Building an interactive UI for the grill-me skill is a total

The grill-me skill is basically a "roast my code" or "roast my logic" agent, but interacting with it through a raw CLI or a basic chat interface feels like you're missing half the experience. I've been playing around with a new interactive UI that actually makes the "grilling" process visual and dynamic, and it changes the vibe entirely. Instead of just getting a wall of text telling you why your architecture is flawed, the UI highlights the friction points in real-time.

For anyone looking for a practical tutorial on how to wrap an AI skill in a front-end that doesn't feel like a generic GPT clone, this is a great case study. The goal here wasn't just to put a skin on an API, but to create a feedback loop where the LLM agent can actually "point" to things.

If you want to set this up or integrate a similar AI workflow into your own project, here is the general deployment logic:

1. Backend Bridge: You need a middleware layer that parses the grill-me skill's output. The skill usually returns a mix of critique and suggestions; the backend needs to categorize these into "burns" (critiques) and "fixes" (suggestions).
2. State Management: Use a reactive framework (like React or Vue) to track the "heat level" of the roast. As the LLM finds more issues, the UI should reflect that intensity visually.
3. Prompt Engineering for UI: You have to tweak the system prompt so the agent returns structured data (like JSON) alongside its snarky comments. This allows the UI to map specific critiques to specific lines of code.

{
  "critique": "Your loop complexity is an absolute nightmare.",
  "location": "line 42",
  "severity": "scorching",
  "suggestion": "Use a hash map instead of nested for-loops."
}

The real-world utility here is that it turns a passive reading experience into an active debugging session. When the AI grills you, you aren't just reading a report; you're seeing your mistakes highlighted in a way that feels urgent. It’s a deep dive into how we can make LLM agents more visceral and less like a polite customer service bot.

Most "AI wrappers" are boring because they just mirror the chat window. By building a dedicated UI for a specific skill like grill-me, you can implement features like "heat maps" for your code or a "burn meter" that tracks how poorly your logic is performing. It makes the process of improvement much more engaging because the feedback is immediate and spatially mapped to the work.