You've probably experienced this. You're 30 minutes into a productive Copilot agent session, things are flowing well -- and then something shifts. The agent asks you to clarify something it already knows. It repeats a question from earlier. The quality of the output quietly drops. You didn't change anything. What happened? Context ran out. Every Copilot session has a fixed token budget. Once that budget fills up, the model starts compressing or dropping older context. The result isn't an error message. It's subtle degradation -- and most developers don't even realize it's happening until they're already fighting it. I built a skill to address this directly. Here's what I learned. The problem isn't Copilot. It's how we use it. After tracking a few weeks of agent sessions, I found five recurring patterns that burned through context faster than the actual work: 1. Re-reads When the same instruction exists in multiple files, the agent reloads it every time it's referenced.…