Prompt Engineering

By the time I was maintaining the fifteenth or so skill in dev-skills, I realized I had no reliable way to tell whether any of them actually worked. Every demo produced clean output. But the output had no control group, no causal grounding — I knew what happened with the skill mounted, not what would have happened without it. How I Was Judging at the Start # The original method was simple: write a skill, load it into Claude Code, run a task, check whether the output looked right. The first version of grill passed this way — I fed it a service design with a concurrency bug, the agent flagged a lock contention issue, I marked it valid.

If you’ve used Claude Code, you may have noticed the /skills command listing installed “skills”. Writing standards, architecture design methods, TDD workflows, code review strategies — it sounds like a plugin marketplace. Skills are not plugins. That’s the first and most important thing to understand about the Skill system. A plugin is code — it extends what the program can do. A Skill is a prompt — it extends what the model knows.

If you think a system prompt is just “giving the AI a persona”, Claude Code’s system prompt will upend that notion. It isn’t a few sentences. It’s a thousand-line template engine made of multiple layers, each responsible for something different: identity definition, behavioral constraints, tool documentation, project context, memory injection, skill loading. Some layers are static (identical every time); others are dynamic (injected in real time based on the project, the session, user preferences).

Conclusion first: this is not another “type a topic → force it into a template → output garbage” AI PPT tool. It runs a complete expert workflow — requirement research, material retrieval, outline planning, layout planning, visual design — where every step allows manual fine-tuning, or the whole thing can run automatically end to end. In terms of output, pages generated with Gemini 3 Flash already reach commercial delivery quality.

Anyone who has used AI knows the feeling: with the same request, a well-written prompt and a badly written one produce wildly different results. The problem is that most people simply aren’t good at writing prompts — not for lack of talent, but for lack of a systematic optimization process. I recently came across an open-source project, Prompt Optimizer — nearly 29k stars on GitHub — built to solve exactly this.

AI Tutorial: Prompt Engineering # Prompt engineering focuses on designing, optimizing, and strategizing prompts — helping users mobilize the capabilities of large language models more effectively, and pushing their adoption across real-world scenarios and research domains. 1. Basic Concepts # 1.1 What Is Prompt Engineering # A prompt is simply this: you use natural language to tell the model what to do, how to do it, what it may do, and what it must not do. That is all there is to it.