# Scaling Productivity with AI Agents and Custom Skills

**Podcast:** The Startup Ideas Podcast
**Published:** 2026-04-08

## Transcript

Ross Mike, welcome back to the pod.
By the end of this episode, what are people gonna learn?
I hope I'm gonna share some wisdom on how you can use the agents better.
There's a lot of information going on right now.
I disagree with most of it, and that's what we're gonna talk about.
So at the end, whether you're building something, using an agent for some sort of work, you have the best output possible.
And is this gonna be a technical dive or you know, non-technical person can anyone can watch this?
There's gonna be a lot of diagrams.
That's all.
You're gonna make it clear to understand the concepts, right?
Easy.
Okay.
Basics.
Let's go.
So the start up by this podcast.
The first thing that I want to announce, previous episodes, we probably disagree with this point, but now what's true is the models are good.
The models are exceptionally good.
Opus 4.6 is amazing.
GPT 5.4 is amazing.
I know there's like two sets of camp where, especially when it comes to programming, people are like, oh, Opus is the better UI designer, GPT 5.4 is a better back end.
Generally speaking, we've reached a point, we're not at AGI yet, where we reached the point where the models are good.
But context still matters.
And you have the power to steer the models in a direction where you can get quality or you can get slop.
And that's what I really want to talk about.
But before we get into all that, and feel free to cut me off because this topic excites me.
Um, we need to learn how context works.
And context is the model assembling information that it needs to execute an action.
And the way the context is assembled, let's say in the coding agent, but really in any sort of agent, is there's this general system prompt, usually by the model provider.
So for example, cloud code leaked recently, and one of the cool things that um, especially as a developer, I got to do is I got to read the system prompt.
So they have this general system prompt that guides the model on how to act, what to do, what not to do.
The system prompt is very important.
And then you have a lot of people have agent.md files or claude.md files.
Now I'm just gonna say off rip, 95% of people don't need this.
The reason being is again, you have to assume that the models are already good, right?
Now imagine I told you, Greg, every time we're about to shoot a podcast, Greg, you need a microphone.
You know you need a microphone, right?
You've done this plenty of times, right?
So if I'm building like, let's say a website with um cloud code, and I'm telling Cloud Code, this code base uses React.
I don't need to because it has the code base in context, it can check the code, right?
So there is this disparity where a lot of people are putting a lot of onus on the harness and the context building, and I'm low-key starting to strip things off.
Like I'm going super super minimal because again, not to sound like an anthropic or open AI shill.
Unfortunately, I have not been acquired, none of them are paying me.
Um, but the models are really, really good.
Wait, so 95% of the time, I don't even need a bot bother with an agent MD file?
You don't.
Like it, unless this is some sort of proprietary information.
Yeah, what is the five percent of the time I should care about it?
Proprietary information that like maybe is specific to your company or some methodology that is specific to you that has to be referenced in every single conversation.
Because the annoying part with an agent.md file is every time you go back and forth with the agent, it's added in the context, right?
The cool thing about skills, and I'm gonna talk about skills in a second, the way skills are designed, the uh skills are used in a way that's called progressive disclosure.
Meaning when you have a skill file, the entire thing isn't added to context.
It's just the title and the description.
So the agent has the title and description in the context.
And when you let's say you have a notion report skill, right?
And you tell your agent, hey, I want you to create a notion report.
It's then going to check its context and be like, oh, I have this skill.
Let me check out the entire document.
So it's not in the context.
What's in the context is the name and the description, but that's enough for the agent to be like, oh, this is a skill I need, let me go use it, which is fantastic.
I'm a skills maxi.
And I'm gonna show later in the episode like how you craft the perfect skills.
But with agent.md and claw.md files, it's context being added at every turn, right?
So let's say you have like a thousand like line file claw.md, and let's say that's like 7,000 tokens.
You're spending 7,000 tokens on every run.
Now, do you need to?
Most likely not.
It probably should be a skill.
But if you have some sort of company proprietary information or like there's something specific that you do that the model needs to know at every single turn, then you use it.
The thing is, 95% of people don't have that, right?
So I'm not a fan unless that's the case.
So and and the reason being is we're wasting tokens, right?
It's in every single turn.
But this is where the beauty of skills comes.
Um, I'll show my screen here.
The your skill, again, this is not like word for word how it looks, but a skill basically looks like this.
There is a name, there is a description, and then underneath is a bunch of information.
I'm gonna put a bunch of info.
What when you create a skill.md file, what gets added into the context is actually just the name and the description, right?
The bunch of info doesn't get added.
So imagine you have two sentences versus an agent.md that has like a thousand lines that get added into the context.
We're talking thousands of tokens compared to a couple hundred.
And the agent only gets the bunch of info when it realizes it needs this skill.
So if I have, let's say, a certain way of generating a report, a certain way of structuring my code, why would I put that in the agent.md file when I can have the agent call on it progressively when it needs it, right?
So this is why skills are honestly, like I'm a shill, I'm a maxi, but people do it wrong, and I'm gonna share the right way on how do we create skills.
So so far, we have the system prompt, the agent.md, the skills, and then we have the tools, right?
So if you're using cloud code, there's already built-in tools, a read tool, a write tool, like there's many tools that it uses.
This has to be added into the context because the model, the model doesn't call the tools.
It like it's the agent harness around it that allows it to call the tools.
And then in this case, we also have our code base, right?
Like whatever, if we're building a web app, a mobile app.
I know most people here won't care for the specific framework.
And honestly, we're getting to a point if you're not technical, you really shouldn't.
Um, and then we have the user conversation.
So this is what the complete context window is filled with, right?
And this can total up to let's say, like at the beginning, this could be like 20,000 tokens.
And as the conversation continues to grow, you might reach your limit of 25,000, 250,000 tokens, and that's when you see both Cloud Code and OpenAI codex, they they'll compact, right?
So beautiful so far, right?
This is how context works.
Why skills are important and how you should generate skills.
Let's say I have a specific workflow.
For example, for my YouTube channel, you know, we we're at a point right now, uh, Greg, where we get sponsors now.
Crazy.
When I first joined, not when I first came to the pod, not a thing.
We get sponsors.
It was just your mom sponsoring the channel.
Yeah, yeah, yeah.
It's just her showing love, feeding me.
Uh, but now we get sponsors.
I get a lot of emails, and some are good, some are bad, and it's a lot of time, I'm sure you're aware to comb through and to check.
So I have an open claw agent that has its own email, right?
I I don't have it, I don't haven't given it access to my email uh because there's like attack vectors, and I've been hacked before, so I'm very careful with these things, but it has its own email.
And every time I get an email from like a sponsor, I forward that email to the agent.
Now, the first time I told my open cloud agent, I'm gonna forward you emails, check every 15 minutes when you have an email.
Um, and when you check the email, do research on a sponsor and tell me if they're worth it.
That's all I told the agent.
Every sponsor email I sent it, it was like legit, legit, legit, perfect, perfect, perfect.
There was no like there was no rejection.
There's no this is bad, or these guys are a scam, or this product's not good.
Like there was no deep research being done by it.
So then I realized, huh?
Okay, the model needs a step-by-step guide.
This is when I create a skill.
But here's the problem.
A lot of people will, I'll just write it down here, will identify, uh, identify they have a workflow, right?
You have some sort of workflow, and then they'll jump to create the skill right away.
This is the let me click hide here.
This is the worst thing you can do.
I'm just gonna draw arrows to signify that this is bad.
You don't do these.
And the reason why you don't do this is imagine you hire an employee or you're mentoring somebody.
Um, correct me if I'm wrong, you're probably going to tell them what to do.
And if they ask you questions on how to do it, you'll help them.
You would ideally like them to fail, and then you want to then tell them, no, this is how you do it.
Like it there needs to be some sort of experiential learning.
The way I've been creating skills, Greg, and I have like a hundred percent hit rate now when I tell my agent to do something specific, is I actually walk with it step by step on doing the workflow.
So, in the case of my YouTube uh analysis, I told the agent, okay, I just sent you an email, tell me about the company.
Companies, this, this, that, and that.
Okay, check their Twitter, check their YouTube, check their trust pilot, check if they've raised any money.
If two of these are have not, if two of these don't exist or not in good standing, automatic rejection.
It checked and it was like, you're absolutely right.
I was using Opus.
Um, these uh, this is not a good company.
And then it would just, we would we have a spreadsheet in Google Sheets, it'd be like no contact.
It's so frustrating too, right?
Because you're like, you give it a task and it seems like so binary, like right or wrong.
And then when you tell it, hey, like, why didn't you look at the trust pilot?
Why didn't you see if they've raised money?
You're absolutely right.
Yeah, you're absolutely like, what?
And and the thing is the reason why this is the case is the models um actually don't think.
They're predictors of tokens, right?
So when you give it English, when I give it English, it maps it on this vector graph, and then it looks for the closest resemblance, and it says this is the response, right?
So when you say what is the capital of France, it maps it again on this graph and it says, Oh, Paris is pretty close by.
Then he gives you Paris.
It has no, it doesn't think, it doesn't understand.
It feels like it understands, it feels like it thinks.
Heck, it even feels like it has emotion.
That's because it's been trained on so much data, but it actually does not know how to think.
And this is where a lot of people be frustrated.
Um, with like, why is it not understanding me?
You have to walk with it.
So I told it, okay, this is how you research, and it's like, okay, it researches.
And guess what?
This is part of the context.
And we're like, okay, now that you're done researching, when it's a good company, these are the qualities you look for.
And then when it's really good, send me an email.
And then once we had a successful run and we did it again and again, then I converted it to a skill.
The reason being is a lot of people create the skills themselves, or I mean they'll use the AI to create the skill, but it doesn't have the context on what a successful run looks like, right?
Because most of the times, especially if you're using open claw, it's probably gonna fail at the API call, it's probably going to call the data wrong.
Like, there's so many places it's going to get wrong, and I see a lot of people saying, This is so frustrating, this is terrible technology.
Why doesn't it work?
It's because you don't understand how an agent works, right?
It will mimic you perfectly, but you've given it nothing to mimic, right?
So I will do the workflow myself.
So the the updated version is identify the workflow, go back and forth and teach it.
So like I'm doing it, like I'll be like, okay, first do the research.
Here's the result, and I'm like, what do you think about this?
Oh, these guys are terrible.
You're absolutely right.
Okay.
What do you you you should go to the Google sheet and mark this as bad company?
I've done that.
Once I've had that back and forth, then I tell the AI, uh, review what you did, and then create this skill.
So now it has actual context with how it worked, and it's going to create the skill beautifully.
I don't handwrite skills, I don't think you need to.
You can use AI to do it.
They even have a skill to create skills, skill exception.
But you should have the context of what a successful run looks like.
And this is why, by the way, Greg, I don't install skills.
Like I've seen people like, oh, this notion skill, this social media skill, whatever.
I'll review it, I'll check it out, I'll even give it to my AI and be like, oh, what are some things we can learn from this?
But I don't download skills because your agent needs the context of a successful run, which you then turn to skills, right?
Um, and this is the big thing I've seen.
You see skills marketplaces, you see download this and that.
First of all, it's an easy way to attack somebody.
So I would be very, very careful with downloading some random person skills.
But second of all, again, it's all about context, right?
It's all about, and you know, open claw has a memory layer and all these type of things.
You want it to do the right thing.
And the only way it can do the right thing is if you give it the proper context.
And to me, the best way to create a skill is to work with it in your specific workflow.
Once you have a successful run, tell it, okay, review what you just did.
This is the skill you need to create.
I'll pause here.
I mean, it makes sense, right?
Because if you hired an employee, you would do the same thing.
Yeah.
You wouldn't, you wouldn't just be like, okay, go do this thing.
Good luck.
Yeah.
Uh, and by the way, this is how you're gonna go do things forever.
You would map out a workflow.
You would identify what right and wrong is, you would uh do it iteratively, and then once you've gotten to that point, you would codify it.
100%.
And I think like that's the thing.
Like, we should treat models and these agents like very new employees versus like these black magic boxes that like know everything, right?
They know everything because they've been trained on a lot of data, but they don't know your workflow, your steps, right?
So I see a lot of people who have you know 15, like right off the rip, they'll set up open claw and um 15 sub agents, 30 skills, yet you haven't even set up your own workflows, right?
And these things are cool right off the bat, and and there's a perfect time to use sub agents.
I use sub agents a lot, but the way you build, like I call it scaling for productivity, not scaling for what looks cool, right?
Like I've seen, like for example, paperclip paper clip looks awesome, cool.
I used it, I loved it, right?
But I think people would be more productive if they built up from scratch their own version, meaning, like, okay, you have your own, like um, you know, like editor, right?
Content creator.
So you're uh you're asking people to do the work, basically.
100%, 100%.
And because the thing is, it's like, look, I'm in the position where like people using like these beefed up things make a lot more sense for me.
And the reason being is like I can build a product like that, like I know what your audience wants, I know what my audience wants.
Like, you know, heck, I spin up agents and build this thing, right?
But if I'm gonna be completely honest, if you want to scale for productivity, it starts with one agent and you building up the skills, and then okay, now you've built up some skills, and now you add a sub-agent, and your one agent manages multiple agents, right?
Like, imagine this.
Like, imagine I start a company and off rip, I have 10 employees.
Never managed a team in my life.
Heck, I don't even have a really big family.
So, like, I'm alone, like you know what I mean?
So it's like you have to sort of, yeah, it's not sexy.
Um, and I apologize if this is not the cool thing people wanted to hear, but you sort of have to put in the work and build it up.
And I and I personally believe you're building skills, like your personal human skills, not skilled.md files, that when the models get better, with the agents get better, you will be more valuable.
Because at the end of the day, as long as there's no new paradigm for models, LLMs just predict tokens.
They don't understand or know the way you and I do, right?
And this is why, although like yeah, the job scene and all this stuff is scary.
I genuinely believe anyone who knows how these tools work and like knows how to build agents and like craft skills and like knows how to make them productive, we're in a for a good run.
So you're saying that if you know how to do this, you won't join the permanent underclass.
The permanent underclass.
So is the permanent underclass basically like I've seen this these this this on Twitter a lot.
Is that basically AI has replaced you?
So now you're just from what I understand, it's once AGI comes, all these white collar workers are gonna lose their jobs.
And if you don't know how to build skills, use AI.
People say you're joining the permanent underclass.
That's that's the term.
It's permanent, too.
That's scary.
So I just have a little bit of time left.
Yeah, by the way, like it's ridiculous to call it a permanent underclass.
Yeah, because that's terrifying.
I can understand underclass.
But permanent, it's like you're saying there's no hope.
Like yeah.
Um, I mean, we are in like knowledge that took 20 people 20 years to acquire is now like 20 bucks a month, right?
So there is like a huge shift, right?
People who are non technical are I think I saw yesterday, like some guy hit like a hundred million dollars um and he vibe coded the whole app.
I think it was him and 1.8 billion.
Billion?
Yeah.
So you know what I mean?
Like it is the there is a shift, right?
And I think this idea of like, well, I'm not sure.
I thought you were like billion.
You were about to just leave this podcast and just be like, no, you know what it is?
I just realized, man, I overthink things.
Like, I just need to drop the thing, release the thing, and there's like wisdom in that.
Like there needs to be this level of delusion, which I don't have.
Like, I'm trying to work on where you're like, this is just gonna work out.
We're just gonna launch the product, it's gonna succeed.
And if it doesn't, on to the next one.
Because 1.8 billion.
Yeah, dude.
Like B.
B.
USD.
Yeah, we're not talking monopoly Canadians.
I was just saying, because if it's Canadian, uh, it's it's um we're not talking carny coins.
We're talking, we're talking real Benjamins.
Yeah, yeah.
That makes sense.
That makes sense.
But yeah, like I I hope this like understanding of like again, I personally don't think you don't need an agent.md file unless you have something proprietary.
Um, skills are valuable.
Build your own, though.
Build build your own.
Like, you know, like when you asked your mom when you were a kid, oh, can we have McDonald's?
And she's like, we have food at home, we have food at home.
Build your own skills for coding perspective, from coding wise, um, a lot of the companies, model companies have realized that the agents are really good at writing code, particularly TypeScript.
And this is why there's been like you see this advancement with like Claude Cowork and like even OpenClaw.
Really, what they're doing under the hood is they're writing code, right?
They're writing code calling APIs and all this stuff.
So when it comes to building a project, um, you actually don't need skills or like you don't need an agent MD file specific to the tech stack use.
Like, I remember we used to, I'm using React and you know, convicts, or I'm using Next.js and Superbase.
I'm using this and I'm using that.
And you put that in the agent MD file, and you have like all these lines.
For the most part, unless again you have a specific specific workflow, unnecessary.
And the reason being is code itself has become context now.
So the more the more important thing is starting with a solid foundation.
Templates used to be big back in the day, people made lots of money with templates.
I believe templates are gonna have a renaissance because if you have a solid like template, right?
Like whether it be like for a web app or mobile app, because that becomes context for the agent, it's going to build on top of that, right?
And again, I didn't need some large agent.md file, I didn't need any large cloud md file.
What I needed was again minimal context usage and skills.
So if there's anything anyone can learn from me, is build your own skills, build your own skills, and there's this methodology.
I don't know if I've shared this with you, recursively building skills.
So let's say you've built your skill, right?
I have I'll draw a diagram because why not?
Let's say I have a workflow, and after you like setting up my workflow with an agent, I've decided you know what?
I'm going to turn this into a skill, right?
So this is my uh skill.md.
Now, here's the thing.
Even though you have the skill.md, the agent at some point is still going to mess up because there's probably gaps in the information it has in the skill.
So when it messes up, I'm going to work with it again.
How do I work with it?
You messed up.
Try calling the API again, try doing this again, or even ask it when it tells you, oh, I failed, I couldn't do this task.
Believe it or not, when you tell the agent why did you fail?
When you ask it, like what's the error that you got?
It will tell you descriptively, oh, I got a 500 five error.
You uh you have insufficient credits.
Like, oh, okay, so it's a credit issue.
Fine.
So I would tell it that, and then I would pass that failure back to the agent.
So let's say it did something wrong.
We identified the failure.
All I did was asking it.
I will give that failure back to the agent.
I'll be like, you failed here.
This didn't work.
Fix this.
It's going to fix it, it's gonna write code, it's gonna do whatever it does.
Once it fixes it and it's done it right, now you tell it with the new fix, update the skill so this doesn't happen again.
I have like for my YouTube channel, I have like a report generator, it calls Notion, Dub Analytics, YouTube Analytics, Twitter analytics, pulls from my it pulls from like eight data sources.
There's no way you're going to one prompt and the agent's going to do it.
But every time I tell it to do that workflow, and it takes like 10 minutes, it executes it flawlessly.
Why?
I went through five loops of this, five iterations of recursively building this skill.
And that skill is so good.
I genuinely think if anyone's going to, if like skills marketplace is going to be a thing, there's gonna be people who sell skills, like really well-defined, like step-by-step skills, because people are just creating them without having built out the workflow with the agent, right?
So use the workflow by hand, like telling it each step.
Once it's done it completely, create the skill.md file, continue to use it.
It's going to mess up.
When it messes up, you thank God you don't complain because a lot of people are like, oh, it messed up, I'm angry.
No.
This is a moment where you identify the error, tell it this is the error, fix it, it'll fix it itself, and then you tell it to update the skill file so that this doesn't happen again.
So that's a little bit about shifting your expectation, right?
Because people just assume uh it's gonna work in the beginning.
You're saying basically it's not gonna work initially.
There's gonna be two, three, five, six hiccups.
Um, and over time, it should be good.
So this is most people's expectations, right?
Yeah.
And the way I've personally experienced is it's like this.
So there's like this early area of investment that you have to make that sucks, that nobody will tell you, especially agent harnesses company, because they wouldn't raise as much money if they did.
But like this, maybe I would give it two weeks, because it took me two weeks.
Like open claw.
When I first set up OpenClaw, I thought at the same time, I'm like, what is this garbage?
Right, like it doesn't understand anything, it's confused.
And then I realized, like, oh, like, let me go lower level.
The models and the agents, like, they they don't think like you and me, right?
Like, I could tell you, hey, um, Greg, we need a report on like you know, the financials and notion.
Because you're probably we're in the same business, we work together, you would understand based on the context you have of the business what that means.
But imagine a new guy joins, like, yeah, I need a report on the financials.
So, where do I even start?
You know what it reminds me?
I wonder if we can put this clip in.
But in the office, you watch The Office.
I am not an office watcher, unfortunately.
There's a clip that uh there's a new boss, and the new boss goes to Jim, one of the main characters.
Yeah, and he asked for a rundown.
So go go the office, the office, the rundown.
Oh no.
Basically, Charles, the whole episode is about Jim trying to ask around and be like, What is a rundown?
Like, what is a rundown?
He's like calling his dad, like, what is a rundown?
You know what I mean?
He's just um he didn't have the context.
Yeah, he didn't have the context.
Yeah, and and it goes back to my initial point.
The models are really really good now, but the context matters more than anything, right?
So when you see like these large agent like companies and sub agents, and again, I'm not saying those don't work, but I'm saying probably won't work for you off rip because you haven't built it up to get to that point, right?
So, let's say, like, for me, for example, I started with um one agent.
Let me draw this.
I started with one agent, and this was like my main agent.
This did everything, right?
This checked my spreadsheet, this checked my sponsor's email, and all these type of things.
And once I had like predefined workflows, let's say for like working with sponsors, then I can actually have a sub agent.
What's the purpose of the sub agent?
The sub agent does all the marketing stuff, right?
But I'm not creating the sub agent for the sake of creating it.
It's going to have skills, it's going to have context, and it actually makes sense for me to have sub agents, right?
So I've built out my thing to like now.
I have five sub agents.
I have one for marketing, one uh for business, one for personal, and and that's it.
And I'm willing to bet if I want open cloud or open cloud with anyone, my system is more productive because I didn't scale for what looks cool, I scaled for productivity.
That was a bar.
That was a huge bar.
We got a clip that.
I was just thinking that clip, that's gonna rip.
Yeah, that's that was a bar.
Um what else do you want to leave people with?
Or is this this is the main point?
Yeah, like here's like the we've got to a point where the models are good.
The models are really good, the context matters plus the harness, right?
So, for example, um, there was this benchmark, although I'm not a hundred percent supporting it, that there was a difference between the quality of output um that cursor generated versus cloud code versus codex, right?
Um, so what that tells me is that we've reached a point where the models are really, really good.
They're probably going to get better.
The next iteration is probably going to get better, but the harness and the tools that you surround it, the context that you give it is going to matter even more.
And just like in everything in life, less is more, right?
Like building up step by step, making it productive for you first before you add the shiny new thing.
Like, because I tried all these tools all the time, like especially paper clip paperclip blew up, and a lot of people have been talking about it, and it's fantastic.
But I'm willing to bet if people took two weeks to build up to the version, because you can prompt open cloud to do all that stuff.
If they built up their own version of paperclip in two, three weeks where like they're building things that they actually need, their productivity level will skyrocket through the roof.
It's a hot take.
It's a hot take.
Might get me in trouble.
No, okay.
Who's it gonna get you in trouble with?
Maybe Pepper Clip uses a billion dollars and they don't acquire my podcast.
I think uh listen, you're you're out there, you're trying things and you're just sharing what you're learning in real time.
So if you're just you know things can change by the way, yeah.
Like two weeks from now, it could be like no, give the age and everything.
There's this new memory paper that Google released, and like now, like it has the ability to index information and stuff, but as it is as it pertains to real life, less is more, simple is better, right?
If you can't explain it in in a few sentences, you probably don't really understand it, right?
And I find that the models are trained on so much information, especially when it comes to programming, building, and like and um what do you call like day-to-day work, like financial work or like any sort of like you know, checking contracts and stuff.
Like they the model companies are focusing on that, like on white collar work.
The models are really, really good.
What matters more is the harness and the tools you provided.
And the one thing that you and I have that the models don't have is my specific workflow, my specific taste, my specific strategy of doing things, and those can be codified in skills, right?
This is why like skills make sense when you build them.
Not if you download my skill, like I have this one skill.
Um, like again, don't download it.
Dude, I'm I'm telling you now, do not download it, don't use it.
I just put it so I can get some GitHub stars.
Um I have this one skill, and it's literally a code structure skill.
And I'll put the markdown so people could see it.
Um, it's 116 lines.
It's basically after AI has generated a bunch of code, I like it structured in a certain way, so it's easy for me to review it.
And like I mentioned earlier, with skills, the only thing that gets added into context is the name and description.
So when I look at the name, it's code structure.
When I look at the description, use when multiple workflows duplicate the same operational logic when deciding that blah, blah, blah, blah, blah, some nerd stuff.
So when I tell the agent I want to clean up the code structure, it checks the skills it has, it sees the name, it reads the description, it's like, oh, this makes sense.
Then it progressively discloses, meaning once it realizes it needs this skill, then it adds the rest of this, right?
Versus if this was my agent.md file, imagine every single time, and we can actually check how many tokens this is.
Let me check.
Um what was it?
OpenAI token tokenizer.
If I go to this, so this is 944 tokens.
So if this was an agent.md file, every single time I have a chat, I'm adding 944 tokens.
Tokens ain't cheap now.
No.
But if I just have the name and the description is just 53 tokens.
And it's not even cheap.
It's just like you, you're not trying to hit the limit quicker than you need to hit the limit.
Because the model will get dumb as the context window closes, right?
So if you have like a context window, and I can draw this out, if this is your context window, and like the optimal is you're between, like there's always like maybe like 10% already filled with all the system prompt and all that stuff.
You want to be between like, you know, fresh to like 70%.
Because the closer you get to 99%, 100%, like 99, 90, 80%, it starts to get dumb, right?
And you could think of this like a human.
Like, imagine you throw a bunch of information again and again and again and again.
And this is why, like when I like was in school, like last minute studying never worked for me.
Because like I didn't pay attention the entire year.
Now I have to learn about polynomials, and I have to do these graphs, and there's this weird notation.
It's impossible for me to catch up, right?
And it's the same way with the agents.
You want to keep your context when you want to save your context window because aid saves you money, but not only that, it makes a more performant um agent.
So less is more, less is more.
Rely more on the model strengths, and what the model needs is what's unique and special about you, your workflow, your business, not general knowledge.
Don't tell the model, use React.
It knows to use React.
Don't tell the model um, you know, things that like should already be known uh for the per like you know, tasks.
Like, for example, like let's say I'm doing a financial report, and in the agents.md file, I say um, to denote money, use a dollar sign.
It's going to use a dollar sign, right?
Now, if you have a specific currency, then you like, oh, use this currency.
This is the you know, like for something that the agent won't do manually, like, won't know manually.
That's when you have like your agent.mds, claw.mds, but honestly, these are a farce, you don't need them.
Um, skills, skills, skills, skills, skills is what it's at.
Thanks for keeping it real.
I appreciate you, man.
That's all I'm gonna do.
Thank you.
I appreciate it.
Uh, like always, I'll include links where you can follow Ross Mike on YouTube and X and other places in the show notes in the description.
So go follow him there.
Always clearly breaking down things.
We uh I have to be real with you, you weren't gonna come on the show today.
I wasn't, and I'll be honest, I I told Greg, and I'm just gonna be frank.
I'm like, I don't have that banger, you know, something new drop in, let's review it.
Because if we gonna be honest, there are not that many tools dropping nowadays.
Like, unfortunately, the big dogs are running the show.
Yeah, um, the Clauds and the Anthropics and the OpenAI, especially when it comes to general purpose and and coding, they sort of run the game.
So they're releasing updates, and like all the stuff has already been covered.
So I was like, Greg, I don't know if I have anything valuable.
And what did I say?
You're like, the people, you know, you gotta think about impact.
You gotta think about what you know.
This could apply to someone's, and you showed me like a testimony, right?
Like someone sent a text to you.
Yeah, I'm gonna pull it up.
Uh I sent a text to you of someone who saw a video that we did together, and it that video got him into coding.
Now he's running a cake business and he's making 150,000 a year and growing.
And he said, the Greg and Ross Mike episode in November last year is what got me into coding.
I've recommended to everyone asking how to start out.
And I just sent you that text.
I said, it's not about the numbers, it's not about, you know, because you said in the text.
You don't see it sometimes, right?
I need everything we do to get to 200k views minimum.
Yeah, yeah, yeah.
And I'm just like, I hope this gets 200k views or more.
So like and comment to juice those algorithms.
But if it gets 2,000 and two people end up taking this information and changes their business, their productivity, how they think about things.
And, you know, I think that's why I think that's why you and myself have been put on this planet Earth is to inspire people to get their creative juices flowing.
And so I thank you for for coming on and and taking time out of your days.
And I appreciate the motivation.
And yeah, I hope this uh helps somebody and uh can't wait to be back with more.
Absolutely.
All right, catch you later, dude.