Nufar Gaspar

Our research skill also does that.

So you can take a look at that.

And of course, the sky's the limit.

And we'll be curious to see if you have other advanced patterns that you've discovered that are working well with you.

I want to make sure that you don't just create skills, but that you test them and make sure that they're working well for you over time.

I think the easiest test for you is if you find yourself having to iterate after you get the output of the tool that used your skill, that means that your skill is not good enough.

Because ideally, a skill should create a ready-to-use output.

And if this is not the case, you have to go back and fix the skill.

And this becomes even more important when you are about to share it with 50 different people.

That's the case for you to treat it like any other AI product.

and basically run a proper evaluation.

And the rigor, of course, should match the stakes.

If it's something that also updates your CRM, then make sure that the skill is well tested.

If it's customer facing, make sure that it's well tested.

And there are some ideas here on how to test it.

But in general, every time that you have a new model or that you have a different tool that will be using the skills, you have to go back and reevaluate.

Okay, let's talk about the organizational perspective.

So up until now, skills were, at least it could be inferred that we're primarily talking about skills as a personal asset.

However, organizations that are very AI forward already realized that skills are the future of how to streamline work and how to get everybody to get more value from AI.

And as you can hear, this is where I get genuinely excited because it's basically the pipe dream of every knowledge manager that finally can become real.