GitHub Shares a Copilot Workflow That Beats Chasing AI Tools — SkimNews
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- Burke Holland of GitHub argues that the biggest AI productivity gains come from understanding the Copilot harness rather than installing new tools, MCPs, or prompt tricks.
- GitHub Copilot's CLI, app, VS Code, Visual Studio, and JetBrains experiences are increasingly centralized on the same harness, so the core workflow transfers once learned.
- Holland recommends beginners start with the GitHub Copilot CLI because it's a terminal interface with minimal UI, calling the interaction "more direct, immediate, and very satisfying."
- GitHub Copilot supports YOLO mode (/allow-all) to let agents execute commands without per-step approval, but Holland warns against running it on a local machine and points to Codespaces or dev containers as safer sandboxes.
- The workflow's prototype phase leans on visuals—including Mermaid diagrams rendered by the agent—to surface edge cases before code is written, and the plan phase uses /plan mode plus Matt Pocock's "grill-me" skill to force the model to ask exhaustive clarifying questions.
- Holland recommends a medium-sized model like Claude Sonnet or GPT 5.6 Terra at medium reasoning, and urges developers to stick with one model for the duration of a feature so prompt caching kicks in.
- GitHub Copilot's Autopilot mode automatically orchestrates subagents—using a small model for "Explore" tasks and a larger model for "General Purpose" work—without any custom configuration.
Why it matters: For developers drowning in weekly AI announcements, Holland's bet is that the productivity edge is already sitting in GitHub Copilot's existing features—YOLO mode, subagent orchestration, prompt caching—and the unlock is workflow discipline rather than tool adoption. That directly contradicts the "install the next MCP" mindset dominating AI discourse and reframes Copilot as a deep product to learn, not a thin wrapper to swap.
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