microsoft/GitHub-Copilot-for-Azure▌

Analyzes markdown files and suggests token-reduction optimizations while preserving clarity. \n \n Counts tokens using a 4-character-per-token approximation and identifies token-wasting patterns including emojis, verbosity, duplication, and oversized code blocks \n Generates a detailed suggestions table showing issue location, description, recommended fix, and estimated token savings \n Targets SKILL.md files for under 500 tokens and reference files for under 1000 tokens \n Provides recommendati

Unified access to Azure blob storage, file shares, queues, tables, and data lakes with lifecycle management and redundancy options. \n \n 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, and Data Lake for big data analytics \n MCP server tools for listing accounts, containers, and blobs, plus downloading and uploading blob content; CLI fallback available via az storage commands \n C

Iteratively improve skill frontmatter compliance and test coverage using the Ralph loop pattern. \n \n Automates a 10-step feedback loop: read skill metadata, score compliance against the agentskills.io spec, scaffold missing tests, improve frontmatter triggers, run tests, validate references, check token budgets, and prompt for commit/issue creation \n Targets Medium-High compliance: distinctive WHEN: trigger phrases, descriptions under 60 words, passing tests, and token budgets under 500 lines

End-to-end deployment, evaluation, and lifecycle management for Microsoft Foundry agents. \n \n Covers the complete agent development lifecycle: create, deploy (Docker build, ACR push), invoke, evaluate, optimize prompts, and troubleshoot \n Organized into specialized sub-skills for deploy, invoke, observe (evaluation and prompt optimization), trace, troubleshoot, and dataset curation from production traces \n Supports both prompt-based agents (LLM-backed) and hosted agents (container-based cust

Creates a GitHub issue in microsoft/github-copilot-for-azure for integration test failures.

Build and deploy GitHub Copilot SDK applications to Azure with flexible model configuration. \n \n Three scaffolding paths: create new greenfield projects, add SDK services to existing repos, or deploy existing SDK apps with Azure infrastructure \n Supports three model configurations: GitHub's default models, specific GitHub models via discovery, or bring-your-own-model (BYOM) on Azure with DefaultAzureCredential authentication \n Template includes Express/TypeScript API, React/Vite frontend, Bi

Systematic diagnosis and remediation for Azure production issues using AppLens, Azure Monitor, and resource health checks. \n \n Covers Container Apps, Function Apps, and AKS clusters with service-specific troubleshooting guides for image pulls, cold starts, health probes, invocation failures, and node/pod issues \n Includes AppLens MCP integration for AI-powered root cause analysis and Azure Monitor MCP for KQL-based log and metric queries \n Provides a five-step diagnostic flow: identify sympt

Assess and migrate cloud workloads from AWS, GCP, and other providers to Azure services. \n \n Supports migration from AWS Lambda to Azure Functions with scenario-specific conversion rules and best practices \n Generates assessment reports mapping source services to Azure equivalents before any code conversion \n Converts source code to target Azure runtime models using documented migration patterns and MCP tools for best practices \n Outputs all artifacts to a separate <source-folder>-azur

Guidance and reference material for instrumenting webapps with Azure Application Insights. \n \n Covers SDK setup, telemetry patterns, and configuration for ASP.NET Core, Node.js, and Python applications hosted in Azure \n Distinguishes between this skill (reference and guidance) and azure-prepare (which orchestrates actual instrumentation changes) \n Recommends auto-instrumentation for C# ASP.NET Core apps on Azure App Service; provides manual instrumentation paths for other stacks \n Includes

Find minimal Azure RBAC roles, generate assignment commands, and provide Bicep infrastructure code. \n \n Identifies built-in roles matching desired permissions using Azure documentation, or generates custom role definitions when no built-in role fits \n Produces Azure CLI commands and Bicep code snippets for role assignments to identities, managed identities, and service principals \n Documents prerequisites for granting roles, including required permissions and recommended least-privilege role

Identify and quantify Azure cost savings through resource analysis, utilization metrics, and actionable optimization recommendations. \n \n Discovers orphaned resources (unattached disks, unused NICs, idle gateways) and over-provisioned services using Azure Quick Review scans \n Queries actual costs from Azure Cost Management API and utilization data from Azure Monitor to support rightsizing recommendations \n Generates prioritized optimization reports with estimated savings, validated pricing,

Prepare Azure applications for deployment with infrastructure-as-code, configuration, and containerization. \n \n Generates Bicep or Terraform infrastructure code, azure.yaml configuration, and Dockerfiles based on application type and requirements \n Follows mandatory plan-first workflow: analyze workspace, gather requirements, select deployment recipe (AZD, AZCLI, Bicep, or Terraform), design architecture, and get user approval before code generation \n Supports creation, modernization, and up

Diagnose and resolve Azure Event Hubs and Service Bus SDK issues across languages. \n \n Covers connection failures, authentication errors, AMQP link issues, message lock timeouts, and event processor stalls across Python, Java, JavaScript, and .NET SDKs \n Includes language-specific troubleshooting guides, configuration best practices (retry, prefetch, batch size), and checkpoint/dead-letter diagnostics \n Integrates with Azure Monitor for KQL log queries, resource health checks, and Microsoft

Azure compliance scanning, Key Vault expiration auditing, and resource configuration validation against best practices. \n \n Runs azqr (Azure Quick Review) for comprehensive compliance assessment across subscriptions and resource groups \n Monitors Key Vault keys, secrets, and certificates for expiration dates and identifies items without expiration policies \n Detects orphaned resources, misconfigured settings, and security posture issues through Resource Graph queries \n Classifies findings b

Downloads artifacts from a GitHub Actions integration test run, generates a summarized skill invocation report, and files GitHub issues for each test failure with root-cause analysis.

Guidelines and validation for writing Agent Skills compliant with agentskills.io specification. \n \n Covers skill structure (SKILL.md, references/, scripts/), frontmatter constraints (name format, description limits), and token budgets (SKILL.md <5000 tokens, references <1000 each) \n Enforces metadata best practices: use WHEN: trigger phrases in descriptions, avoid DO NOT USE FOR: keywords, keep descriptions under 60 words \n Implements progressive disclosure with just-in-time refere

Recommend Azure VM sizes, scale sets, and configurations based on workload type, performance needs, and budget. \n \n Analyzes workload requirements (web, database, ML, batch, HPC) and recommends appropriate VM families, sizes, and instance counts without requiring an Azure subscription \n Supports both single VMs and VM Scale Sets (VMSS) with guidance on autoscaling, load balancing, and orchestration modes (Flexible vs Uniform) \n Queries the unauthenticated Azure Retail Prices API to provide c

Fast discovery and inventory of Azure resources across subscriptions using Resource Graph queries. \n \n Queries any Azure resource type (VMs, storage accounts, web apps, container apps, Key Vaults, etc.) across subscriptions and resource groups using KQL \n Identifies orphaned resources, missing tags, and resources in specific states without needing dedicated MCP tools \n Routes single-resource-type queries to specialized MCP tools (compute, storage, cosmos, acr, aks) when available; falls back

Analyze Azure resource groups and generate detailed Mermaid diagrams visualizing resource relationships and architecture. \n \n Discovers all resources within a resource group and maps their interdependencies, including network connections, data flows, identity bindings, and configuration references \n Generates layered Mermaid diagrams with logical grouping by function (Network, Compute, Data, Security, Monitoring) and includes SKU, tier, and configuration details in node labels \n Creates comp

Query and analyze massive datasets in Azure Data Explorer using KQL for logs, telemetry, and time series data. \n \n Execute KQL queries against billions of records with sub-second performance; discover clusters, databases, and table schemas \n Supports five core query patterns: basic retrieval, aggregation analysis, time series analytics, multi-table joins, and schema exploration \n Includes 4 MCP tools (cluster list, database list, query execution, table schema) with Azure CLI fallback for tim

Execute Azure deployments for applications with validated infrastructure and deployment plans. \n \n Requires .azure/plan.md with Validated status from azure-validate and prior completion of azure-prepare; will not proceed without both prerequisites \n Executes azd up , azd deploy , terraform apply , and az deployment commands with built-in error recovery and post-deployment verification \n Includes pre-deploy checklist validation, recipe-based deployment workflows, and post-deployment configura

Access Azure AI Search, Speech, OpenAI, and Document Intelligence services through unified MCP tools. \n \n AI Search supports full-text, vector, hybrid, and semantic search with built-in AI enrichment for entity extraction and OCR \n Speech service handles real-time and batch speech-to-text transcription, text-to-speech synthesis with neural voices, and speaker diarization \n MCP tools provide direct commands for listing search indexes, querying indexes, transcribing audio, and synthesizing spe

Passwordless PostgreSQL authentication on Azure with Microsoft Entra ID integration. \n \n Configures Entra ID authentication for Azure Database for PostgreSQL Flexible Server, eliminating password-based access in favor of token-based authentication \n Supports three access patterns: developer identities, managed identities for Azure-hosted applications, and group-based permissions via Azure AD groups \n Includes migration tooling to transition existing password-authenticated databases to Entra

Check and manage Azure quotas and usage across regions for deployment planning and capacity validation. \n \n Azure CLI ( az quota ) is the only reliable method for checking quotas; REST API and Portal are unreliable and show misleading \"No Limit\" values that do not indicate unlimited capacity \n Supports quota discovery, usage tracking, limit checking, and increase requests across compute, network, storage, and container services \n Quota resource names do not map 1:1 to ARM resource types; u

Query metrics, logs, and traces across Azure Monitor, Application Insights, and Log Analytics. \n \n Access Azure Monitor metrics, Application Insights APM data, and Log Analytics KQL queries through MCP tools or Azure CLI commands \n Supports distributed tracing, performance analysis, and infrastructure monitoring across applications and resources \n Includes interactive workbooks for building custom dashboards and reports on observability data \n Common use cases: error tracking, request perfo

Pre-deployment validation for Azure infrastructure, configuration, and permissions before deploying. \n \n Runs recipe-specific validation commands (azd provision, bicep build, terraform validate) and records proof in .azure/plan.md \n Requires .azure/plan.md from azure-prepare skill as input; blocks deployment if plan is missing or status is not Approved \n Validates azure.yaml, Bicep templates, Terraform configurations, Azure Functions, and app readiness across multiple deployment scenarios \n

This skill handles assessment and automated upgrades of existing Azure workloads from one Azure service, hosting plan, or SKU to another — all within Azure. This includes plan/tier upgrades (e.g. Consumption → Flex Consumption), cross-service migrations (e.g. App Service → Container Apps), and SKU changes. This is NOT for cross-cloud migration — use azure-cloud-migrate for that.

Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost analysis). WHEN: create AKS environment, provision AKS environment, enable AKS observability, design AKS networking, choose AKS SKU, secure AKS.

Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment'. PREFER

Microsoft Entra ID app registration, OAuth 2.0 configuration, and MSAL integration for secure application authentication. \n \n Covers app registration setup, authentication configuration, API permissions, and client credential management across web apps, SPAs, mobile, and daemon services \n Provides step-by-step workflows for first-time registration, console app authentication, and service-to-service flows using Client Credentials \n Includes Azure CLI commands, MSAL library examples for .NET,

Unified Azure cost management: query historical costs, forecast future spending, and optimize to reduce waste. WHEN: \"Azure costs\", \"Azure spending\", \"Azure bill\", \"cost breakdown\", \"cost by service\", \"cost by resource\", \"how much am I spending\", \"show my bill\", \"monthly cost summary\", \"cost trends\", \"top cost drivers\", \"actual cost\", \"amortized cost\", \"forecast spending\", \"projected costs\", \"estimate bill\", \"future costs\", \"budget forecast\", \"end of month co

Govern AI models, MCP tools, and agents through Azure API Management with semantic caching, token limits, and content safety. \n \n Supports five core policy categories: semantic caching (60-80% cost savings), token rate limiting, content safety filtering, jailbreak detection, and request rate limiting for MCP tool protection \n Enables backend configuration for Azure OpenAI, AI Foundry models, and custom APIs with load balancing and managed identity authentication \n Includes token metrics and