In this episode, Amir takes us through

how to use Claude skills to build

digital employees. We go through AB

testing idea agent, marketing insight

agent, and then we build one live

together. You're going to learn about

what cloud skills is, why it's the

biggest thing that happened since sub

agents, and how to actually build them

yourself.

Amir, what are we learning today?

>> Today we're going to talk about claude

skills. I'm going to tell you what they

actually are, how they're different from

projects and sub agents and claude, and

why this matters, and how you can

actually apply for work.

>> Okay. And by the end of this episode,

are we are we going to be able to apply

Claude skills?

>> 100%. I'm going to show you first I want

to talk about what it actually is and

why it matters. But I'll show you how to

use existing skills in claude that they

just came out with and how to create

your own and how to apply for your work.

So whether you're in marketing, in data

analysis or any sort of document

creation, you can actually use skills to

do that.

>> Cool. Let's do it.

>> Cool. So first thing is I want to talk

about cloud. So not a lot of people are

familiar with cloud projects and I want

to talk about what that actually is and

why it matters and how it's kind of

related to skills. So within Cloud AI

specifically, you can actually create

projects and they're essentially

workspaces with a set of custom

instructions. So this is a system prompt

and it has relevant context, memories,

and tools. So say for example you're

part of a a broader marketing team and

you want to create a project that will

uh have a set of instructions to analyze

marketing data for example or generate a

newsletter and you want it to connect to

specific tools um have relevant context

and files. So this could be a glossery

of terms you use within your

organization, your brand guidelines

depending on the task it is that you

wanted to do and then also have memories

generated from the chats that you have

within that project. So it's really

great for collaboration with other team

members. Now you can also use it

yourself as well, but really uh the

ability for you to create repeatable

tasks and do you know certain set of

instructions with external tools and

data. So if I'm in a marketing team,

this is something that I want to look at

and essentially within cloud share with

my team members and create projects

around it. All right. So the only thing

with projects I would say that it's

important to one work with your team

members to actually refine the system

instructions and then always have

relevant context files. As your business

changes, the data changes, you need to

update it and you have to go back and

constantly update these context files.

Um and I'll talk about why context is

important in this specific session and

kind of how it ranks up against skills.

Now the next part of it is sub agents.

With sub aents, this is more relevant in

cloud code specifically. And I actually

use uh sub aents in cloud code to spin

up multiple agents. And multiple agents

are really great at breaking down

complex multiworkflow tasks into

individual tasks with specialized

agents. So what does that mean? Say for

example, you're building a very complex

feature and you want to delegate the

front end to one agent and the back end

to another. So within the chat, you can

actually spin up these agents to say,

"Hey Claude, create an agent that will

work on the front end using this set of

rules and then create another agent to

spin up to do the back end for it." And

what's interesting is the context is

isolated to that conversation window.

Um, and so whatever context is provided

or gathered in that conversation is

actually um used as an input, but those

agents have a set of system instructions

as well. Now,

where things get interesting is skills.

And I want to talk about kind of why

this actually matters. Um, skills are

automated workflows and tasks that you

can apply globally at a project or

individual level. So whether you're an

existing project, you have a set of

system instructions, you can use skills

which is an add-on or augmented skill

set um within that project or individual

chat and it can do a set of set of

tasks, create documents, create PDFs,

analyze documents, um it can actually

help build MCPS for you. You can use

skills to create other skills or create

you know visual art as well. Now when do

you actually use this? It's for very

specialized tasks based on the

constraints and guidelines and steps

built by you the expert. I think uh it

was Kaparthi um a couple days ago he had

an excellent analogy where it's like AI

is essentially your coworker or someone

that like reports to you. You want to

train it. You want to build the

guidelines. You know this is not

verbatim but like basically what he was

trying to say was yeah it's someone that

you work with and you can kind of build

the limit the constraints around it and

guidelines on how you want it to respond

to you. And this is kind of similar in

some nature. uh you can create for

example let's say you are a paid media

expert and you run campaigns for your

clients and you want a very detailed

analysis on your visit to booked

appointments and what the conversion

rates look like and how that attributes

to the different channels you have and

what's performing better than the other

in terms of campaigns. You can create a

scale that can follow a set of custom

instructions but also scripts that you

can build out yourself to analyze that

data. And I want to circle back later on

why that actually is important. Um, but

what's also interesting is that it

actually only loads context when it's

relevant to the task. So when a project

often times you have the LLM that's

determining which context to retrieve

and add into the conversation window and

reference it. Um but in this instance

it's only con based on the judgment of

the task whether or not it should pull

relevant context and it's just relevant

to exactly what you want to get done. So

I would say the key takeaway here is

that it's repeatable instructions. It's

laser focused on a set of tasks pulls in

context as is needed and it has the

ability to run scripts or run code to

perform specific functions. Why this

matters because um there's a paper great

paper out there called talking about

context rot and how essentially um talks

about how to do effective prompt

engineering and how the right amount of

system prompts from you know very

detailed to vague and the right amount

of context has a huge impact on

performance and as you add more context

you essentially could be you know I

don't want to quote on this but

potentially be like degrading

performance from the LLMs and likely to

lead to more hallucination.

Sam Alman, the co-founder of OpenAI,

just said that it is the era of the idea

guy, and he is not wrong. I think that

right now is an incredible time to be

building a startup. And if you listen to

this podcast, chances are you think so,

too. Now, I think that you can look at

trends uh to basically figure out uh

what are the startup ideas you should be

building. So, that's exactly why I built

ideaser.com. Every single day you're

going to get a free startup idea in your

inbox and it's all backed by high

quality data trends. How we do it?

People always ask. We use AI agents to

go and search what are people looking

for and what are they screaming for in

terms of products that you should be

building and then we hand it on a, you

know, silver platter for you to go check

out. Um, we do have a few paid plans

that, you know, take it to the next

level. uh give you more ideas, give you

more AI agents and more almost like a

chat GBT for ideas with it, but you can

start for free. Ideabra.com. And if

you're listening to this, I highly

recommend it.

>> I mean, makes sense, right? Exactly. The

more context you have, the less likely

you are to hallucinate.

>> Well, the more Well, well, yes and no.

The more context you have, you're less

likely to hallucinate. with the right

amount of context. So, it's like kind of

like like a like a coworker like do you

want to give them all the information or

just the right amount so that it doesn't

bombard them to get the right task done.

>> Exactly.

>> So, that's that's what I would really

call um break it down into. So, I'm

going to go through some examples, but I

want to talk about the importance of

scale and why it's actually solving a

real problem that I have faced myself.

So with custom skills, how it works is

that you essentially uh create this

markdown file that explains exactly what

the skill is and what it does. And you

can actually create reference files that

it can reference back into for

additional context. So say for example,

you create a skill that uh applies uh

XYZ's company brand guidelines to

presentation and documents. And this

overview um essentially you know this

the skill overview has a set of tasks

and instructions it follows but you can

also have an additional document as a

reference that is an example existing

brand guideline document that it can

reference and it's not it's only pulling

it when it needs to.

You can take it another layer and you

can essentially create custom scripts as

part of that scale. Now um there's a

great documentation by Anthropic on this

and they talk about kind of how to write

good scales and descriptions. But what's

interesting is that when you are using a

cloud project and you have MCPs or tools

connected connectors connected the LLM

is determining which tools to call based

on your instructions and how to perform

that task. So say for example you have a

raw um like output of your meta campaign

ad data or your Google ads data and you

have a project in cloud that says like

it's a market analyzer. I want you to

your instructions are to analyze this

data and give me insights. The LLM is

determining

how to like the model is determining how

to actually look at the data and perform

insights and it's it's nondeterministic

in a way right like it's you know it can

look at it differently every single

time. um and you're not giving the right

guidelines on how to actually take the

data and analyze it. And I've seen this

firsthand actually working with a lot of

clients where like you know um a

director of revops is looking at churn

data new subscription data and um

they'll put the file into the cloud

project and it's not giving the right

output of insights they're looking for.

How this gets interesting is you can

actually create scripts that are very

specific. So say for example um if you

wanted to um have a very set of strict

guidelines on how it should actually run

and analyze the data then you can create

that within the scale itself to say I

want you to look at column X Y and Z

multiply by this divided by that to the

power of this to give me this insight

that way it's actual functional code

that's running this and it's not

deterministic nondeterministic by the

model itself. Y

>> so um so yeah that that's kind of the

beauty of skills itself where uh you're

able to really um bound or create the

boundaries of what I should actually

work towards um for um yeah for for like

building out these skills.

So yeah you can essentially have

metadata with it resources and code you

can load it as needed and it kind of

breaks down exactly how you should write

these skills.

>> Now let's jump into some examples.

>> Let's do it. This this the fun part. How

do you actually apply this? So, the

first one we're going to go through is

an artifact builder.

>> So, you can actually go to Claude and

it's preloaded with some existing

um skills. So, we're going to go to

capabilities and essentially you'll see

there's some existing skills that are

preloaded. So, I have created these two

ones right here. We'll go through them,

but I want to show you the ones that are

already already in there. So, you can

have an artifact builder, an MCB

builder, and a skill creator. So it's

very meta. You can create skills with

skills. So we'll go through an artifacts

builder one and I'll show you an example

of what that looks like. So say for

example, you want to create a tool that

is um relevant to marketers. Marketers,

you know, when they run campaigns, they

always have to have UTM links to do

proper attribution back to their data to

see okay, which campaign was driving the

most and when they're seeing the

analytics. So here um you know I have

added the artifacts builder skill

please create a UTM link generator for

my marketing team. So

what's happening here is that Claude is

now going to reference that skill

specifically that we have defined. And

I'll show you what that skill looks

like.

>> And essentially, it's reading the

documentation to understand how to build

components. Artifacts are essentially

these like live apps within cloud itself

that you can create very functional web

apps. And you can also share with your

team as well. So what it's doing is it's

actually referencing that skill here and

now creating an artifact/web

app of a UTM link generator that

marketing teams can use. And you can

actually just share this with the rest

of your team as well or your entire team

can use this as well. I mean, it's

literally a web app.

>> It's literally a web app. But what's

interesting is

>> we're now creating a set of specific

instructions and skills. We're adding a

skill to this LLM now that knows has to

follow this versus before you're saying,

"Hey, code this web app." And it's kind

of you're not really defining the the

guard rails or the the parameters of

what it should do.

>> And and what happens is people get

frustrated that they're not getting the

right result.

>> Exactly.

>> And then they're like, "Oh, you know, AI

doesn't work for me."

>> Exactly. Exactly. Exactly. So this is

where it gets interesting, right? Like

you as an individual, you have the

opportunity to um work with Claude and

skills to build exactly the skill you're

looking for to do. That's a repeatable

task. So if you as a a marketer are

doing weekly tasks of reporting,

create a skill that can actually help

you with that. Just explain to it in

terms of what you're looking for, what

you need. Be very detailed. if you were

to assume that you're hiring someone

else to do it for you.

>> Yeah. I mean, it really is I mean, it

really is thinking about AI as a

teammate.

>> Exactly.

>> Especially a junior teammate.

>> Exactly.

>> That you have to really give it guard

rails and really give it context cuz

that's how you would, you know, if you

hired someone junior, you would be like,

"Okay, these are the tools that you're

going to use." because they don't know

the tools they're going to use because

they're new. This is the context that

you need to know about our business and

how we operate. And then you kind of

drip feed them. You don't want to

overload them, right? Cuz then they're

going to they're not going to remember

everything or they might, you know, it

might Yeah. just might be overwhelming.

So you drip feed them the context over

time.

>> Exactly. But what's also interesting is

if you start now as these models get

better and as the toolkit expands you

now have this like history of like

training and reference and and metadata

and memories that you've created over

time now then so like someone like me

who uses cloud a lot I now have a lot of

pre-context of like memories and

experience building these projects now I

know exactly how to use skills and apply

it here. So I think that's where it gets

really interesting. Um, I generally

think scales is probably the a huge

problem solver for a lot of problems

I've seen firsthand working with people.

Like I've worked with a lot of teams

right now that have actually like a lot

of go to market teams that have used

cloud as part of the workflow and the

number one um feedback I get is the

output was not on what I expected or

it's incorrect. There's two reasons for

that. One is the promp prompting is not

good, right? the prompter

>> the prompter it's the problem is them

but the latter of it is it's also the

you can prompt you know I worked on with

them on right setting the right guard

rails the prompts the access to tools

the right retrieval of context and it

still doesn't get it just right and I

think this is where skills come in and

solves that problem where it's just that

task so you now have an artifact that's

fully functional and working you can

actually share with your team members if

you wanted to and you can essentially

like provide a URL like humble.com or

ideabrowser.com and it will append

um um the rights like Google and you

know CBC you know

was it Black Friday Cyber Monday? Yeah,

something like that. And it'll actually

create those um I will append it for

you. I don't know why there's a clear

button. It should be a submit button.

You can also sell this to other people

as a as a product, right?

>> Yeah. So I think uh Claude in

collaboration with someone else created

this like repository of skills. I I

don't I I I don't want to butcher the

name so I'm not going to say it but

basically there is a directory of some

sort with skills and plugins because

they recently came up with plugins as

well which is like a collection of

context and pl tools and skills and

prompts allin one that you can install

for your cloud workflow. So there is a

huge opportunity for people to sell

skills. Absolutely.

>> Okay. Sorry. What's the difference

between plugins and skills? So yeah,

yeah, plugins just came out last week,

which is like a plugin. You you plug it

in and it has ancillary features,

>> MCP access, context, system

instructions, and I think skills now.

I'm not don't quote me on this. Cloud's

been shipping. Cloud's been shipping.

I'm having trouble keeping up.

>> You know, when I was I was building this

out, when I was writing this out, I was

like, I'm trying to understand what

skills is. And as I was actually

building with it, I was like, it's clear

to me. Cuz initially, my gut reaction

was this is over complicating it. How is

this different from projects,

>> right?

>> And now I understand why.

>> Okay.

>> Yeah. So, uh yeah. So, we we

essentially, you know, https idea

browser.com.

We can go Google CPC

Black Friday Cyber Monday and then

generate the URL and we have a URL.

Boom.

>> So, that's one use case. Let's make it

more interesting. Um I am interested in

finding AB testing ideas for my website.

>> Yeah. So I have a skill that essentially

looks at AB test generator and what it

does is that you provide a URL and it

will come up with headlines or

experiments for you to run for your

website to increase conversions and it

actually uh the skill I created the

skill using the skill creator. I said

I'm going to give you a URL and you're

going to run a framework on actually how

to run good AB tests for me. So we're

going to test this and see what it looks

like and then I'll show you an example

of how to create your own skill as well.

So, hey Claude, I have just added I have

added the AB test generator skill. Can

you run an can you provide me with AB

experiment ideas for humble.com?

And what this will do is here because I

have access to uh an FCB called

firecrawl, it would actually use

firecrawl to scrape the URL, the page

and the contents and then come back with

a very clear framework on experiments to

run. So while that's running, maybe I'll

just show you an example of what that

actually looks like. And essentially, it

looks something like this. So it gives

you an experiment pipeline impact,

confidence, ease, um, IC score. And, um,

you know, it was actually a really good

one. I actually did it right before this

call, before the session today was it

asked me to, it told me to actually test

um, shifting the case study that I have

above

one section above. So it's like hero

section and then case study go to

immediately social proof and I was like

damn that's a good idea like why am I

showing the features when I should show

do the social proof. So I'm running AB

test right now to see which one is

likely to drink drive more conversions

and signups. So um that's interesting

like it really breaks down exactly the

control the variant the headlines you

should be testing. So, experiment number

one, experiment number two, and if I

really wanted to, you know, just take

this, put it into my app, and then run

run through an experiment.

>> You know, would be really cool if you

can automate this so that you, you know,

every month send me a report.

>> Yeah.

>> What to change?

>> Exactly. Exactly. So, um, if you really

wanted to, yeah, you can. You can

probably write a skill, uh, that I

wonder if you can. I wonder I wonder if

you can already do that today where you

write a skill that writes a script that

automatically sends you rapport every

single week or every month. Yeah.

>> Why not?

>> Yeah. Yeah.

>> Yeah.

>> So, yeah, you know, I am going to plug

in my app and say we do that

automatically in our app. So every week

we we have like four sets of sub agents

that go through your website and give

you insights from like a copy conversion

marketing like um a designer as well. So

every week and then we give you like an

optimization score. So similar um kind

of similar approach here but I think

doing that within cloud is actually

really interesting as well. Now what I

really really really want to show you is

a problem I've been trying to solve for

the past couple months with these

companies I've been working with which

is take data and give me the insights

that I actually want to look at. It's

such a repeatable task and it's so

important that

I think I can't confirm yet skills has

probably solved that problem for me now

in a way. So um I uploaded a file called

traffic analytics. It it's just

basically like a a CSV of um just a

bunch of campaigns and you know revenue

data and whatever. And I was like, I

need some insights on this. And that's

what really matters to a lot of people

in terms of just did cost go down did

you know CBC go up down? What does the

trial conversion look like? XYZ. So I um

I provided a file and it referenced the

scale and has a set of scripts within

that scale to then do a comprehensive

analysis of the data the traffic data.

So overall performance your total spend

was 400k your revenue was 854K.

uh net profit, conversions, which

channel did better than the other. Um so

you have a clear idea and I'll be honest

like I you know you know I am going to

be honest but um I would say that I I I

would I would say that if I had done

this through a project and I just

uploaded a file with set of instructions

without running scripts, it would have

probably hallucinated some of the data.

That's what I was going to say because I

>> when I look at this, this feels

this if this wasn't in in Claude and I

had a product manager send me this, I

would be like, "Yeah, you know, this

feels like that level

>> of fidelity." Um, it just, you know, it

just it it's

>> it looks right.

>> It looks right.

>> It looks right.

>> I mean, I can't confirm cuz I don't know

the I didn't look at the Excel, but I'm

just just looking at it looks right.

>> Yeah, it does. Yeah, exactly. So, and

and I'll show you what it looks like

essentially within the breakdown of the

skill itself. So, skills um what you do

is you have the actual skill.md file

itself. So, this again is a breakdown of

what the skill is, the scripts it should

run and then yeah like generate data for

90 days, generate data of 7 days,

generate data for 10 campaigns and then

what the structure should look like. So,

you can actually use this to define it.

And you know if I want to take a step

further I can use cursor to then update

the scale itself. Um and I'll show you

an example of how to create your own

scale. And then you can also reference

files. So you can see here it says see

references metrics MD for detailed

metric definition and typical ranges. So

if we want to go back into references we

can see what metrics MD has which is all

the you know definitions or glossery. So

as a marketer, you get to define what

these are and you should be doing that

so that you know when you run these

scripts and skills, it gives you exactly

what you need instead of getting the LLM

to actually do it for you. And then the

scripts are made by cloud itself where

it's running a Python script on, you

know, calculating all this for you. So

it's accurate in some way or another.

>> Yep.

>> Cool. Um let's see where we are at.

Yeah. So let's now create we we've gone

through AB testing ideas. We created an

artifact. We got some working insights.

I think we should now just create our

own skill. Do you have anything in mind?

Tell me if this is possible. So

I tweet every day

>> and I also have a newsletter and every

single week I

basically I use my tweets like if it

rips on Twitter I'll kind of expand on

it on my newsletter.

>> Okay. Um, I have a specific type of

style how I write on my newsletter. So,

what would be really cool like this is

some this is something I would hire for

potentially like almost like a ghost

writer.

>> So, is it possible to have a skill that

basically like looks at my tweets and

turns it into long form content that I

can review and and be the editor of?

>> Okay, let's try. Let's let's let's find

out. Um, we're doing a lot. You're like,

"Maybe."

>> Yeah. Yeah.

>> Yeah. No, absolutely. I think I think we

can figure out we can we can try. So,

hey Claude, I just added the skill

creator skill. So, we're using the skill

creator to do that. Can you make me a

skill that takes an existing tweet

provided by the user and turns it into

long form content for LinkedIn

>> for uh

>> for newsletter

>> newsletter

>> for newsletter. Okay. So I would I think

what what I would do in this scenario is

making sure that we have a reference

file, right, of your existing

newsletter.

>> I would need a and would you need like

an export of all my tweets?

>> Exactly. Yeah. Yeah. So maybe we can try

to do one example one right now and then

um

>> and then see. But ideally what I would

do is actually I actually have this I

have an automated bot that looks has all

my tweets and then finds the most viral

ones and tries to like expand on it.

Ideally, we we would export all of your

tweets.

>> Yeah.

>> And then

>> and then, you know, we would in order to

keep it updated, we would need to make

sure that we're, you know, constantly

updating.

>> Yeah.

>> Yeah. Exactly. Exactly. So, um, while

that's running behind the scenes, I'm

going to scrape I'm going to get some

examples of, uh, of your posts on

Twitter. Yeah,

>> that's me.

>> Okay. So, what's a good tweet? Yeah. You

find a tweet that like speaks to you and

then we'll

>> Okay. I like this one actually. It

speaks to me.

>> Okay. Pricing.

>> Yeah.

>> Yeah. I mean, this is a perfect one cuz

it was too long for Twitter.

>> Yeah.

>> But I'd still posted it anyways.

>> Mhm.

>> Um but if I did even if I did this on a

newsletter, I would totally expand on

this. I got you. And then um

>> for the newsletter, where's where's the

best? I mean I I

>> I don't even know if you can

>> Yeah. How do I

>> find it? Go to I think Greg Eisenberg.

Do gregisberg.k.com.

>> Um All right, cool. So, let's take this

actually and

we're going to

copy this and then create

notes.

Export this example newsletter.

>> Export it as a markdown. Right.

>> Yeah. As a markdown. Exactly. Exactly.

So, we're going to go back to Claude

now. So, now that we have an example

tweet post and example newsletter, um

we're going to um up uh we're going to

upload that as a reference file in there

as well. So, what we'll do is let me

just drop this in here. Add these files

as references. You know what's

interesting is they actually gave you a

zip of the scale for you to upload. And

we're saying now like add this as a

reference into that scale.

>> Okay. Stuff is happening.

>> Things are happening. We're seeing

instructions. Yeah. So, we're adding

instructions. So, similar to I showed

before where there's instructions to

reference the file in the folder, we're

going to see a zip now with a folder

called references. And if you open it

up, you'll see these two examples in

there. Boom. So, now we can essentially

download this and re-upload it back into

Claude.

There you go. Sweet. Okay. Two

newsletter. And we're now going to go to

settings capabilities

upload a skill, and we're going to

upload tweet to newsletter.

And now we're going to write the skill.

Try in chat. I just had the skill. Can

you turn this tweet into a newsletter

format? And you you're going to be the

judge of this. Tell me if you think it's

good.

>> Yeah.

>> Um, so we'll go back.

You know, we already had it, but I'll

just copy paste this to see how it

works. Okay,

and we'll go cloud. Let's see what

happens.

I'm honest. I'd be surprised if this

crushes it on the first try cuz think

about how

I don't know. I'd be surprised. I hope

it does. So, I'm I'm I'm very curious.

It's like I this is we're doing this

live and I'm curious to see how it

actually comes out and I want to get

your honest take on it, right? Because

for me, at least from a data standpoint

to get the insights, I think it's

interesting. Um cuz like there was

challenges with projects before to get

the right insights. So

>> honestly, this is fire.

>> I mean, so tone of voice. So, we just

did this in one shot, but if we really

wanted to, we would take all of your

existing tweets, all of your

newsletters, and then use that to

generate a like a style guide or tone of

voice, and then kind of refine this. But

as a starting point, it's not bad.

>> As a starting point, it's not bad. And I

I I'll even take it a step further.

Like, this is

>> It's actually not bad.

>> Really good.

>> It's actually not bad at all.

>> Until next time, keep building and keep

raising. Not keep raising.

>> Yeah. We don't,

>> you know, but keep building. I like

uh forward this to a founder who's been

sitting on the same price point for too

long. Like I like that. I think that's

really smart. Right.

>> We talking about product market fit. We

should talk more about price

>> pricing market fit. Like that's a that's

a banger.

>> That's a banger. That's actually a

banger.

>> Like if I tweeted that as its own

oneliner like that probably would do

really well.

>> Oh, until next week.

>> Yeah, I think so.

>> Yeah.

>> Yeah. 100%. Now, it would have been

interesting if it like if we you know,

we could technically say, "Okay, now go

scrape like tweets and embed it in

there." Yeah, that would be really cool.

And the cool thing is you can actually

create skills now.

>> Dude, this is crazy.

>> Actually, yeah. Here's where it gets

interesting though, right? Cuz you can

now create skills that generate visual

graphics cuz that's that's a thing now.

You can so

>> you can you don't have to do MCP calls

like Canva or anything like that. You

can programmatically create these

visuals. So we can update the skill to

say, "Hey, now add images as well in

there."

>> Yeah,

>> this is pretty good.

>> All right.

>> Yeah. So, you know, we covered why

projects matter, how it's different, I

think, from skills, and I think we got a

good idea that skills are probably more

deterministic in terms of what you want

to do in terms of how you want to define

the skills and you can now do

programmatic code in there with MCBS and

tools and how impactful context is.

>> Yep. and uh you know essentially how it

differs from everything else. So and

we've covered some use cases as well.

The last thing I want to talk about is

kind of just like you know I saw this

report I don't know if you if you saw

from ramp where they were tracking

subscriptions for like different AI

tools and they saw that there's a dip

happening and they're saying there's

getting stickier in enterprise but the

AI is off ramping and it's not as sticky

as we want it to be because cost is

coming down. I actually want to say now

with what we're seeing with skills and

all the education and awareness around

prompting we should be able to solve

that gap because the reality is a lot of

companies are investing in AI and

there's reports now saying that it's not

actually being as productive as we

thought.

>> The issue is I think prompting and

context

>> the issue is people.

>> The issue is not Yeah. Yeah. That's the

reality right?

>> There isn't enough AI fluency and

education around how to actually do

prompting. People like write, you know,

build me a SAS 1 million AR, don't make

mistakes, you know, and the reality is

like, no, no, you got to give the right

amount of context and do some prompt

structure. And um I think when it comes

to anthropic, they do a really good job

of not only building with intention, but

creating the resources, the education to

help people actually become more AI

fluent and giving them tools to do that.

And they're very deliberate with what

they create. And it's actually real

problem solvers. Like I've never seen a

company so dialed into customer feedback

and just creating something around it.

It's almost like they heard me in

conversations about what issues I'm

having and you know why skills matter.

So um the net of this takeaway is AI

adoption may be falling and the adoption

rates may be down for this month or this

past quarter. I think part of it is just

because companies don't have the right

resources and the people to build

education um on AI enablement and AI

fluency and then once we see that come

into play adoption is going to come back

up and there's the tools now to support

that.

>> Beautiful. Well, thanks for explaining

it to me honestly and everyone else. Um,

>> for more of air, uh, I'll include links

in the show notes where you can go ahead

and follow him. Uh, X is the best place.

>> Yeah, Air MXT. A M I R MXT.

>> Cool. I appreciate you coming on.

>> Cool. Thanks for having me.

>> Thanks, man. Thanks.