305 | Mastering AI Automation: Custom GPTs to Agents with Isar Meitis

Are your Custom GPTs living on borrowed time—or is this the perfect opportunity to build something even better?

Rumors that OpenAI may phase out Custom GPTs have sparked plenty of debate. But instead of focusing on what might disappear, this episode explores what comes next—and why business leaders should be paying attention now.

If you've invested time building AI automations, or you're just starting to explore how AI can streamline your business, you'll discover how Custom GPTs, Projects, Skills, and Agents fit together, where each one excels, and why the future belongs to portable, reusable AI workflows.

Rather than waiting for platform changes to force your hand, learn how to build AI systems that are flexible, scalable, and ready for what's next. This episode breaks down the concepts into practical examples you can apply immediately.

In this session, you'll discover:

• Why the rumors around Custom GPTs matter—and what they could mean for your business.
• The differences between Chats, Custom GPTs, Projects, Skills, and AI Agents.
• When Projects are a better choice than Custom GPTs.
• How Skills make AI automations reusable across multiple workflows.
• The building blocks behind effective AI automation: instructions, context, and memory.
• A practical framework for creating reliable AI workflows that deliver consistent results.
• How to transition from simple prompts to sophisticated AI-powered business processes.
• Real-world examples of proposal automation, reporting, and workflow orchestration.

About Leveraging AI

• The Ultimate AI Course for Business People: https://multiplai.ai/ai-course/
• YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
• Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
• Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events

If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Transcript

Speaker 0:03

Hello and welcome to the Leveraging AI podcast, the podcast that shares practical ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This is Isar Matis, your host, and I have a interesting episode for you today. There have been growing rumors in the past few months that OpenAI will cancel custom GPTs. Now, those of you who don't know what custom GPTs are, well, then you're not missing much. But if you did not use any custom GPTs so far, then you were missing maybe the most capable automation tool that AI gave us until the existence of Skills and agents. And many, many, many people, myself included, has multiple custom GPTs which they were regularly using to run multiple things in their business. I switched completely to skills and agents, but I know many people who have not. Now, I haven't seen any real information from OpenAI about custom GPTs going away, but if you're following what's happening on X or on Reddit, then you would see that there are many conversations talking about custom GPTs going away. And if you are somebody like me, who until not too long ago were running a lot of aspects of my business through custom GPTs, then it is something that you need to be aware of and start preparing for. Either way, whether you have built custom GPTs that you're using regularly or you don't and you wanna know how to build different kinds of automations with AI that can run more or less everything in your business, this would be a great episode for you to learn the basic concepts because they're the same across the board. So let's get started. I'm going to be sharing my screen, but those of you who are just listening and not watching this, I will explain everything that is on the screen. So if you are driving or walking your dog or doing the dishes or something like that, yoga, and you can't watch the screen, then that's perfectly fine. But if you do wanna watch the screen and you are able to do this now, you can either do this on Spotify. We're sharing the videos on Spotify as well as on our YouTube channel, and there's a link to the YouTube channel on the show notes. But let's get started. So there are several different levels on the ways you can work with AI, whether it's ChatGPT or any of the others. The very basic one is chat, right? You can just go to the chat window of Gemini or Claude or ChatGPT, et cetera, and just chat with it, and then you give it instructions or questions, and then it does what it needs to do. You are heavily involved in the process. Option number two is to create custom GPTs or projects. These are a consistent setup with a bunch of instructions and additional supporting information that becomes reusable and allows the AI to follow an existing set of instructions time and time again, without you having to give it any additional instructions. We're going to see examples shortly. The third level, which is the newest of the three, and if you want from an evolution perspective, we had custom GPTs first. That was something that OpenAI invented. Then we got projects from Anthropic, which were then copied by OpenAI to have projects inside of OpenAI. And then we received gems from Gemini, which is a very similar concept. And then we got skills and agents and so on. So skills was after, long after we had projects, but skills allows you to make it portable and reusable automation components, building blocks that you can use in multiple places. You can use skills in other platforms. So if you are using, as an example, ChatGPT in Excel, there is an extension for that, or ChatGPT in PowerPoint, there's an extension for that. You can take your skills with you. So if you have created skills for specific processes in financial analysis, you can bring them from ChatGPT into ChatGPT in Excel, and if you have a specific branding or process or style that you're using and you created skills for that, you can bring it into PowerPoint as well. And then on top of that, there are obviously agents where you can build agents. And to be fair, I'll do a little distinction on what agents are and what they're not, because there's a lot of chatter, and everybody calls everything agents right now when it's not really the case. So agents are employees, right? It's an entity that can do a full complete workflow and not just one simple specific task again and again. And they can do it end to end, and they can do it in a consistent way while taking into account the information they have and they do not have. I'm not going to cover this today. We're going to stop at skills because I wanna show you the basic building blocks that you can use. Uh, now, a skill is basically a very simple agent if you want, and that's why a lot of people call them the same. But an agent could be a lot more sophisticated, have access to more tools, be connected to more systems than just a simple skill. So these are the different options that we have in front of us today. Now, before we dive into the different options, I wanna kind of show you the process that I went through, the evolvement that I went through through this process, through the lens of writing proposals. So today, I use what some of you see on the screen, which is a very sophisticated proposal pipeline agent that has multiple skills, that is connected to multiple systems in my ecosystem, and that creates amazing proposals every time I finish a conversation, uh, that is relevant in which a proposal was requested or discussed. It does all of that automatically. It updates my CRM, it updates my Google Drive, it updates my outbox with a draft email while attaching the proposal. It does research in the back end on the customer. It does a lot of other great stuff. But this is not how I started. How I started was a custom GPT. So now let's look at what a custom GPT is. If I go to ChatGPT Initially, and this is one of the reasons why people thinking that custom GPTs are going away, custom GPTs lived on the left menu right above where the conversations are. And now you can see it's not there. Like, if you look for custom GPTs, you won't see them. So where are they? If you look on the left side menu, you have where you have New Chat and Search Chats and Library and so on, there's the three dots that says More. If you click on More, you will see, uh, GPTs, and if you click on that, you will see all your GPTs over there. So they made it less intuitive to get to or create, and if you don't know they exist, they just will disappear on you, and you won't even know that they're there. That gives you a hint that this is not their focus and not the focus of their product. Now, how does this work? You can see that this is called Multiply AI Training Education Proposal. That's the name of this custom GPT. All I have to do is I have to go to Add a File and drag in a file of a conversation I had with a potential client and once it loads the file, all I have to do is click Go. I won't type anything, I won't say anything. I literally just upload the transcript of a conversation that I had with a client that requested a proposal, and it is going to work, and it is going to write a great proposal. And you can see it's already started writing, and it has an introduction, and the objectives and why AI training is urgent, and why to work with Multiply, which is my company where I provide training and education, and training formats recommended for this particular company based on their needs, and so on and so forth, and pricing and the whole thing. It writes about a eight to 12-page proposal, well-structured, and I'm winning a lot of business, so these proposals actually work pretty well. Now, this is how I started. It worked very, very well. When projects started arriving, I switched it to projects, and we're gonna talk about later on why, but at first I wanna show you just so that you see the different options. So now if we go in this particular case, this is a Claude project, but inside the Claude project, what you can see is the same kind of thing. I uploaded the transcript. I asked, "Please create a proposal based on my brand guidelines," and it created the proposal. One thing you can see immediately inside the project that we're seeing right now is that it has my logo on top. It is using my brand colors. It is using my fonts. It is, from a structure perspective, has a much better structure from a document perspective. The benefit of doing this in the old school way without this is that I can edit the file. Here I cannot edit the file because it is a Claude artifact, which is not editable, which is really annoying, but that's the case. Uh, but it does come as a fully branded output, which now you can do with skills inside of custom GPTs as well. So what we have right now is we saw that y-- I can do this in a custom GPT. I can do the same exact thing in a project, whether a project in ChatGPT or a project in Claude or a gem in Gemini. All of them will do exactly the same thing, and they will write solid proposals And then like I said Then I switch to the full skill-based agentic pipeline that has all the other functions as well. So now I do not use the custom GPT or the projects, but it doesn't mean they're not good. It doesn't mean that I couldn't use them. It just saves me more time and does more things in an automated way, and so it saves me more time while creating a better output. And so this is why I'm doing this. But what I wanna show you today is different options so you can pick the one that is best for you. So first of all, let's talk about what they are. All of these things, custom GPTs, projects, and skills have the same exact DNA, and that same exact DNA is they have two components. They have a set of instructions that tell them what to do, and there is context, meaning knowledge files and additional information that the AI may need in order to create the output that it needs to create. So in the proposal example, this could be a proposal draft, or if you want a template that it can use. They could include a winning proposal or a few that has won me business to show it what good looks like. It could include descriptions of the services that I provide or the products that I sell, so the AI understands what it can pick from when it writes the proposal. It could include links to my website or testimonial websites or testimonials on LinkedIn that it can pull from in order to use when it's writing proposals. So all of these things are included as reference material, as context for each and every one of the three. It doesn't matter if it's a custom GPT, a project, or a skill. So they're very, very similar, and that's why when you think about, let's say you do have 20 or 30 custom GPTs, and you're afraid maybe OpenAI will take them away, converting them into projects or skills is a very small effort because they use exactly the same things Before we dive further, let's talk about instructions. AI reads instructions from multiple places every single time you talk to it, especially in these kind of places. So first of all, there is the system-wide, if you want, the account-wide always-on instructions. In Claude, you can change your claude.md, which is the file that Claude reads at the beginning of every single conversation, and you can tell it exactly how to work with you across the board in every single interaction it has with you. In ChatGPT, there is the custom instructions that live inside the settings on the bottom left corner, and you can go and tell OpenAI how to work with you, again, on every single conversation. Then inside a project, if you created a project, there are project-level instructions. Uh, same thing with a custom GPT. So you can give it instructions, and again, I'll show you in a minute where exactly you do this in the system itself. Then there in the Skill, there is Skill details, which is where it writes the instructions for the Skill, and there are two levels of memory in addition. One of them is the general memory, which remember things about you, and you probably noticed that ChatGPT or Claude already knows stuff about you. It knows where you work, it knows what you do, it knows your hobbies, it knows everything you talk to it about, but it's not at a very granular level. On the granular level, a, it is connected to the specific project. So every project is a little bubble of context that remember things about that particular project. And a project could be, well, a project, that kind of makes sense, but it also could be a specific client. So you can keep a specific client information in a project, have a separate project for every client, and have all your conversations about that client in that project, and the project is gonna remember more and more details about that particular client, about the people who work there, about proposal you sent them, and so on and so forth. Uh, and so there's these two levels of memory. There's the general memory and the project-specific memory. So all of these things are places that the AI will reach out to, depending on what kind of conversation you're having, in order to get additional information to be used Now, what I want you to remember that applies to each and every one of those things here is that AI is an intern. It's not just an intern, it is the best intern on the planet, right? It will do amazing things. But just like you won't bring an intern into your room and say, "Hey, I want you to write me this report," or, "I want you to create this proposal," or, "I want you to create the summary of one, two, and three," because it will probably fail. And if it will fail, you will know it is your fault because you didn't tell the intern exactly what to do, and the intern doesn't know you, he doesn't know the company, he does not know the industry. He or she is an intern. And AI is exactly the same thing. So the instructions you're gonna give it, regardless whether you give them in a custom GPT or a project or a skill, et cetera, are the SOP, the standard operating procedure on how to do a specific process. The knowledge files that you're gonna give it is the binder you're going to give the intern in order to know everything they need to know. So when you sit with the intern, you're gonna show them, "Okay. Here are the previous proposal we gave to this client. Here's information you can find about this client. Here's where on SharePoint you can find information about the services that we provide. Here on these Excel files, you can run the pricing and get to the..." Like, that's what you're going to do, and you need to do the same thing for the AI, and then it's going to do an amazing, amazing work for you every single time. Now, the project, if you want a ChatGPT or a Claude project, is a dedicated workstation, right? It's, it's the table that has the computer and all the files and everything you need in order to get access to the right information. And the skill, if you want, is a laminated SOP card. It's something you can take with you if you want a suitcase with everything you need, so you can go to other offices and do the work over there, and you'll see why I'm saying that once we talk more about what a skill is. But it is basically a project or a custom GPT that is fully portable and can work from everywhere without going specifically to that folder. So let's dive a little deeper to the first two options of a custom GPT versus a project. And again, if you'll see that they're very, very similar and that you can switch between the two very, very quickly. So if custom GPTs will go away, your first immediate line of defense is switching your custom GPTs into projects. And as you will see in a minute, there are benefits to actually using projects over custom GPTs. But both things will do roughly the same thing. They will get an input, they will run through a process, and will give you an output, and they will give you a consistent output every single time So we looked at the example before of the proposal, but what I wanna show you right now is in addition to showing you the outcome, I wanna show you how it actually works. So if I go back to the custom GPT on the top left corner, there is the name of the custom GPT that has a dropdown menu, and you can click on Edit. If you click on Edit GPT, which is exactly the same screen you're going to see if you create your own, which I'll show you in a minute how to do, you're going to see a similar screen. What you're going to see is on the left side, you're going to have the name, the description, the instructions, the inputs, the knowledge base, the actual graphs you attach, and some other stuff such as recommended model that you can pick and capabilities that you can choose. And if you really want, you can add code on the bottom. And there are conversation starters where you can add buttons to allow other people to understand how to use your custom GPT. But let's dive into the most important things. The name just gives it a name, tells it what it does. So in this particular case, it's Multiply AI Training and Education Proposal. The second thing is the description, writes proposals, blah, blah, blah. The description doesn't really matter. It's for you, and if you share it with others, for the people you share it with. The most important part is the instructions. The instructions is where it tells you what it's actually doing. And you can see you're an expert proposal writer. Your goal is to write clear, easy to read, and follow an attractive AI training proposals. And then it explains what the inputs are going to be, and it's going to explain exactly what the process is to take those inputs and analyze them, and then it tells it exactly what the output needs to be. So this is how it runs. It references two separate files in the proposal, in the instructions. One is the Multiply AI services brochure, where it can take information about what are the services and describe what's the value in doing or taking these services from us. And the other is a master template AI for training and education of AI. Why does it need that? Because then it knows how to draft it from a formatting and flow and structure perspective. And this outline is really a very full comprehensive outline of a proposal I will never write. It describes every single thing that I deliver, which nobody ever orders all of them, at least not all at once. And so What it tells it in the instructions, it says, "Each proposal that you write will include one or more of these training options. You need to only include the components that were discussed with the prospect based on the transcriptions and/or emails I will provide you." Right? So this is what it does. It gets access to a lot of different options, and it knows how to pick just the ones that are relevant based on the conversation we had. And that's it. That's all you need. That's the entire magic, and it will know how to write proposals if you wrote the instructions correctly, and we're gonna talk about this in a minute on how to do that. In a very similar way, if you go to the project, the project has the same thing. So if we look at the project level, you'll be able to see the files that are connected to it, as well as the... You can see brand, uh, guidelines and, uh, clarifying client proposal details, and so on. And you can see here that it has instructions. So inside the instructions, if I click on them, it is the same exact instructions that we've seen before. I literally copied and pasted the instructions. So if we go back to the previous section, you can see it looks-- it's, has the same exact thing, the overview, the knowledge base requirements, the core workflow, like all the different things, uh, that were there before are also here. And then you can attach different files to here, uh, that you can attach, so it can use as references for the proposal. So very similar process, different tools, but instructions and knowledge files, and then you can just drag in the transcript or emails or whatever you define for it as inputs, and it will know how to do the work By the way, to create new custom GPTs inside of ChatGPT, you go, as I mentioned before, to the three little dots where it says More, you click on GPTs, and on the top right corner there's a Create button. If you wanna create a project inside of ChatGPT, they live right above your chats, and you can see I have many, many, many of them. And in here, next to where it says Projects, if you put your mouse over it, there's a plus button that will allow you to create a new project. So on both platforms, it is relatively easy to know where to go. Again, other than custom GPTs that are now hidden under the More menu because I think OpenAI wants you to now build projects and/or agents or skills. So now let's continue with our flow. Another example, by the way, that we have here, which I'm not gonna dive into, but I will talk about in two seconds, is converting data, raw data, like really large Excel files. So if I open this, you will see that it has, multiple columns, like dozens of columns, and then thousands of rows in this particular file. And this could be a sales report, this could be financial analysis, this could be a scraping of customer, uh, pricing, like whatever the source data is, and it turns it into a very detailed report in the end that shows, in this particular case, sales, and it has the table of content, and it has an executive summary, and it has a business overview, and it has a lot of other stuff that shows graphs and charts and different information. By the way, all the information in this one is completely fake. It's based on a random generated, data, but the concept is perfect, right? You can see a very detailed... Again, those of you who are seeing, those of you who don't have to believe me. It is a well-branded, well-structured, well-organized report with graphs and charts and analysis of all the data from the source file. And this could be built either as a custom GPT or as a project or as a skill, so that doesn't really matter. So now between custom GPTs and projects, let's look at a quick comparison table. The purpose of both of them is roughly the same, right? Is to bo- be able to have a conversation. But the project has another benefit. The project is more of a workspace or, like I said, a context bubble where you can have free conversation. So while the custom GPT is built to do a recurring task and again and again and again, the project can do the same thing, but can also allow you to just have any open conversation inside the project while taking into account the memory of the project and the information that was attached to it, while a custom GPT doesn't do it, or at least doesn't do it as good. You can attach files to both of them. You can give instructions to both of them. The amount of data you can give into each and every one is roughly the same, depending on the specific plan you have. There are Three big differences. One is memory. As I mentioned, custom GPTs, every single chat is a new chat. It doesn't know anything about what happened in the previous chats with that custom GPT. Inside the project, there is a project memory where it's going to learn more and more information as you have more conversations inside the project. The other benefit of running the conversations inside a project is the conversations live inside the project, where you can see each conversation that happened inside the project, one under the other, while in a custom GPT, the conversations appear in the regular conversations of ChatGPT, which means they're gonna be hidden between the other 10,000 conversations that you're having. So if you wanna be able to see the same report that you created last week, it will be extremely easy to do in the projects because it's just gonna show there as the previous line item, and in the ChatGPT, you'll have to do search and filter and find the right one and check it, and so on. So that's another benefit. The only real benefit of custom GPTs is that you can add code at the bottom of the custom GPT, which means you can connect it to an API of external tools. This is something you cannot do in projects right now. That being said, with Skills, you can now do similar things. And that being said, I probably have over 50 custom GPTs that I created and used, and I created code in two of them. So it's not something that I've done very commonly, and I doubt that a lot of people did. While it is a benefit, it is not a huge benefit. Once I understood the benefits of projects, I stopped using custom GPTs completely. I converted the main ones into projects, not all of them. And then I said again, I converted more or less everything into Skills and Agents How do I create all of them? Whether I'm creating a custom GPT, a project, and/or a skill, I'm always creating them starting with a regular chat. I'm going into a regular chat, either in Claude or in ChatGPT. When I do this in Claude, I do this in Claude Cowork. When I do this in ChatGPT, it doesn't matter. You can do this in the regular ChatGPT and/or in Codex, and you can explain what you wanna do. You can give it the files, and you just work through the process. You give it the data. I said, "Okay, let's clean the data. I want you to understand what's in the data. I'm gonna give you this kind of data every single time." Now you iterate through the process. You explain exactly how to get to the outcome you wanna get to, whether the final report or the proposal or the analysis or whatever it is that you're trying to do. Once you get to that outcome, you can ask the AI, again, whether ChatGPT or Claude or Gemini or any other, and say, "I want you to turn everything we did right now, just the stuff that worked, not the stuff that didn't work, into a X." This could be instructions for a custom GPT, instructions for a project, or a skill, and it will know how to do that. If it builds a skill for you, it will package it and will give you an Install button as soon as it's done. If it is a custom GPT or a project, you will have to then take the instructions and paste them into a custom GPT you create or a project that you create. What you should tell the AI is which files you're gonna give the custom GPT or project as references, so it can use it in the instructions that it's creating. So let's look at a quick example. In this example, I was building a custom GPT that writes questions for sessions of the courses that I teach. Uh, those of you who do not know yet because you're new to the show, I teach AI courses. I either teach them online to people who can join or do private workshops for companies. Either way, I need tests, and I need ways to check that people are learning the process. And instead of doing this manually, I'm using a custom GPT to do this. Again, not anymore, but this is how I started. So what I started here is I said, "Hey, I need your help in creating a new custom GPT. I'm going to explain to you what the needs are, and I'm going to give you some examples, and then I will need your help in creating the instructions for the custom GPT. Is that okay?" And it said, "Absolutely. That sounds great," blah, blah, blah. And I said, Okay, so I'm creating a course about AI, and what I have is the transcript of all the different sessions What I will give you is examples of the transcripts of one session and the questions that were written for it by humans, so you get an idea of what kind of questions we are looking for. And then I would like you to do is I would like you to explain these questions in a way that the GPT will be able to create questions when I give it new transcripts, and so on and so forth. You understand the point. We went back and forth several times. You can see if I scroll down, those of you who are watching, it's a very, very long conversation. It created the first set of instructions. It didn't work well. We tested it again. We iterated, we compared it to other questions. We basically went in several cycles of fixes, and you can see it here about iterating. It most likely won't work perfectly the first time. You iterate several times, and once you're done, you ask it to create the instructions, and then you can create the GPT or project or skill. Then you still have to test it. There is a pro tip here about reusing the code. What does that mean? If you're doing financial analysis, as an example, every time you're uploading an Excel to ChatGPT or Claude, what it's actually doing it is writing Python code. By clicking inside the thinking little thing, uh, when it's blinking, when it's doing its work, or with retrospect, you go back and click and expand these sections where it was thinking or creating or doing something. Not where it's providing you the answer, but the section where it was thinking. It's usually in a more, uh, grayed out, smaller font. You click on that, you can see the code. So if the AI wrote code that actually did the process perfectly, you do not want it recreating that code every time you run the process because of two reasons. One, it's gonna cost you tokens to write the code, so it's gonna cost you more money. Two, one in every X number of times, let's say one in twenty, it will not write the code perfectly and you're gonna get the wrong outcome. So what you can do is you can take that snippet of code and tell AI inside the instructions to use that code for that step in the process. This way, it just uses the code that is there. It doesn't have to recreate it from scratch, and it will run consistently every single time. so now we understand what is a custom GPT and what is a project. Now let's talk about skills. So what is a skill? A skill is basically the same kind of thing, only it's portable, and the fact that it's portable make it a lot more reusable in several different ways. You don't have to go to the project or the custom GPT to do the work. You can just tell AI to do something, and it knows which skills it can pull. It can mix and match skills together to do much more sophisticated processes, and it's just much more powerful because of that, and they are the building blocks for more advanced agents afterwards. So there are a lot of benefits for skills. But a skill is basically the same thing we talked about before. It is a set of instructions and knowledge files just packaged in a way that the AI knows how to pull it when it needs to pull it So how does it know? How does AI, whether ChatGPT or Claude or any of the other tools that are using skills today, and today it's more or less everything, including coding platforms and so on. The way they know how to use skills is because every single skill has a short one-paragraph description in the beginning that explains what it does, and this is how it's formatted every single time, and you don't have to know about it, and you don't have to care about it because you don't do this. The AI that creates the skill will create that first paragraph for you. But every time you start a conversation, any conversation with any AI, it is going to upload all the descriptions, that first paragraph of all the skill it has. So in this case, you can see there's a skill called Proposal Coordinator, and then there's a description, Master Orchestrator for the Multiply Proposal Pipeline, which I showed you earlier the screenshot for, with all the different skills in it and all the different components and the connection to my tools and all the other stuff that it does. So this is the orchestrator that manages the entire process. By the way, talking about the courses that I teach, we have been teaching the multi-agent orchestration course for the past few months, both to companies as private workshops as well as open to the public, and it is extremely successful, and people and organizations are doing absolutely magical things immediately after the course. So if you're interested in learning how to combine multiple skills together to create processes like this one, but like any other process, literally any knowledge work that you have in your company right now can be automated with this exact process. Just be adapting it to the needs that you have. So if you wanna learn that, we are currently selling the last few seats of our August cohort. We sold May, June, and July already. August is the currently open one, but it only has a few seats, if any. So if you wanna do that before September, this would be a great time to click on the link in the show notes and jump straight there, or you will have to take the course in September. If you are in a leadership position in an organization and you wanna do this privately to your team, please reach out to me on LinkedIn or via email. There's a link to book time with me in the show notes, and I can explain to you exactly what are the pros and cons and, what is the service and so on. But back to this. In this particular case, this is an orchestrator skill that manages the entire process, and if I would have opened this in whatever tool that I'm doing, I would have gotten the entire instructions, which are long, detailed, and complicated. But because it knows how to read this, then if I'm going to say something, "I need your help in writing a proposal," or if I will say, "I need to write a quote for this and that client," or if I will say, run their proposal pipeline, all of these things it will understand in a regular chat anywhere in Claude. It will know how to pull the right skills and use them, and this skill will know how to call the other skills so I don't have to package them all together, and so on. So it understands from the context which skills to use based on that initial paragraph that it reads from. So because you can build multiple skills and they can call one another, in addition to writing proposals, you can do really cool things. In this particular example, this is a finance team example. There's a really big, large file that I showed you before. It's again a fake file that I use for different examples, but it's a really big, large fake file with multiple data points and multiple rows and columns. And if you are in a financial team, and this is a financial report, you need at the end of each week, each month, each quarter, whatever frequency, create multiple reports, like a trend report, a variance report, a regional report, a labor, a WBS breakdown report, a movers and changers report up and down, multiple reports that you need to create. And it takes a lot of time, and it generates more Excel files. Well, what you can do is you can create multiple skills that will create different outcomes whenever you want. So in this particular case, it's something I've done for a real client. Instead of creating just the Excel reports that they were creating previously, we also created a skill that creates a document, like a detailed report with analysis and graphs and charts and explanations like I showed you before. We created a executive summary of that report in a PowerPoint, and we created a dashboard that shows live data that you can filter and change and select and go through different aspects of the data in a interactive dashboard. Each and every one of them was a separate skill. They're all being fed from the same data source. So this is the benefit of using skills, and all you have to do is tell it what you want, and it will know how to pick the right skills or run all of them because there's an orchestrator that knows how to create all of them all at once. So what are these skills' superpowers? First of all, they're more flexible. They can be used anytime. You don't need to open the project or the custom GPT in order to do them. They can be combined with other skills to create agents or to create multi-skill processes with one skill calling other skills, and so on. And you can run it in other apps, like I said in the beginning. You can run skills inside of Claude and skills inside of ChatGPT, extensions inside of Excel as an example. So if you created a skill that knows how to do a specific financial analysis, you can now take it into ChatGPT in Excel and do that in Excel itself without ever leaving it and continue to work the way you're used to while enjoying those skills. This is something you cannot do with projects or with custom GPTs So we talked about three out of, if you want, the five things that you can create with AI today that can help you automate your work. The first one is just a regular prompt in a chat, which we all know and like and use a lot. Uh, to be fair, I switched completely to the agentic world. The amount of times I use regular chat is negligible. I'm not saying it's not helpful. I'm just saying using a more agentic environment like Claude Cowork or like ChatGPT Codex, or now Copilot Cowork, which is basically a copy of the Claude Cowork just connected into the Microsoft universe, is a lot more powerful. You can create projects which are reusable automations. Again, you can create custom GPTs. I do not recommend doing this anymore. Just create projects and that's it, because all the agentic universe are gonna be built on top of that. And then it can be the skills that are more flexible, can be combined with others, and so on. The two layers above that is creating entire workflows, so agents that will do more sophisticated things like the one I showed you in the beginning that connects to many components in my tech stack, including my CRM and including my, email platform and my marketing platform and my data behind the scenes. For my case, it's Google Drive, but it could be Notion or it could be, Microsoft or any other kind of solution where you save files. And you can create entire applications, meaning use vibe coding in order to connect a lot of these things together, which in many cases are not necessary Something to think about though before you develop any of these things is if you're running on a personal account, then these things will most likely force you, as you build more and more of them, to go into the higher levels of licensing. So you won't be able to use-- I mean, you will be able to use the $20 a month, tool, but not to do a lot of these things in parallel. So you will be forced to upgrade to the next level, either 50 or 100 or $200, depending on the platform and how aggressively you use these tools. Uh, if you are on an enterprise account, many of these things charge by tokens or credits, and every company does it differently, and they do it confusing on purpose. But in general, every time you're using agentic capabilities, it is going to cost you and/or your company, if it's not you paying, additional money, so you need to be aware of that, and you need to be aware and start learning how to reduce the amount of tokens these tools use in order to reduce the cost that is going to be associated with using these more advanced tools. There are many, many different ways which we're not going to jump into. The easiest one to save money is to go to not the frontier model. Models from six months ago and even a year ago are good enough to do most of the tasks you're doing today, and so, you don't necessarily have to use Fable 5 or GPT 5.5 or whatever the case may be. You can use a model from six months ago, pay significantly less, and still get a solid output. That's it for today. I hope you found this helpful. Again, I'm not sure OpenAI is going to sunset custom GPTs. It definitely seems this way. The rumor mill is pushing hard in that direction, and the fact that they've hidden it very, very well, and the fact that the API that did this through an API, the Assistants API, has been sunset, announced to be sunset earlier this year. Most people deserted it already, and it's gonna be completely stopped by August of this year. So, but there are, again, great other options in the shape of projects and Skills and agents. And as I mentioned, if you want to learn how to do this at Skill and combine these things together with your tech stack to build any kind of automation for any kind of knowledge work in your company, don't hesitate and come join our courses or our private workshops. But that's it for now. Have a great rest of your week, and we'll see you again this weekend