Introduction

Today, I want to discuss an interesting concept.

What does it mean?

I’ve been using OpenClaw recently and noticed that its architectural design is quite intriguing. At its core is a large model, with a scheduling layer in the middle and a Skills layer on top. This layered structure reminds me of something:

Its design approach is similar to that of an operating system.

You are probably familiar with the term operating system. iOS is an operating system for mobile devices, while Windows is for computers. An operating system manages hardware and packages the underlying components into a unified interface, allowing application developers to work without worrying about how the screen is driven or how the camera communicates.

OpenClaw does something similar. It wraps the underlying large model and provides a unified scheduling mechanism: how to understand user needs, how to decompose and allocate tasks, and how to enable different modules to work together.

What you need to do is simply tell it what you want to achieve—the underlying model, how to write the prompt most effectively, and how to maintain context; it handles all of that.

How Does an Operating System Expand?

This brings up an interesting question.

Once an operating system is released, how do we expand its functionalities?

When iOS first launched, its built-in applications were quite limited. The maps were not very useful, so developers created one. The music app was unsatisfactory, prompting developers to create another. If you wanted to chat, developers made something called Instagram, followed by WhatsApp…

Creating apps has always been the primary way traditional operating systems expand their functionalities.

Want to add a feature to the system? You create an app.

However, everyone knows the barriers to creating an app: high.

You need to write code, design, manage back-end systems, handle servers, and promote it. Without a technical background, even the best ideas are hard to realize.

The people who can participate are essentially programmers and product managers.

What About the AI Era?

In OpenClaw, the way to expand the system is entirely different.

Instead of creating an app, you create a Skill.

What is a Skill?

It’s about transforming something you excel at into a workflow that AI can execute. It’s not about letting AI run wild; it’s about organizing your experience, your judgment criteria, and your steps into a format that AI can follow.

Its expansion logic differs from traditional operating systems. Thus, I call this design approach an Agent OS.

Let me give you a few examples.

If you want AI to help you analyze stock performance—not just throwing a bunch of data at it for analysis—but providing it with your own logic: how to view the market, how to select stocks, how much to set for stop-loss, and what signals to act on. AI follows your framework, ensuring each analysis is methodical.

If you want AI to help you review contracts—not just uploading a PDF and asking if there are risks—but giving it a checklist of your own: which clauses are prone to issues, which areas 99% of people overlook. AI follows this checklist, ensuring no detail is missed in each contract.

If you want AI to assist in career decisions—not just asking it whether you should switch jobs and letting it come up with random suggestions—but providing it with a framework for evaluating opportunities: considering salary potential, team atmosphere, growth trajectory, and commuting costs, each with its own weight. AI follows your decision model, producing conclusions that are structured and not arbitrary.

You see, the core is not about letting AI run free; it’s about turning your expertise into a process that AI can execute.

The Shift in Barriers

At this point, one thing naturally emerges.

The barrier to creating products has shifted from technology to domain knowledge.

In the traditional OS era, if you wanted to turn your domain experience into a product, the barrier was technical. If you couldn’t code, even the best ideas couldn’t come to fruition.

Now, with Agent OS allowing for Skill creation, the system handles the technical aspects, and what you need to do is clarify your experience in a specific domain.

The barrier now becomes: how well do you understand this domain?

If you understand stocks, you can create a stock analysis Skill.

If you understand contracts, you can create a contract review Skill.

If you understand the workplace, you can create a career decision Skill.

Your value is no longer determined by whether you can code, but by how deeply you understand a particular field.

Who Should Create Skills?

At this point, the answer is quite clear.

It’s not programmers.

The advantages of programmers have significantly diminished under this logic—the technical aspects are handled by the Agent OS.

It’s the people who create Skills—those who have real experience in a specific vertical field.

The work you do every day, the pitfalls you’ve encountered, the methods you’ve summarized—these things used to be for your own use or turned into courses or books with limited monetization options.

Now, they can be transformed into Skills, placed within the Agent OS for your use and for others.

How to Get Started?

If you’re interested, here’s a simple step to start:

First, list the three things you excel at. They don’t have to be grand; just things you do daily, others often ask you about, or areas where you feel there are patterns.

Second, choose the one that resonates most with you and try to write down the steps you take to accomplish it. It doesn’t have to be perfect; just get your thoughts out.

Third, find an AI tool you commonly use and present this workflow to it. See if it can follow your steps. If it works, you have a prototype of that Skill; if not, you’ll know where the issues lie.

Start with a usable version, even if it’s rough. Use it and iterate.

Conclusion

Returning to my initial statement:

Previously, programmers created products, and the barrier was technical; now, with AI creating Skills, the barrier has shifted to domain knowledge.

Agent OS has dismantled the wall of technology.

Once the wall is down, the real barrier becomes: how deeply do you understand a domain, and can you translate that understanding into a process that AI can execute?

Now, the door is open.

What are you good at? In which direction do you want to build a barrier?

So, are you going to give it a try?