Bicep or Terraform infrastructure code, azure.yaml configuration, and Dockerfiles based on application type and deployment target Requires mandatory plan-first workflow: analyze workspace, gather requirements, select recipe (AZD, AZCLI, Bicep, or Terraform), design architecture, then execute only after user approval Covers new app creation, modernization, and updates; routes specialized requests (copilot SDK, cloud migration, AI gateway) to dedicated skills before resuming preparation Hands off to azure-validate for infrastructure validation and azure-deploy for deployment execution; plan status must be updated to "Ready for Validation" before validation begins
Fast discovery and inventory of Azure resources across subscriptions using Resource Graph queries. Queries any Azure resource type (VMs, storage accounts, web apps, container apps, Key Vaults, etc.) across subscriptions and resource groups in a single command Supports cross-cutting searches for orphaned resources, missing tags, unhealthy states, and resource inventory counts Routes single-resource-type queries to dedicated MCP tools when available; falls back to Azure Resource Graph for broader or unsupported resource types Uses KQL (Kusto Query Language) for flexible filtering, with built-in error handling for authorization, syntax, and scope issues
Diagnose and resolve Azure Event Hubs and Service Bus SDK issues with structured troubleshooting workflows. Covers connection failures, authentication errors, AMQP link issues, message lock timeouts, and event processor stalls across Python, Java, JavaScript, and .NET SDKs Includes language-specific troubleshooting guides for Event Hubs and Service Bus, plus connectivity diagnostics for ports, WebSocket fallback, IP firewalls, and private endpoints Provides MCP tools to query resource health, list namespaces/hubs/queues/topics, and search Microsoft Learn documentation for error resolution Structured diagnosis workflow: identify SDK version, check resource health, match error messages, look up docs, verify configuration, and apply fixes
Execute Azure deployments for prepared applications with built-in error recovery and validation. Requires .azure/plan.md with Validated status from azure-validate and azure-prepare skills; will not proceed without both prerequisites Executes azd up , azd deploy , terraform apply , and az deployment commands with error handling and post-deployment verification Includes pre-deploy checklist, recipe-based deployment workflows, and post-deploy configuration for SQL managed identity and Entity Framework migrations Supports applications with API Management gateway infrastructure created during preparation phase
Recommend Azure VM sizes, scale sets, and configurations based on workload requirements and budget. Analyzes workload type, performance needs, scaling requirements, and cost to suggest 2–3 VM or VMSS options with trade-offs Verifies recommendations against live Microsoft documentation; falls back to reference files if fetch fails Queries the unauthenticated Azure Retail Prices API for current pricing without requiring an Azure subscription Guides VM vs VMSS decision through a decision tree covering autoscaling, instance count, availability, and statefulness Covers all VM families (general purpose, compute/memory optimized, GPU, confidential computing) and VMSS orchestration modes (Flexible vs Uniform)
Assess and migrate cloud workloads from AWS, GCP, and other providers to Azure services. Supports Lambda-to-Azure Functions migration with dedicated scenario reference and best practices Generates assessment reports mapping source services to Azure equivalents before any code conversion Converts source code to target Azure runtime models, with output isolated in a separate <source-folder>-azure/ directory Requires sequential phase execution: assessment first, then migration, with user confirmation before destructive actions Hands off to azure-prepare skill for infrastructure provisioning, local testing, and deployment workflows
Create, test, and iteratively improve AI agent skills with structured evaluation and benchmarking. Guides you through the full skill development lifecycle: intent capture, drafting, test case creation, evaluation, and iteration based on user feedback Runs parallel test cases with and without the skill to measure impact, capturing timing and token usage for quantitative comparison Generates an interactive browser-based reviewer showing outputs, qualitative feedback, and benchmark metrics (pass rates, latency, token efficiency) across iterations Includes description optimization to improve skill triggering accuracy by testing 20 realistic trigger/non-trigger queries and iterating the description through up to 5 rounds
Check and manage Azure quotas and usage across regions for deployment planning and capacity validation. Azure CLI ( az quota ) is the only reliable method for checking quotas; REST API and Portal are unreliable and may show misleading "No Limit" values that do not indicate unlimited capacity Supports four core workflows: checking specific resource quotas, comparing capacity across regions, requesting quota increases, and listing all quotas for a provider Quota resource names do not map 1:1 to ARM resource types; use az quota list to discover correct names by matching localizedValue descriptions Covers Microsoft.Compute, Microsoft.Network, Microsoft.App, Microsoft.Storage, and Microsoft.MachineLearningServices; some providers like Cosmos DB are unsupported and require Portal or documentation
Postgres performance optimization rules across 8 priority categories, from query tuning to advanced features. Organized into 8 rule categories prioritized by impact: query performance and connection management (critical), security and RLS, schema design, concurrency, data access patterns, monitoring, and advanced features Each rule includes detailed explanations, incorrect vs. correct SQL examples, EXPLAIN output analysis, and performance metrics to guide optimization decisions Covers query indexing, connection pooling, Row-Level Security configuration, schema design patterns, locking behavior, and Postgres-specific tuning Designed for use during SQL writing, schema design, performance reviews, and database scaling decisions
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity,…
Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking…