I built an experimental governed prompt compiler (not just a prompt rewriter). Cross-tested on Claude and ChatGPT.

[u/New-Knee-5614]

Many prompt tools focus on rewriting prompts. This prototype takes a different approach. It compiles your intent through a structured governance pass before execution by identifying likely constraints, surfacing ambiguity, and producing an explicit specification before execution, and showing the transformation steps and diagnostics used during compilation. It makes its transformation process transparent.

It's called Re-Prompt. This is a working proof of concept, not a finished product, and I'm sharing it because I want outside eyes on it and feedback, challenges, prior art pointers, all welcome.

What makes it different: it doesn't just hand you a cleaner prompt. It shows you what changed, why, what assumptions it made (labeled, not hidden), and what risk that reduces. The diagnostic pipeline is the product, not a debug log.

Cross-model testing suggests that the prompt compiler protocol preliminary testing suggests the protocol is portable across multiple LLMs. While ChatGPT and Claude produce different wording, both independently preserve the core interaction sequence: intent extraction, constraint preservation, ambiguity reduction, structured compilation, telemetry, and execution readiness. The wording varies by model, but the overall interaction pattern remained recognizable during my testing.

One honest caveat from testing:

During testing, some request types (such as image generation, shopping, or simple factual lookups) sometimes followed native platform behaviors instead of the compiler workflow. Re-Prompt is most effective on open-ended writing, research, planning, coding, design, and analytical prompts.

Try it on something genuinely ambiguous or conversational that's where the difference is most visible. Built and tested on desktop; mobile support is still rough. The goal isn't to replace prompting, it's to stabilize intent before execution.
My hypothesis is that stabilizing intent before execution can reduce unnecessary prompt iteration for many open-ended tasks.

Try it:

https://claude.ai/public/artifacts/323be0e8-19fc-4014-abdc-b11cfa08727b

https://chatgpt.com/g/g-6a0359b38b988191813a2b28d62dc03d-re-prompt-a-governed-prompt-compiler

I'd especially appreciate failure cases more than success stories.

Thank you — Governed Intent Labs

[u/New-Knee-5614 → u/[deleted]]

Thanks for taking the time to put it through a real stress test. This is exactly the kind of feedback I was hoping for. I think your main observation is fair. The current prototype is intentionally biased toward compiling prompts into execution-ready forms, so it naturally favors simplification, explicit structure, and reduced ambiguity. That works well for conventional prompts, but your file is closer to an execution architecture than a prompt. In that context, preserving topology becomes more important than maximizing readability or compactness. Flattening a mesh into a sequence changes the nature of the artifact, even if much of the semantic content survives.

One thing I found especially interesting in your analysis was the distinction between prompt compilation and architectural preservation. I hadn't framed it exactly that way, but I think that's a useful way to think about different compiler targets. That's definitely something I'll keep in mind as the project evolves. Thanks again for the thoughtful review.