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 file when I switch repos, I can write prompt groups for separate workflows, and so on. The last blocker for me was that I actually use Claude Code and not VS Code much anymore. So I figured out how to hook into the external editor command to open a TUI directly from CC and write a prompt from there.

Now I'm happy to finally share it after spending the last 5 months perfecting it. Would love for you to try it and let me know what you think, even if its not something for you - why?

More details see GH: https://github.com/simondevries/prompt-foundry

install vscode: https://marketplace.visualstudio.com/items?itemName=sdevries...

Install OpenVsx: https://open-vsx.org/extension/sdevries/prompt-foundry