Furnace Kiln Oven Operator: Transform Human Expertise into AI Agent for Industrial Operations

Recently I stumbled upon a particularly interesting project on GitHub called "domain-experts" – its name is quite ambitious: "Turning all human experts into AI agents"

To be honest, at first I thought it was another one of those overhyped AI projects that promise a lot but deliver little. But when I clicked into it, I was pleasantly surprised – it actually has some substance. Inside, folders are organized by different professional roles, such as doctor, lawyer, accountant... and then my eyes immediately landed on this one: furnace-kiln-oven-operator. Kiln operator. That really hit home – it's incredibly specific!

My father used to work at a ceramics factory, spending his days dealing with kilns. If the temperature was off by even one degree, an entire batch of products could be scrapped. Back then, everything relied on the experienced old workers: judging the fire color by eye, feeling the temperature with their hands, even licking a finger to test humidity... Now that I think about it, if this knowledge could be turned into AI, how many detours would new operators avoid?

What exactly does this skill do?

Simply put, it's a skill package that digitizes human kiln operating experience into an AI persona. Once installed, you can let the AI play the role of a senior kiln operator and ask it various questions about furnaces, kilns, and ovens. For example:

• Temperature control: How to set the heating curve during ceramic firing? What are the considerations for different clay bodies?
• Safe operation: What to do if the furnace pressure is abnormal? How to safely reignite after extinguishing?
• Troubleshooting: Red flame color and incomplete combustion – what could be the causes?
• Maintenance: How often should refractory bricks be inspected? How to handle a clogged burner?

It doesn't just give answers – it simulates the thought process of an experienced operator, explaining why you should do something and what to watch out for. For instance, if you ask "What happens if the heating rate is too fast?" it might reply: "It could cause the green body to crack because internal moisture evaporates too quickly. It's recommended not to exceed 3 degrees per minute and to hold at critical temperature points for 30 minutes." This is really practical.

How to install? A step-by-step tutorial

This skill is based on the domain-experts project. The installation is very simple, provided you have Node.js installed on your computer (if not, go to the official website and install it first).

npx skills add https://github.com/wonsukchoi/domain-experts --skill furnace-kiln-oven-operator

Copy the command above and run it in your terminal. It will download the entire project from GitHub, then extract only the SKILL.md file related to this role.

After installation, you can view the skill content in the terminal:

cd skills/furnace-kiln-oven-operator
cat SKILL.md

You'll see that this SKILL.md is a structured role description file containing the role background, knowledge areas, behavior guidelines, example dialogues, and more. If you know Markdown, you can even modify it yourself and add specific parameters from your own factory.

I tried it out for a while – it's really realistic

I asked the AI: "I'm a new kiln operator. My master said we need to use a reducing atmosphere to fire blue bricks, but I don't quite understand. Can you explain?"

Its reply was something like this (I made up a version for demonstration, but the logic is similar):

"The reducing atmosphere means reducing the oxygen supply inside the kiln, causing incomplete combustion of the fuel and producing carbon monoxide. Carbon monoxide will extract oxygen from the brick body, reducing iron(III) oxide to iron(II) oxide, resulting in a blue-gray color. When operating, pay attention to: first, reduce the combustion air supply, maintain a certain negative pressure, control the temperature between 950-1050°C, and hold it for 8-10 hours. Also, keep a close eye on the flame color – if the flame turns red and produces black smoke, it means the reduction is excessive, and you need to add more air."

See? It explained the principle, the operating steps, the precautions, and even gave criteria for judgment. Isn't this much faster than flipping through a process card?

A few tips and considerations

• Don't take it as absolute truth: After all, AI is based on public data and experience models. For your specific furnace model and working conditions, you still need to combine it with reality.
• Customize the knowledge base: You can add your factory's operating procedures and equipment parameters to the SKILL.md file to make it more relevant to your actual situation.
• Multi-turn conversations are better: Don't just ask one question. Try to ask "why" or "what then" – you'll find that it can expand on many things, just like a real experienced master.
• If you get stuck: Ask it "Are there any common failure cases?" – it might give you some tables or lists.

Final words

I used to think AI was far away from frontline industrial workers, but seeing this project, I think it's really cool. The decades of experience of a ceramics factory master shouldn't only exist in notebooks or minds. Through this skill-based approach, it can be replicated, disseminated, and even iterated. Just as the project name says – turning all human experts into AI agents. Although it's still just the beginning, the direction is right.

If you're also in industry or interested in knowledge engineering, I suggest you download it and give it a try. It might bring some new inspiration to your training system.