To get an AI to do a task well, you need to give it the right context and the right task description. I've had a lot of struggles working in a large codebase getting AI to follow the existing patterns and architecture. So I started building a VS Code / Cursor extension that lets you quickly snap together sub prompts/instructions relevant to a specific task. After seeing the value of this, I realized you could make those sub prompts more customizable by using a liquid syntax engine.That worked fine for a few days, until the original context blocks got stale as the project progressed. I wrote an MCP server that can update those blocks, creating a feedback loop. A few days later I added an amend option too, so the AI can write a log and decision record as it goes.At this point I realized there were a lot of opportunities here: my AI isn't spending the first 5 minutes doing research, I've got better control over my context window, there's less irrelevant data leaking in from my agents.md f...