Cloud, CI/CD, monitoring, and deployment (6.8K skills)
Review Azure Advisor recommendations and service retirement information, including totals, breakdowns, and affected resources. Update recommendation states with confirmation, and route requests to implement fixes to the separate azure-advisor-actionability skill.
Prepare remediation steps for Azure Advisor recommendations using the affected resource’s details. Guidance comes from the Advisor remediation tool or public Microsoft Learn documentation, with warnings, verification steps, and clear source attribution.
Find the Azure portal screen for a specific operation and generate a direct link. Provide a supported resource’s URL or resource ID plus a plain-language query to locate the relevant menu.
Investigate live Azure Kubernetes Service incidents through read-only checks of logs, events, and resource state. Trace workload, node, networking, or upgrade failures and produce an incident report with supporting evidence, confidence, and proposed remediation.
Collect bounded packet captures from AKS nodes or pods alongside Azure network settings. Use the captured traffic as evidence when investigating where packets are lost, after initial checks that do not change the environment.
Find documented fixes for known AKS errors by matching the exact error message and required conditions. Get an explanation of the cause, a proposed fix, a Microsoft Learn reference, and read-only verification steps.
Investigate GPU and KAITO failures in AKS using read-only evidence. Helps distinguish scheduling problems, memory shortages, workspace readiness issues, and scaling failures, then organizes findings with confidence levels and missing evidence.
Deploy Azure OpenAI models by checking available capacity in the current region first. If capacity is unavailable, reviews other regions and presents alternatives, then deploys with GlobalStandard and monitors deployment progress.
Deploy Azure OpenAI models through a guided workflow. Choose the model version, deployment type, capacity, content filter, and supported advanced options, then review the configuration before deployment and monitor its status.
Create separate Microsoft Entra identities for AI agent instances and configure their permissions. The skill guides Microsoft Graph provisioning, token exchange, actions on behalf of users, cross-tenant authentication, and shared authentication containers for multiple programming languages.
Assess existing AKS clusters or local Kubernetes configuration files for AKS Automatic compatibility. Identify migration blockers, distinguish required changes from automatic adjustments, and present proposed file changes and migration steps.
Find Azure OpenAI deployment locations that meet a required token rate. Compare accessible regions and projects, check platform capacity and subscription quotas, and receive a ranked table of candidates before moving to a separate deployment workflow.
Deploy an existing web app or API to Azure Kubernetes Service. The skill detects the framework, creates container and deployment files, checks AKS safety requirements, and verifies the deployment.
Assess and carry out upgrades within Azure, including Functions hosting plans and Redis migrations. Guides readiness checks, settings transfer, deployment, and validation, with a separate workflow for updating legacy Azure Java SDK code.
Assess whether an application is ready for Azure before infrastructure planning. Inspect source files and configuration without running builds or tests, then report component readiness, dependency compatibility, deployment blockers, and preparation needed before deployment.
Check whether an application is ready for Azure deployment by validating configuration, infrastructure definitions, role assignments, and managed identity permissions. Follow a staged workflow that records progress and identifies issues to resolve before deployment.
Configure Azure API Management as a shared gateway for AI models, tools, and agents. Apply usage limits, caching, content checks, and load balancing, then test requests and track token consumption.
Route Azure virtual machine requests to workflows for choosing sizes, comparing prices, creating machines or scale sets, reserving capacity, and managing machine enrollment and monitoring. Helps identify the appropriate process for each infrastructure task.
Assess deployed Azure Functions and Azure App Service applications for resilience to failures. Review zone redundancy, storage replication, health checks, and regional failover, then apply approved improvements through Azure CLI or infrastructure configuration files.
Turn workload requirements into an enterprise Azure infrastructure plan and Bicep or Terraform configuration files. Covers networks, access controls, and disaster recovery, with validation, security checks, and approval gates before deployment.
Check Azure quotas, the limits on resource usage, alongside current usage and remaining allowance. Compare regions before deployment, investigate quota errors, and submit requests to increase adjustable limits using Azure CLI.
Find and list Azure resources across subscriptions and resource groups. Use it to build an inventory, count resources by type, check missing tags, or discover unattached disks through dedicated tools and Azure Resource Graph queries.
Deploy Flask, Django, FastAPI, or other Python code to Azure App Service on Linux. The skill creates missing hosting resources, configures startup and deployment builds, and selects Azure CLI or azd based on the project.
Choose and use Azure Storage services for files, shared folders, queues, and structured data. Covers listing and transferring blobs, comparing storage tiers by access frequency, redundancy options, and lifecycle management, with MCP tools and CLI examples.