AI Agents Full Course 59 Minutes (for beginners)

I think AI is confusing. There, I said it. I think there's a lot of terms, skills, MCPs, agent harnesses that are difficult concepts to understand. So I had my friend Remi come on the podcast and explain it in the most simple terms possible. In this free course on how to master AI agents, he breaks down exactly which each piece is, how they connect together and the simplest ways beginners could start using them today. Enjoy the episode. I begged them to come on Remi Gasgills on the pod. You've structured your company where you basically have these folders and.md files that run your company. And what I want to do today is I want you to teach people in a beginner friendly fashion. This is only for beginners how they could do the same thing, how they can set up their own executive assistant, head of marketing, chief financial officer. Basically, I want you to tell us the concepts behind all this. By the end of this episode, Remi, do you think you can do that? 100% Greg, we're going to go through all the concepts that make up an AI agent. And by the end of this video, you will know exactly how you can build up agents to run complete departments of your life and your company within any agent platform you choose whether it's Claude Codex, OpenClaw, Manus, all of them. All right, let's do it. Sweet. So one of the reasons why I really wanted to make this episode is because I feel like the AI landscape is moving into like stage two from chat to agents. And most people are getting left behind right now just using the chat models. And the founders and employees that are utilizing agents are like no word of a lie 10 to 20 times more productive in their day. And when you stack that up over days, weeks, years, you're going to just be miles ahead of the competition. So I really want to make this episode today to help bring everyone up to where the AI landscape is at the moment. And to start using agents to manage every department of your business. So the key thing to understand here is chat models versus agents. Because the word agent is thrown around lots online. I'm sure you see that Greg like, "Hey, I agents this, agents this, use this agent for this." And it's kind of lost a lot of meaning. So I wanted to give start by giving a really clear definition of what an agent actually is. So the way I think of it is a chat model is question to answer. But then an agent is goal to result. So moving from just like you asking, "Ii replies," then you do the work to you giving the agent a task, it planning out the task and then executing and then delivering you a result. Does that make sense? Crystal clear. I mean the way I think about it is chat is kind of like ping pong back and forth, back and forth. And agent is, you know, you're giving it, it's a goal. I mean the best way you're giving it a goal and you're hoping that over time it gets better and closer to that goal. Exactly. Yeah, that's exactly it. And I just think that's a nice way to lay down your head. Is chat is question to answer, agent is goal to result. So when you chat to an agent, you might give it a task like, "Build me a website for XYZ." And then it goes away, it does its work and outputs this wonderful website to you. But it's really important to understand what's actually happening in this step here. So inside this agent step, we have what's called the agent loop. So you give it your prompt or task and it goes through these three steps here, which is observe, think and act. So let's just say for example, we're actually going to do this demo after this, but if we gave the agent a simple task like, "Build me a minimalist portfolio site for Greg Eisenberg," it's going to start by, like you've loaded in that prompt, it's going to check if there's any files in the workspace that it can work with, like maybe you've got some information on Greg Eisenberg. And then it's going to think about what to do next. It's going to act and then it just keeps going through this loop. So for that actual example of building the portfolio site for Greg Eisenberg, let's just say it was a blank agent. We hadn't given it any context. The first thing is it's received this prompt to build the website. And the first thing it's going to be thinking about is, "Okay, well, I need to build this website about Greg. Who the hell is Greg Eisenberg?" So it's going to then decide to do some research into Greg Eisenberg. It's going to research everything about Greg and then feed it back into the subserv step. So then it's going to think to itself, "Okay, so I've got this prompt to build a website. I've now got my research here, so I know exactly who Greg Eisenberg is." And then it's going to start thinking, "What is the next step?" And the next step is probably to write up a plan to build the website. So it might write up that plan, feed that back in, now it's got the research, the prompt, the plan, and it will think, "All right, what next? I should probably write the code." It'll write the code, feed it back in, and it just keeps going through this loop as many times as it needs until it can conclude that the task is complete. And how it concludes that the task is complete is based on the parameters that you set in your prompt. So if you're giving it a research task, you might say compile 10 sources and then create a report as a PowerPoint. And then once it's compiled 10 sources and build the report as a PowerPoint, it can conclude that the task is complete and then give you the output as the user. The agent itself is made up of these four components. So it's the LLM, which is the brain behind it. So think like, you know, "Claude Opus 4.6" or "GPT 5.4" or "Gemini 3", it's the model. It's got the loop, which means it just keeps going until the task is done and doesn't stop after one response. So you're going from ping pong to like it continuing to go rather than you having to sit there babysitting it. It connects in all your tools and then it connects in all the context. And a platform that facilitates this process and basically facilitates this loop to happen is known as an agent harness. And all of the popular AI agent platforms on the market that you'd be familiar with are just agent harnesses. They're just applications where this loop is facilitated. And I want to actually run this little prompt I prepared earlier. I want to open up Codex, Claude code and anti-gravity. And I'm going to show you this loop actually happening in action. So I've nicely prepared before the episode these three demo folders, which we're going to run in. So I'm going to open up demo one to work in Claude code. And the way these folders work is if you've used, if you're familiar with any of the chat models like Claude and ChatGBT, there's a project's feature, which is where if I open it up actually, trying to get dizzy with me switching tabs so much. But if we create a project here, it contains all your chats in one place. It allows you to upload all your sources here, which is your context. And then you can even add custom instructions, which tells it how to behave within this project. And that's also known as a system prompt, which we're going to dive into how to do this with agents as well later. But it's a similar concept that you'd be familiar with if you've used projects before. But instead of the project being here on the cloud, we're actually working within projects that are local and our computer. So I've just selected this demo one for now. Then we're going to run, build a minimalist portfolio site for Greg Eisenberg. And then this little bit here just tells it to actually spin it up like to publish it on the web in a preview mode so we can see what it's done. So I'm going to run that. So this is Cloud code. Yes, right now we're in Cloud code. And this is just accessing it through the desktop app for Cloud. So I'm just going to run that. And then I'm also going to give the same prompt to codex here. So this is the codex app. And you can see same concept. It says let's build. We can choose a folder and our computer to work in, like demo too. And then we're going to give that a prompt as well. And we're going to tell it to host it on a different one. And then also in anti-gravity. So you can see same concept going in selecting a folder. And then we will give it the prompt as well. How should people think about security and these different products? I like to think of security as in just like scoping what they have access to. So by default anti-gravity Cloud code and codex they're very, very secure because they're built by these massive companies that have a lot on the line to protect. And I just, you know, if you're building out these agents to manage different elements of your business, like the other way I built one that does manages meta-ads. And obviously that's quite a risky thing to give an agent control over managing ad budgets. So it just comes down to like what you feel comfortable giving the agent. And also you can control what privileges or you can control what tool permissions as access to so that if it was compromised for whatever reason, the worst case like isn't that bad. And that means you know just giving it like read only access to certain important platforms and stuff like that. Yeah, totally. I mean comparing it to like open claw, which is like, wait, I want to touch on it in as well because that's same thing just another harness but it's just like the Wild West. Cool. And one thing like a nice little analogy to think about these harnesses is what we're going to learn today is we're going to learn to drive. So we're going to learn about how to steer the car, like how the pedals, the brakes work, the accelerator works, the hand brake. But then once you know how to drive, you can kind of jump in any car, whether it's like an old Toyota, a Range Rover. And you inherently sort of know what to do. And that just comes down to understanding all these key concepts that we're going to go through today. And you can think of the agent harnesses like different cars. And some of them will have better features, like seat warmers and cruise control. But it's all-- once you know how to drive, you can pretty much jump in any of them and use them. So we've just got our thing over here, building the website for Greg. And it's going through this agent loop right now. So you can see here it's actually decided that it's going to launch an agent to go and research Greg Eisenberg. And I've connected it up to complexity. So it's now using complexity to research Greg. So it's going through its first step of the loop. And I imagine that Codex has also done something similar here. You can see it's still working, but it's gone. And started to build this out through the loop. I think Claude code does the best job of actually displaying that loop and allowing you to see what it's thought about compared to anti-gravity and codex. But it's all just going through the same sort of loop process that I described earlier. And I think-- When you say you hooked it up to complexity, it's not like you asked it to hook it up, right? It just sort of did it. Yeah, because I've given Claude code complexity as a tool via MCB, which we're going to get into very, very shortly, all about MCPs, which is just connecting tools up. So we can see that in anti-gravity. It's gone-- you can see this thinking process. It's gone. I'm now examining the current directory to figure out if there's an existing project or if I should build one from scratch. It's then going, I'm now going to start to build this thing. And then it's built the website. And it's given us a little local host preview here. So it's created this nice little portfolio site for you, Greg. What's interesting is it's super minimalist. And I mean, it did its job, right? Like, this is-- I would totally launch up thing like this. It actually looks really nice. Did it scrape your email address correct? That's not my email, Jess. And I don't want to talk to any more. But yeah, there's a few copy things. But other than that-- Yeah. It's done a pretty good job. It did. And if we go-- that was anti-gravity. If we go into codecs as well, you can see here it's finished doing its website, which is somewhat similar. I think I prefer Gemini's. Yeah, agree. If we check out Claude as well, it's still going. You can see this loop, right? It's gone. OK, it's first off, who is Greg Eisenberg? It's gone in research Greg. Then fed it back into that observed step. And it's gone, all right, what next? Now I need to create the HTML file. So it's written the code. And then now it's gone. OK, so he wanted to spun up on this local server. So now I'm going to spin up on the server. And then the last iteration of the loop is to check that it's actually done and can conclude the task is complete. It's opening it up and screenshotting the website. And then reviewing the screenshots to check that the website is complete. And you see here, it's done another pretty good job. This one's very similar to the Gemini one. It's true. But yeah, that's just demoing how that loop is actually working in real time. Yeah. I mean, what comes to mind just by watching this is how many people on the planet would benefit from a very clean website? And how do you set up these agents so that maybe it's like a cold email loop, right? You're sending cold emails. Hey, I built you this website. So into a business, do you want it? It's going to cost $250. Yeah, that's actually a great idea. Pre-making websites for companies. And it's like an off-the-shelf thing. It's like, hey, I made you this website. If you want to, like if you want to own it, it's $250. You can just do a mass cold email thing. Cool. So I think that's pretty much illustrated that agent loop example. So I'm just going to go back to our trusty board over here. But you can understand that all these apps are just different flavors of the same thing. And then what we're going to be working up to today is my workspace looks something like this. Is I have a big, like a folder of each company or client that I'm working in. And then I'll have folders underneath with all my heads of departments. And then within those heads of departments, I'll have skills and MCPs, which we'll get into. And context. And then I've got like an overarching one at the top to just sort of manage them all. But we're going to be focusing today on building out this executive assistant to take care of just your manual day-to-day task and free up at least once, two hours extra per day. Cool. So to build this out, like we did with our demos, it's running off your local files. So we're going to create a folder here called executive assistant. And also through building out this assistant, it's going to allow us to clearly explain each of the concepts of building an agent in real time. And the way I like to think about building agents is onboarding them like a real employee. So if you took on a real executive assistant, you couldn't expect them to come into the office and you'd give them a task without explaining your business first, your clients, what you do, the tools, because they just would not be a very good executive assistant. So that's the first step that we need to go through when we're building out this agent. So I'm actually going to work in co-work at the beginning. So co-work is just another agent harness to do pretty much the same thing as all the others, just that loop connecting in your tools and the context. So you can see here, this was my little previous session where I was building some diagrams. But we can go-- and you can follow along and include code or codex or anti-gravity or whatever agent harness that you want to work in. But I just think that co-work has really nice, simple UI for people to just understand really well what's actually going on. So we're going to open up this executive assistant folder. And you can see here that if we ask it, write me a cold email and send that off. So people are going to ask, how did you transcribe? You did a voice to text. Yeah. So that is-- I use one called monologue. But there's a lot out there on the market. Whisper flows another popular one. And it just allows you to hold a little button on your computer and just yap away. And it will just transcribe it neatly into text. And I find it-- It looks good. My log looks really good. Yeah, I think it's built by the team at every-- everything that's here. It's a cool product. So what it's asking-- it's straight away. It's got no context here. So it's working out of that folder here on our computer. But there's nothing in the folder. And it has no memory of our previous sessions. And it's asking what do you even sell? And then we've got to kind of give it, who do you target, what tone do you want. This is all things that our executive assistant should know. So I'm just going to stop the response there. And one thing that's really important to know, which might be a bit of a shock moving from chat to agents, is that these agents, memory, work a little bit different. So if you're used to using chat models like ChatGbT in Claude, if you open up a fresh session in one of these chats, you don't give it any context. You don't upload any files. And you just say, who am I? And what do I do? It's going to know a scary amount about you. And that's because with these chat models, they have memory built in automatically. So every time you sort of say things that are important, the chat model saves it to its memory in the cloud that you can't see and you can't control. And with agents, you have to set up memory and control exactly what you give it. And I think that's actually a benefit, not a limitation, because what happens is if you're using ChatGbT, and it's got the auto memory, you're having conversations about three different companies. Maybe you're asking for relationship advice. And then all of a sudden, when you ask it to write a landing page copy, it's pulling in context from all these other places that you don't really want in there. So with these agents, you need to actually set up that context and memory. So as you can see, when we asked it to write a cold email, it just had no idea about anything. So we need to give it a context file. And the way you do this, right? So you can see this example here. It doesn't know anything about us. And that's because we haven't populated what's called an agents.md file. And an agents.md file is just like a system prompt. Just like if you've created any custom GPTs before, that field for custom instructions. Or in the project, like I just showed before, you've got that field for custom instructions. And it just gives it this context that's always there, always on. And you put in there things like it's role context about your preferences for working. and then what happens is every new session before it answers your query or task, it loads in all this context to its brain as part of that observed step in the loop. So I have pre-prepared, pardon me, so I've pre-prepared a agents.md file here. So if we drag this in over here, this, when you're working within Claude code, it's called a Claude.md. When you're working within Codex or OpenClaw, it's an agents.md, but it's all the same concept. So we can drag this into our folder here, and if we open up this file for a little preview, we can see here, I've got in here, all about me, what my business does, my working preferences, like the tools that I use and what fall, like notion project management, Stripe, we've got all the information, my item customized, loaded with context here. And I pre-prepared this, but if you want to make one of those, you can just use Claude chat or co-work whatever, and you can ask it to help you build out this agent.md file, and so just ask you interview style questions to extract all the context from you and then build the file. So if I jump back in, now if I go to a new task, same folder, and we say, write me a cold email, it's going to have all that context. Yeah, that's what we hope. That's what we hope. There we go. And it knows, you can see this file's over here. It knows automatically to load in this file if you tie it look correctly. Yeah, it's basically just like a reminder file. Yeah, pretty much. It's just like loading it in so it has all this set context before you even start working. And one of the other big shifts to make, which comes with moving from chat to agents, is prompt engineering used to be the big thing. It was like, here's the ultimate prompt for going viral on social media, or use this prompt for this. And now it's all about context engineering. Now it's all about context engineering. It's about how well can you load up your agent with all the information about your business, so that your prompts can be stupidly simple, like write me a cold email, and you're still going to get an amazing result. You can see already here, it's already asking like, is it a brand-os sponsor, potential partner, or consulting client? So it's already got that context book of call. You know, it's loaded in everything that we've given up from that agent's.md file. And then now we've got a pretty decent cold email there, ready to go. So that's basically agents.md files for you. And you want to create one of those to onboard your agent with all the context it needs. And if you have lots of context, without getting into too many advanced concepts here, sometimes what I will do is I will create like a folder called context. Load that in. And in here it's got different files about me, brand voice, idle custom profile, etc, etc. And then in order to keep this smaller, I will then just say in this cloud.md file before answering any questions or before doing any tasks, read my context folder to understand about myself and my business. Because by default, if you just have this context file in here but no, cloud.md, it won't load all that into the session by default. But if you tell it in this file that it always loads in, so then check this file, you can start to like string all your context together. And a lot of people have done that with obsidian, so they'll have like in the cloud.md file, they'll tell it to go check their obsidian vault for their second brand to go and find context. So that is agent.md files explain. So that's how you actually when you're onboarding your agent, like our executive assistant, you can train it up on who you are and your business. And then as you can see here, you know, I've got folders for all these different roles in my business. And in the head of marketing, that cloud.md file would look somewhat similar. But in the top, it would say instead like you are my head of marketing, you speak like this, these are your tasks, these are your roles. And then the second thing here is about memory and the self improving loop. So we've solved the problem now trying to get to dizzy with me switching tabs. But we've solved the problem of our executive assistant not knowing anything about our business. But now we have a new problem, which is it doesn't really remember the intricate details or your preferences across sessions, unless you're manually going and updating that cloud.md file. So you can see here if we go. My favorite color is lavender. It'll probably say something like got it noted. Yeah, that makes sense, right? Because it's and it's adding it. Where is that adding it? It's not adding it. That's the thing. So we can tell it my favorite color is lavender. And it's gone. The users just shared use that that thinking step. It's like the users just shared this. Like, no, nothing needed good to know. I'll keep that in mind. But then if we go into a new session, same folder and we go, what is my. Favorite color. Mine my spelling. It's going to say I've no idea what your favorite color is, even though we just told it. That is an issue, you know, because if you're working, you know, you've got like a head of sales or something and it keeps it signs off your emails wrong. And you tell it, you correct it and you say, never sign off emails with cheers, say warm regards. And it will go, OK, got it noted. But then the next day you start working and it does the same thing again. It's like, well, like my agents broken. Really, it's not it's running off those context files in the back. And unless you are manually updating it, it won't know to save that preference. So what I like to do is I like to add in something like this to my agents.nd file. So this is just a little simple thing. You can pause the video and copy it. But I like to, I'm just going to remove that context file for now. That was just to illustrate that example of adding more. But we're just working with this one file for now. So I'm just going to open this up so I can edit it. And I will quite often add something on the bottom like that little snippet. And this basically just says, actually, you know what, I might just add it at the top. Just so it's there. Top of mind for my agent. Because I think this is really important. So you can see I've just added this in. It just says read all files in context. Read memory.md. This is what you've learned over time. And then when I correct you or you learn something new, update the relevant section in memory.md. And it's just got a couple little things here. And it just says keep memory.md current when something changes update in place and replace outdated info. So we can do command S to save that. And then I'm going to add another file here. We can actually just duplicate this. And this one, I'm going to call memory.md. And then we can open up this one. And I'm just going to remove all this context here. Except I'm just going to keep those sections. So memory.md is basically, I mean, it's just what it sounds like, right? It's basically like you want to, you know, if the goal is to build, you know, AI employees that do things for us, they're going to need to remember our preferences, right? A good employee remembers preferences and learns over time and not back compound. So memory.md is just a place that you can just make sure that over time, you know, your whatever you're using, co-work, whatever. It ends up getting compounded, getting smarter. So ultimately you might be trying things like Corelc and you're not getting good results. And a big part of that is you don't have a cloud.md and memory.md sort of. Yeah exactly. Exactly. And now the thing is some of these agent harnesses have started to add in this memory system that we're doing manually telling it to update. Some of them have got that built in automatically, like OpenClaw, and I believe like Manus and some of the others have that built in automatically. But it's still important to understand because it's just doing the same thing under the hood, except they've just set this up for you. So we've got this here now, we've got our memory.md, our cloud.md. And then now if we go back into co-work, if we do a new session in that same folder and we say my favorite color is lavender, better remember. For the sake of the demo, I hope that it does what it's told. It's gonna remember it. You can see here. Perfect. It's gone good. I'll remember that. Let me save it to memory. And now you've got this big memory file that builds up over time. And whether, for example, this is your executive assistant, so it might be saving preferences like how to sign off emails or don't connect with clients on Slack. I always want to keep client comms on email. But if you're building out like a head of marketing, it might be preferences about how you like your ads structured in Facebook Manager. If you're building, if you've got a folder where you're working on a website or an app, it might be things like don't use dark mode and then it will update, so it'll never use dark mode again. And there's just compound over time. So as you start to build up these rules, the amount of errors go down and there's just compounds and compounds over weeks and months. Remi, have you seen some of these memory.md files get so big that at a certain point, it's just ineffective? Great question. I personally haven't had that happen to me yet. I haven't hit that threshold. But a best practice for those call.md files is to keep it around like no more than 200 lines. And yeah, I could imagine if you started to build this up over years and years, you'd eventually hit a point where all the rules are stepping on each other's toes. And you could probably go through and do a bit of like a manual clear, but I haven't hit that threshold yet. Cool. So people don't need to worry about cluttering their memory.md. I wouldn't worry too much. I mean, if it's saving like this, like the silliest little things, like the tiniest corrections, you can maybe update that call.md to say only save substantial corrections. And then you can have more control about what it's saving. So that's probably what I would do there. But once you've set this up, now when you say something like quit writing so formally, it's going to do the task, then update its agents.md, or in this case, claw.md, to keep time casual, never formal. And then now in any new sessions, it's going to keep that preference over time, which is pretty cool. So now we've got our executive assistant set up with memory, and we've just given him his role. We now need to connect our tools. Because by default, most of these agent harnesses, they just have web search baked in. But if you want to actually start linking it up to your tools, like Gmail, calendar and everything else, which is where the real productivity gains are made, you need to do so via what's called MCP. And I actually got Greg. I got this MCP explanation from when you had on, is it Ross Mike? Yeah. So this, he did a great explanation. And it just dropped into my head really nicely. And it's basically that before MCPs, your agent or your LLM, in order to speak to tools, it had to kind of learn their language, because called Speak's English, notion speak Spanish, Gmail French, your browser speaks Japanese and Slack speaks Chinese. And it was capable of connecting to those tools, but it required these extensive custom developments that took a long time. But then Anthropic actually created MCPs, that right? Yep, it's right. Yeah. Anthropic built MCP to basically sit as this translator in between your tools. So that Claude can still just speak English, and your tools can just speak their languages. And this MCP speaks every language and then just translates your calls from your agent to the tool and then from the tool back to your agent. So just set a really easy standardized way to connect the tools up. And that's what we're going to be using to connect all of the tools to our executive assistant. So if we go back into co-work here, you can see that Claude make it really, really easy to connect up your tools. You can just go to connectors, browse connectors, and they've got like hundreds of all the biggest apps that you probably use. And you can just, you know, add them, sign in, pretty self-explanatory. But I believe codecs would be the exact same. You know, you can go skills or if we go settings, they probably have like a. And then like Manus is the same. For example, if you go into Manus, we can see, we can go and connect our tools. Very, very similar process. And then same with complexity computer, you know, you go to connectors and you can connect all your tools in here. It's just all using that model context protocol, MCP. So I've already before the episode, gone and connected all of the tools that I use most, like Gmail, Google Calendar, Grenola, and Ocean. They're all set up already as MCPs. And what I'm going to do now is I'm actually going to open up this executive assistant folder in Claude code to sort of demonstrate how these harnesses are all the same and they work off your local files. And the real future proof AI stack is just having those markdown files on your computer. And the reason why I like to work in markdown files is because it's just the easiest sort of format for your LLM, for your agent to actually digest and understand, compared to if you were to give it your files as like a docs or PDF file. So I like to use Claude code within Visual Studio code. So you can see here, I'm just going to. It looks very similar to anti-gravity. I'm just going to open up our executive assistant folder here. And the way that I see the future of this ongoing Greg is I think that everyone's going to have their. What I call an AIOS, like an operating system. And this will just compound over time like you saw with adding the rules and getting less errors. But with adding your tools and then skills, which we'll get into, which is basically just training AI on your processes. And I think that everyone's going to have like an AI operating system they work in. And everyone will just have personal agents and agents to manage each department of their company. And people won't actually use these apps anymore. Like I've connected up Gmail, Google Drive, Calendar, Granola for my meeting notes, Stripe for payments, Notion for project management. And I don't even enter these tools anymore. I just sit in Claude code as one central place. And an example here is I sent myself before the episode. I sent myself an email from a fake prospect. And I also entered in Granola, a fake meeting with this prospect. So now I can say things like summarize my inbox from today. I'm, you know, someone might ask like, well, how important is that really, you know, like, is that such a high value task? Like, what are high value tasks that you're actually getting done here? So, or maybe you get a lot of emails, you know? Well, if, you know, emails are a big thing if you do get a lot of emails, but just having like all those tools connected in one place and not having to switch and copy paste context. So you'll see an example here, right? So we've got summarize my inbox from today, which is like one of the most basic agent tasks ever. But we can see this is one I sent earlier. We've got this one email here, like I've called today excited after your call once next steps. So I might just say here, okay, great. I review my meeting notes with Maltoshi from today and then draft up the email, sending the proposal and creating the Stripe payment link and then go into Notion and set up the project. And where this starts to compound even more is when you start to build out skills for each of your processes, because every time I do a process, even like this manually prompting it, and I know I'm going to do it again at some point, I'll then just turn that into a skill. And then you eventually end up, if you automate like three to five tiny manual processes each week with skills, you eventually end up automatically like your entire life with these agents. Right. So it's not so much in like summarize my emails where it's super, super valuable. It's like, that's where the starting point is and then we want to like manipulate it and use it and go deeper and stuff like that. That's when exactly these tools really, really are valuable. And you can see here it's now connecting on my tools. So it's going into Grinola and found the full meeting of what we went through today. It's now going into Stripe to create the product link. And then it's going into Notion to set up the project. And then it will, it should create the draft ready for us to go to send out. This is really like a new way of working. Right. Yeah, it is. It's so new and I, and I, even this task, it's really simple, right, just sending an email based on a call with a proposal link and stuff. But like, even if you can just do something like seven times faster without having to go into all these tools, copy the meeting notes into the page to give it context on your meeting, it really starts to compound. Then you start to fit like a week in a day and then seven weeks in a week. And stack that up every year and you're going to be miles ahead of everyone else. And when we get into skills, you're going to see how this continues to get even better. But you can see here it's drafted the email. It's pulled in all these insights from our call in Grinola, which is like where I do my meeting notes. And then it's created the strike payment link here, ready to go. That's cool. And then now I can just go send this email. And we use my G-Mode integration to go and send it. And then that's really cool. It is a, I think this is the new way of working. And Cody Schneider, who had on the pod the other week, I saw a tweet from him and he said that in the future, everyone's going to have like an AI operating system like this. And you're going to have like the 100x employee because everyone will come into their role with a pre-existing AI operating system and then build out skills for all their manual processes, similar to how us describing and just keep building skills each week for anything manual that comes up until eventually their entire life and work life is automated. Great as you can see here, it's now created the draft here in GMO ready for us to go and if we're happy with it in the plight. I could also just ask Claude to send it there and then. But then what, like also gets really cool is, I'm going to demonstrate now how I actually build out skills for these processes. So I know I've talked a lot about skills so far. I want to just give a little overview on what skills actually are. So the easiest way to think about skills is SOPs for AI. So standing operated, standing operated, oh my god, standard operating procedures for AI. So it means once you explain something once, you never have to explain it ever again. An example of this is without skills. If you are creating a proposal for a client and you're sitting in your Claude chat or whatever agent harness you're using and you ask it to create this proposal, you're probably going to go back and forth a bunch of times, remove, change the formatting here, use this color blue for this part, put the price at the bottom instead of at the top. And eventually, maybe after 15 minutes, half an hour, you landed on proposal that you're really happy with. And you send it. And then next week, you want another proposal written. But unless you're going and finding the same session and working in that same session, it's going to have completely forgotten all of these preferences. And even if you have that memory system set up, these kind of things you don't really want clogging up your memory, they're better off as skills. Which is basically it packages up that process into a dot skill file. And in that dot skill file, it's basically just a markdown file that explains the exact process that you went through. So you could create a proposal skill. And then every time you now you need a proposal written, it just takes that skill, knows exactly what to do. And then you can have that proposal the same way every single time. So is a skill like a memory file? What's the difference between essentially a memory dot MD and a skill? Is it just a memory dot MD file for particular job to be done? That's pretty much like exactly it. And all of these agent harnesses pretty much now have skills as a feature. So you can see here, like if we go into codecs, for example, they've got skills here, same with Claude as well. And when you're working with these agent harnesses that operate like mostly locally off your computer, you can see here, it actually operates out of this hidden file called a dot Claude folder, skills. And these are all of the skills that I've created. There's tons. And if we open one up, for example, like this one here, let's find a good one. For example, I've got this one here for writing viral hooks. And in this skill, we have a dot skill file, which is basically like your memory dot MD, which explains the exact process for writing viral hooks. And then it's also got packaged in here, some references like hook formulas. Okay, so wait, so how did you create that skill? Okay, so there's two ways that I find useful to create skills. Is one, you can have an idea of a skill you want to create off the bat. So like viral hooks, for example, I had this course on viral hooks, which I transcribed, put it into Claude. And Claude has this by default. It has a skill creator skill added into it. Same with all of the major agent harnesses. They'll have a skill creator skill. So it's kind of like skillception. You use the skill creator skill. And you say, hey, take this course on viral hooks and create a viral hook skill. And it can create it like that. That's one way. And then it will package it up nicely with that skill.md. It'll do the whole thing for you. Wait, you asked it to take the course. Yeah, I uploaded the course, like the full changes group upload. Literally. And I said, yeah, based on this course on viral hooks, build me a viral hook skill. And then I use that for my content team. So just like we're building the executive assistant folder, I've got a folder called content team. And I've got that that uses that skill for me. And the second way to create skills is going through a process manually once with Claude. And then if you know you're going to have to do it again like that proposal example, you can just say once you've done the task, hey, create a skill for what we just did. And it will package up that process you went through. And that's the second main way that you can create skills. So in your viral hook example, if you go into that folder again. Yeah. So you have a references folder. Yeah. So that is probably like was that, yeah, let's open it up. I'm just curious. See here it's got a full thing about like, so was this from like four years? Yeah, this was basically from like a little course I put into it. And did you ask it to create a reference folder? Like how should people think about? No, it just did it. It just did it. So I think what would be great is if we could actually demonstrate building a skill live. And so for example, like this process here, I could create a skill called like a daily brief skill, you know, that goes through and summarizes like your calendar, your inbox and your projects in motion and plans out your day for you in the morning. And then you can run that on a scheduled task because a lot of these agent harnesses now, are starting to introduce scheduled tasks. So you can just run it on 9 a.m. every morning, use my daily brief skill to prepare me for my day. But I think another cool one here, just to show you an example, right, of how my new, like how intricate I make these skills, is let's just say for this fictional meeting I had with this person, I might say, can you draft up an email? I want to refer Maltoshi to my good friend Sebastian, who has an AI automation agency, and can help them out better with their needs. And then we can just go, Sebastian's email is, and we can just say that, right. And then now it's going to be able to take the notes from granola, all the context, and then draft an email connecting these two, a prospect with a friend. And I have different referral things set up like that, with people with marketing agencies. And that's just like a little manual process there, tiny. It maybe takes 15 minutes out of my day. But then I can just go, I want you to use your skill, create a skill, and create a Sebastian Refer skill, so that whenever I ask you to refer someone to Sebastian, you know exactly what to do, and you know his email address. And then that'll build out that tiny skill for us, tiny process. But it means I know in the future I'm going to have to refer someone to Sebastian, and even if this skill now saves me 15 minutes, another five or six times, they start to compound when you create skills for every single little process in your business. Yeah, I guess it's like, we should just be asking ourselves, you know, in our day to day life, like what are all the jobs to be done, and what are all skills that we need, like what are the repetitive processes, or SOPs as you talked about, and then just setting up as many as possible, right, to make your eyes easier. Exactly. And just to give you a little demo here, so I sort of alluded to that folder structure at the start of the video, and this is it here. So I've got Workspaces, I have with Remy, and for example, I can open up my content team. And within this folder, this is just like a little more elaborate version about executive assistant, but we've got our Claude.md in here, which explains, you are like the main orchestrator, you have these subagents. It's just a more elaborate version of that Claude.md, but I've got a skill within this for like a meta-adson analysis. So that was a process, for example, if you're a marketing agent, ZO-NAR, this is probably like the kind of stuff that you can get inspiration from. Like ads analyzing, you know, taking competitors at libraries, breaking down all the creatives, and their landing pages. So I built out this ads analyst skill, right, literally just do ads analyst, and then I paced in like the ads library, URL like that. And I'll click Run. And I did an example yesterday with the UDI, which is a super large e-com brand, and it ran through and basically scraped all of. It took screenshots of all the landing pages. It went and scraped all of the ads that are running, all like 220. It then did a full deep dive here on all the ads. Visual analysis, copy analysis, why did this work, what could be improved. It basically did a breakdown of all the landing pages with screenshots. And it did a master report here about everything that's gone on. So it did a full breakdown. And that was like a manual process that I would have gone through when I used to run like my marketing agency. And that probably would have taken me like three or four hours. And then I went through to build out this skill. I went through the process once with Claude. Like I started a fresh session, and I was like, "All right, go to this ads library, "I will scrape this, do this, do this, do this, "for like two hours." And then after I've done the entire process, I just said, "Use your skill, create a skill "to make a skill for ads analyzing." and package up the entire process we just went through as a skill. And then now whenever I want to do that process again, I can just invoke the skill and it knows what to do. Which is pretty cool. Crazy. Absolutely crazy. So now the refer Sebastian skills live. And now whenever I want to refer someone to Sebastian again, I can just say, yeah, refer to Sebastian. And it will just start to use that skill. That's example of their tiniest process, but you build like those up for all these little tasks you do day to day. And then it just compounds and compounds and compounds. And I can already think of an idea here where you could then you can change skills together. So you could example have like a meeting prep skill that prepares you for a meeting by researching the guest and compiling some talking points. You might have like a podcast research skill, for example, Greg for a guest that's coming on. And you might also created a morning brief skill. And in the morning brief skill, you can say if there's any meetings coming up or podcasts in my day, use the podcast research skill to research the guest. And you can like chain them together and build like some really really cool workflows. Yeah, and you can have it so it sends you an email, right? Yeah, exactly. And then now these harnesses of science get more and more autonomous. Like they're starting to add like you know in the car they're starting to add like cruise control and stuff. Like now within most of these harnesses, you can schedule tasks, like in co-work or cloud code now. And you can like, for example, this one here, you can go new task. And I could say like run my morning briefing skill. And then set that to go every morning at 9 a.m. And then now it's like an automated workflow that like you've just got running every morning now, which is pretty cool. Yeah, I'm doing this right now. Like for example, I'm buying a new car right now. And it's like a particularly unique like color that I want and feature set. And there's just none none really available. So you know every three hours I have I'm scraping all the different car marketplaces. And then I'm getting a notification that you know when something comes up and it's it's crazy, right? It saves me. I'm one of those people that like if I didn't have this, I would be spending an hour of my day just like checking religiously every single you know car max and cars. Car and auto trader and all these websites and refreshing like a insane person. So yeah, the schedule. Yeah, great example there. But you know this like the this is a skill that would be relevant for my executive assistant. Same with that car one. That could be a good executive assistant skill. But then I've got those more elaborate skills built out for like you know my content team that adds library scraping one. And then I've got like you know, research weekly research skill for my newsletter team. And that runs on a schedule every Thursday morning to go and scrape like I built the skill out. So it goes and scrapes Twitter and Reddit to find what's new in AI. But yeah, skills are so so powerful. Combine them with like your MCP so it can use your tools. And then you can start to just train up your agent on all the processes in your business. And I did a build out on open claw for a agent to manage meta ads. And it went pretty viral. The way I built this was with all these key concepts. So open claw functions the exact same way. So I hope it hasn't timed out. But I've remote access into my open claw dashboard here. And you can see it's just operating off an agents.md file in the back end. But instead of the dot clawed folder, it's in the dot open claw folder. And then it's got a couple of these other ones here. It's got a memory.md. But some of these other ones that it's added on, like a soul which tells its personality and an identity which tells it who it is. But it's that same concept of markdown context files, connecting your tools and then creating skills. So that meta ads manager one that went pretty viral. I just planned it out with clawed. I want to have this open claw manage my meta ads. Help me write the agent.md file to tell you are my meta ads media buyer. You do these processes. And then I created skills. So I created a add creative skill. So you to go look in the drop box folder and create creatives. I created a copywriting skill. So I knew how to write good copy for the business. And I just built out. There's probably maybe 15 different skills. And then I would combine schedule tasks, which is cron jobs with skills. And the context files. And then just give all the tools it needed. And just following the same process as we just went through to build the executive assistant. I had an open claw meta ads media buyer, which was sick. I love it. And for the beginner, like, would you recommend people use an open claw or should they be using co-work or man-ass and some of the ones you showed? So great question. I would say that open claw is probably like one of the hardest to learn and set up of these harnesses. I would say co-work is probably the easiest. I think perplexity computer you did a video on. It's pretty simple too. Same with man-ass. But I would definitely learn and get comfortable using like claw code or one of these other ones before I started to play around with open claw. And I would also have all the processes built out in Claude code first. So for example, that executive assistant over the next two weeks, I might build out a bunch of skills like the Sebastian Refer skill, like a daily brief, meeting prep, etc. And then once I'm happy with how it's all functioning in Claude code, then I could look to migrate that into open claw, where it has that more autonomous nature to it. So that's kind of how I think about using open claw and those other harnesses. Yeah. Cool. All right. Anything else you want to cover? I mean, like really there's no right or wrong way to run these. Like that was the executive assistant. I've got one built out for all the other departments in my business and then other businesses I work on I have the same. And you can just kind of build out that structure with what worked for you. And you've got like one other thing to mention is global versus project level, which I'll just go over super quick. So like those skills, for example, you can add them at a global level, which means they apply to every single project you work in, whether it's the executive assistant, your head of marketing. And some skills you want globally, because you might use them in every chat. Like a truncate skill that I created, which just makes, whenever I want to make something shorter, it makes it shorter without compressing the sentences, but just removing sentences that don't need to be there. And that's something I want in every session. So I've got that in global, but you can have project level skills like that Sebastian refers skill. I would not want that with my marketing, header marketing, because it's just like clogs up the context and you don't need it there. So I would have that as a project level, for example. And you can have global skills versus project skills, global, cloud.md versus project, cloud.md. And same with MCPs, you know, global MCPs and project MCPs. That's probably the other concept to go over. But look, other than that, that's pretty much the entire agent's crash course. So it's just that loop running in the backend to complete your task and connecting in your tools, your context, and the LLM all in one place. And I would just say to work out what roles you want to start to build out an agent for. Go into glory, your favorite chat model and get it to help you build out those context files through an interview style process. Just say, ask me questions to build this out. I'd connect all the tools that you need and then start building out the skills through daily use. And then pretty soon you're going to have like pretty powerful agents built for every single aspect and department of your business. Right me. Thank you so much, on-cood links in the show notes in the description where you can go follow him get to know him a little bit better. And I appreciate you coming on dropping some sauce. Thank you, man. Thank you so much for having me on Greg. It's been a blast.