If agents keep getting better, do we still need skills?

I recently built three Skills in a row: job search, piano score simplification, and Reflection Companion.

Then, once I started using Dot seriously, a slightly awkward question came up:

If a Personal AI Agent can already understand what I need, figure out how to do it, call tools and remember long-term context, why do I still need Skills?

The piano score simplification example makes this especially obvious.

I’m not a musician. A lot of the specific methods in that Skill aren’t expertise I brought to it. The AI found them on its own, through attempt after attempt.

So next time I have a new score, why not just have a strong enough Agent do it from scratch?

I thought about it for a while, and I don’t think the answer needs to protect Skills’ reason for existing.

Some Skills really will become unnecessary.

01 If the Agent can already improvise well enough, you don’t need a Skill

Say you have a task where:

• the situation is a bit different every time;
• the Agent can research it and get it done reliably on its own;
• the result doesn’t need to be strictly consistent;
• and there are no fixed rules, scripts or acceptance criteria to reuse.

Then there’s really not much point in turning it into a Skill.

As models get stronger, a lot of what we used to write down to “teach the model how to do things” will slowly thin out:

• very long Prompts;
• step-by-step hand-holding;
• fixed routines telling the model “analyze first, then think, then output”;
• piles of instructions stacked up to keep the model from going off track.

The value of all this was always built on the model not being able to do it yet.

Once the underlying capability improves, it naturally gets absorbed.

02 So what’s left for Skills?

Later I asked myself a different question:

Are there problems I’ve already solved that I shouldn’t have to solve again next time?

That’s where the value of a Skill becomes clearer.

For example, the first time I did piano score simplification, I’d already spent time figuring out:

• how to tell the difficulty levels apart;
• what can’t just be cut;
• what the output needs to include;
• how to check the generated result;
• what to do when something goes wrong.

Whether I came up with these answers myself doesn’t really matter that much.

The first time can absolutely be a person and AI exploring together.

What’s actually worth saving is this:

the approach has been validated, and I want to keep reusing it next time.

So a Skill is more like a way of saving:

• workflow;
• constraints;
• eval;
• scripts;
• templates;
• failure handling.

It turns the result of one round of exploration into something you can repeat, test, version and distribute.

So from now on, when I decide whether to build a Skill, I probably won’t ask:

Can AI do this task?

I’ll ask:

Is there anything here worth reusing reliably?

If letting the Agent improvise each time is good enough, you don’t need a Skill.

If you need repeatability, consistency and testability, a Skill is still worth it.

03 But Skills may become more and more “invisible”

This is another thing I’ve felt very clearly while using a Personal Agent lately.

It used to be that to get something done, I had to know for myself:

• which App to open;
• whether to Search;
• which Skill to use;
• when to run an Automation;
• where to move the result next.

All of that is really the user doing the orchestration.

If a Personal Agent knows me well enough, it can decide on its own:

“Time to search for new roles.”

“This step should call the job search Skill.”

“The Recruiter replied—need to update the application state.”

“There’s an interview tomorrow, so pull together the Calendar and the earlier materials.”

The user might only need to say:

Keep following up on my job search.

What happens in the background is:

Search → Skill → Gmail → Calendar → Automation.

But the user doesn’t necessarily need to know.

So I’m increasingly convinced that one change Personal Agents bring is this:

a lot of things won’t disappear—they’ll just gradually disappear from the user’s view.

Skills may still be there;

Automation is still there;

Search is still there;

all kinds of SaaS are still there.

We just won’t necessarily need to find each of them, understand them and operate them ourselves.

04 Will AI make ordinary users more and more “dumbed down”?

To some extent, I think it will.

It used to be that learning a new tool meant understanding its structure and how to operate it.

Then natural language came along, and we no longer had to memorize so many menus.

With Personal Agents, even the step of “which tool should I use” might be handed off.

Users can just say something closer to what they actually need:

I want to find a job.

I’m moving next month.

I want to learn to play this piece.

Whatever model, Skill, search tool, database or automation is used behind the scenes may slowly become an implementation detail.

That’s good news for ordinary users, of course.

But “knowing how to express what you need” isn’t everything.

• What counts as a job that’s right for you?
• What kind of home is worth paying 20,000 more for?
• Is a piece of investment advice actually sound?

When tools get easier to use, the judgment to evaluate the results doesn’t automatically appear.

Maybe what we really need to learn will be less and less “how to operate tools,” and more and more “what I want, and how I judge whether the result is good.”

At least for building Skills right now, I’m going to keep one simple principle in mind:

Don’t turn something into a Skill just because it can be one.

What’s worth locking in are the problems that have already been solved and are worth reusing reliably in the future.

The rest can be left to increasingly smart Agents to improvise.