Caveman Commit

Azure AI provides developers and cloud architects with tools to integrate Azure's AI services—including AI Search, Speech, OpenAI, and Document Intelligence—into their applications through MCP tools and CLI commands. The skill solves the problem of implementing complex AI capabilities like vector search, speech-to-text conversion, OCR, and semantic search by offering simplified access to these services. It serves users building AI-enhanced applications who need quick reference guides, SDK documentation, and command-line access to Azure's cognitive services.

The azure-deploy skill executes deployment operations for applications that have already been prepared with infrastructure files and a deployment plan, running commands like `azd up`, `azd deploy`, and `terraform apply` with built-in error recovery. It is designed for developers who need to push prepared applications to Azure production environments, but requires that the azure-prepare and azure-validate skills have been completed first to ensure infrastructure readiness. This skill solves the problem of safely deploying pre-validated Azure applications while preventing users from skipping critical validation steps that would cause deployment failures.

This is a comprehensive Azure cost management tool that actually queries your real spending data instead of making you guess. It pulls historical costs, forecasts future spending, and identifies optimization opportunities like orphaned resources and oversized VMs. The skill handles the tedious parts well, constructing proper Cost Management API calls with correct scopes and rate limiting, while generating actionable reports with potential savings calculations. What I like is that it always shows your actual bill alongside recommendations, so you're not optimizing in the dark. It's thorough enough for serious cost analysis but practical enough for quick spending checks.

The azure-storage skill enables developers to manage Azure Storage services including Blob Storage, File Shares, Queue Storage, Table Storage, and Data Lake through MCP tools or Azure CLI commands. It allows users to perform operations such as listing storage accounts and containers, uploading and downloading blobs, and configuring access tiers and lifecycle management for object storage and big data analytics workloads. The skill solves the problem of programmatically accessing and managing distributed storage resources across different Azure storage service types.

The azure-diagnostics skill enables developers and DevOps engineers to systematically debug and troubleshoot production issues across Azure services including Container Apps, Function Apps, and AKS clusters. It solves the problem of diagnosing root causes of failures by providing a structured diagnostic flow that combines Azure Monitor, AppLens, resource health checks, and service-specific troubleshooting guides. The skill addresses common issues such as image pull failures, pod crashes, invocation timeouts, cold starts, and Kubernetes networking problems.

The microsoft-foundry skill enables developers to deploy, evaluate, and manage AI agents on Microsoft Foundry end-to-end, covering containerization, Azure Container Registry (ACR) deployment, agent creation, invocation, batch evaluation, prompt optimization, and troubleshooting. It serves developers building agentic applications who need to handle the complete lifecycle from Docker build and deployment through monitoring and optimization. The skill solves the problem of managing complex agent workflows by providing specialized sub-skills for deployment, invocation, observation, tracing, and troubleshooting while preventing misuse for unrelated Azure services.