Building better platforms with Azure, GitHub, Terraform and AI
The Draw.io MCP Diagramming Agent Skill provides a reusable working method for creating clear, editable diagrams with Draw.io MCP. It supports cloud architectures, network topologies, CI/CD pipelines, Kubernetes platforms, event-driven systems, C4, UML and more, while applying consistent guidance for layout, containment, connector routing, labels and validation.
Agent skills, custom instructions, and MCP configuration are becoming part of the engineering trust boundary. This post walks through using NVIDIA SkillSpector in GitHub Actions to scan AI skill repositories, surface findings in SARIF or PR comments, and make risky agent behaviour visible during normal review.
AI-assisted engineering is moving beyond “can the agent do the task?” and into the same practical concerns platform teams already deal with: cost visibility, ownership, observability, governance, repeatable workflows, and sensible defaults.
This post looks at why AI engineering needs boring platform controls, from APIM policies and token visibility through to Agent Skills, MCP, Terraform modules, OpenTelemetry, and evaluation loops.
AI-assisted engineering is moving beyond proving that agents can complete tasks. The harder question is whether those workflows can be repeated, reviewed safely, kept within sensible cost, and improved over time.
This post looks at why platform teams need to treat AI usage as more than activity metrics, with a focus on token visibility, repeatable agent skills, controlled context, practical guardrails, and measurement that connects AI-assisted work to real engineering outcomes.
Building Terraform modules is rarely the hard part. The harder part is deciding whether a module should exist at all, where the boundary should sit, how much of the provider surface to expose, and which platform defaults should be opinionated rather than endlessly configurable. In this post, I walk through why I built a terraform-module-creator GitHub Copilot skill to help with that design work up front, using live Azure MCP and HashiCorp MCP guidance to shape better module boundaries, interfaces, documentation, validation, and long-term supportability.
If you are building GitHub Copilot agent skills, creating them is usually the easy part. Sharing them across repositories without drift is where things get messy. In this post, I look at using APM to package skills, agents, and MCP configuration in a way that is reusable, focused, and much easier to manage across teams.
Learn how an Excalidraw Agent Skill powered by Excalidraw MCP in GitHub Copilot can generate Cloud, Terraform, and workflow diagrams from natural language prompts.
How I built the azure-pricing skill for GitHub Copilot, using Azure MCP and the Azure Retail Prices API to bring live Azure pricing into architecture and engineering workflows.