What an AI “Skill” Really Is: Putting a Harness on AI

Some personal understanding, shared with friends who are also learning AI. Discussion and pushback welcome.

Most people learning AI have probably heard the word skill. But ask them point-blank — “what exactly is a skill, and what does it actually do for you?” — and not many can explain it clearly. Today I’ll put it in plain language. And once I have, I want to add one thing of my own: in the AI era, what really separates people may not be whether you can use AI, but whether you can “freeze” a process into something repeatable.

1. Start with a feeling we all share: AI “drifts”

A lot of us have chatted with AI, asked it a pile of questions, had it do things for us. And you’ve probably noticed one thing: it’s a little unpredictable.

Ask it the same question today and tomorrow, and the answers may differ; have it do the same task twice, and the results often don’t line up. It’s not that it’s in a bad mood today — this is just how large models work. They predict “the next most likely word,” which makes them inherently random.

That uncertainty doesn’t matter when you’re chatting — it can even be fun. But the moment you want AI to do something real for you reliably and repeatedly, it becomes a problem: you can’t guarantee that today’s good result will be reproducible tomorrow.

2. Putting a harness on AI: Harness Engineering

So what do you do? The fix is actually quite plain: don’t let it improvise — give it fixed steps. Step one does this, step two does that, step three does the next thing. Pave the road for it.

This brings up another term in AI: Harness Engineering. A harness is the bridle and tack you put on a horse. The metaphor is vivid — AI’s ability is like a very powerful horse: lots of strength, but without a harness it runs wild; put a harness on it, and it goes where you want it to go.

So what harness engineering does is wrap a layer of constraints around AI’s powerful-but-unruly ability, reining in the uncertainty and making it move the way we want.

3. What a skill is: packaging “steps + a definition of done”

With that, a skill is easy to understand.

One-line definition: a skill is a fixed set of steps, plus an explicit definition of done, frozen into a reusable package of prompts.

It’s different from the “ad-hoc prompts” we casually type at AI. An ad-hoc prompt is one-off, made up as you go. A skill is designed in advance — it has ordered steps, it has acceptance criteria for “what counts as done,” and it’s saved so you can call it again next time.

Put simply, a skill is harness engineering made concrete — the bridle itself. It doesn’t make AI smarter; it makes AI more obedient and more stable: the same task, done the same way, to the same standard, every time.

4. An example: turning “an accountant doing your taxes” into your private skill

Take something many of us can’t avoid — filing taxes. You do it every year; some people handle it themselves, some hire an accountant.

The work an accountant does on your taxes can be turned into a skill. And the key is this: this skill is customized to your personal situation, not a one-size-fits-all template.

Why does customization matter? Because tax complexity varies enormously from person to person:

• Someone with a simple income — just a salary, no other sources — has a short filing process, fixed information to fill in, only a few steps;
• Someone with several properties, stocks, and investment income is operating at a completely different order of magnitude.

These two people’s tax skills should be two entirely different things.

So what’s it like to use a tax skill that’s “built for you”? Each tax season, you either prepare your materials in advance, or you let it walk you through its steps and ask you for what it needs. And it remembers your situation — as long as nothing major has changed since last year, it won’t ask again about the things it already knows.

That’s exactly the best part of a custom skill. It’s not like getting a new accountant every year: a new accountant doesn’t know you, so they have to ask everything from scratch — “Do you have an investment property? Any stock gains?” A skill built for you has already internalized all of that; it only needs to ask about the few things that actually change: this year’s salary, household expenses, what’s deductible. The process gets short, fast.

And so filing taxes becomes standardized into an assembly line: one, two, three, four, five — run it, done. That’s a textbook AI skill.

5. One more thing: skills are leverage for ordinary people

That’s enough explanation, but what I really want to talk about is the layer beneath it.

Look back at that tax example. It’s not really about “how to use AI” — it’s about something bigger: a process that used to belong to a professional has been “packaged” by an ordinary person into a skill. You don’t have to become an accountant, but you can own “an accountant’s tax process” — one that’s privately customized for you and remembers your situation.

So I’m increasingly convinced that in the AI era, the real leverage isn’t “whether you can use AI” — that barrier is vanishing fast; everyone can chat a couple of lines. What really separates people is whether you can take something valuable and turn it from “improvising on inspiration each time” into “a fixed, reusable process.” Plenty of people can use AI; far fewer can settle a process into a skill.

That said, let me add a restrained note about the boundaries, so I don’t overstate it: freezing has a cost. A skill trades a bit of flexibility for certainty and reproducibility — which makes it suited to things that are recurring and clearly defined (taxes, weekly reports, organizing materials), and not to one-off tasks that need divergence and exploration. For the latter, AI’s “drift” is actually the more valuable trait. In other words, not everything should become a skill: freeze what should be frozen, let roam what should roam.

But for anything you’ll do repeatedly and want to get right every time, making it into a skill is almost always worth it.

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This is just my own understanding, and I may not be right about all of it. I’d love to hear your take — have you ever turned something you do repeatedly into a skill of your own?