While visiting multiple organizations and talking to colleagues about integrating AI into their software development lifecycle, I noticed something: The approaches couldn’t have been more different. Some teams were embedding AI deeply into every step of development—coding, testing, documentation, even architectural decision-making. Others were deliberately cautious, limiting AI to narrow, controlled use cases. Opposites. And yet, both felt… reasonable. That’s when it clicked for me: We are all beginners. Not in the dismissive sense. Not in a “we don’t know anything” kind of way. But in the ways as described inside the Dreyfus Model of Skill Acquisition . The Dreyfus model, briefly The Dreyfus model describes how people acquire skills through five stages: Novice – Rely on rules and rigid guidelines Advanced Beginner – Start recognizing patterns, but still need support Competent – Can plan, prioritize, and make conscious de...
With the introduction of agent skills , we can teach our AI agent to handle our most repetitive and specialized workflows. After adding context through an agent.md file, integrating tool calls using MCP, creating our own Agents, this is a logical next step in defining your AI enabled software development lifecycle. Here's everything you need to know to get started. What are agent skills? Agent skills are folders of instructions, scripts, and resources that GitHub Copilot can load automatically when relevant to your prompt. Think of them as reusable "playbooks" you write once and invoke repeatedly — without having to re-explain the context every time. Unlike custom instructions , which set broad coding guidelines that apply across nearly every task, skills are meant for specialized, on-demand capabilities: things like running a specific test suite, converting file formats, generating components, or following a custom deployment checklist. How to create your firs...