Master 95% of Claude Code in 36 Mins (as a beginner)

Cloud Code has been allowing me to build things that used to take me hours in just minutes. So that's exactly what I'm going to be teaching you guys today. Even if you don't know how to code and even if you've never touched an IDE before. IDE stands for integrated development environment. But if you didn't know that, it's still completely fine. It's crazy how fast the technology is evolving every single day. What used to take people this long with manual code was significantly reduced when editing came out because we could drag and drop nodes and build workflows that way. And now that has once again been significantly reduced with the release of things like cloud code and anti-gravity. Now I'm not out here saying that NAND is dead or that Cloud Code completely replaces NADN. They're slightly different. But I am going to show you how easy it is to build automations with Claude Code today. If you've never touched Claude Code before or even watched a video about it, you're in the right spot because my job is to make confusing things as simple as possible. So in today's agenda, I'm going to be going over the interface, what do you need to know because there's a lot of stuff, but I'm just going to tell you what's actually important to understand. We're going to go over the framework that we use to actually build automations. I'm going to talk about planning and the importance of clear communication. We're going to talk a little bit about the superpowers that you can give Cloud Code like MCP servers and skills. We're going to talk about testing and how you actually optimize your workflow and then talk about deployment, which means actually kind of turning it on or pushing it into production. And I'm not just going to be talking throughout all of this. I'm actually going to build a full workflow in front of you guys and deploy it by the end. So after this video, you'll have everything that you need to go build your first automation in Cloud Code. and you're going to see how easy it really is. All right, so we're just going to jump right into it. This is the interface. We're going to be using Visual Studio Code, which has been around for a long time. And if you go to Google and type in VS Code, you can just go ahead and go to this link and just download it. It's free to download. And then in here is where we're going to actually be using Claude Code. So this is what it should look like. What we're seeing here is just kind of the welcome page. You can see we can open new files, new folders, we can do some of these walkthroughs. But what I'm going to do here is I'm going to go over to this lefth hand side and click on extensions and just type in claude code. And then you'll see right here that this extension pops up which lets us use claude code inside of VS Code. So what you're going to do here is go ahead and install it. You could also do this in anti-gravity or in cursor or somewhere else or you could even use the claude code kind of app by itself and install that locally. But wherever you choose to use it, you're going to log in and then we'll get started. I'm just using VS Code in today's tutorial. It'll prompt you to sign in with your anthropic account and then you'll be all set. Now, in order to access Claude Code, you do have to be on a paid plan of Claude. As you can see, if you're on the 17 bucks a month plan with Pro, you get Claude Code. Um, but you will probably find pretty quick that you'll want to upgrade to Max or the the higher version of Max because you'll be doing a lot of automations in there and you don't want to hit your limit and then have to upgrade. But you could always start on Pro and then upgrade later. So once we got that extension installed, I'm just going to go ahead and click on this button in the top right, which looks like the Enthropic logo. And I'm just going to open up Cloud Code. I'm going to close out of this window. And now you can see that we have basically a chat GBT like looking interface where we have Claude Code right here. So on the left hand side, instead of looking at the extensions marketplace, we're going to click on this button up at the top that says explorer. And what it tells us right here is that you have not yet opened a folder. So it prompts you to open a folder. So before we go ahead and open one up, let's talk about why and what we're looking at. So this is kind of the environment that we're looking at right now. We've got our files on the lefth hand side and this is where we're going to actually build our project, our system prompts, our workflows, our tools, and then on the right hand side we have the agent. So this is where we talk to claude code. We have it help us with a plan. It asks us questions and then it actually executes on those actions. So lefth hand side is files, right hand side is the agent. It's going to be super simple and I'm going to show you how we can keep our file structure really clean so it doesn't get overwhelming and confusing on this lefth hand side over here. So whenever you're in cloud code, you have to be working inside a project and that's why it prompts you to open up a folder. So what I'm going to do is in my documents, I've got a folder called aentic workflows and I've got a bunch of ones that I've been playing around and testing with. But I'm just going to go ahead and open up a new blank folder for today's video. I'm going to go ahead and call this one YouTube analysis. And then I've created that folder. So now when I go back into cloud code, I'm just going to open up that folder. Cool. So I just opened it up and it changed what we were looking at over here. On the right hand side, we've got like VS Code's agent. So I'm not going to worry about that and just close out of that. And then on the left hand side, you can see we're now in the YouTube analysis folder, but there's nothing in there yet. So once again, I'm just going to reopen Cloud Code, close out of this one. You can see you can have multiple different files open on the right hand side. So if you wanted to have like five cloud code agents running or you wanted to look at five different files or system prompts, you could do so. But right now, we're just going to keep it open to one. So the first thing that we need to do is we need to give Claude Code a system prompt for this project. And that's the first thing that you should do whenever you open up a new project in Claude Code. And we call this system prompt a claude.md file MD just standing for markdown. So I'll show you guys that in a sec. But without a system prompt, it's like we have an NN AI agent like an expert copywriter and we don't actually give it a system prompt in here. So without a system prompt, it wouldn't actually really be an expert copywriter. It would be super generic. It wouldn't understand the tools it has, the product that we're trying to sell, or where the documents live and what those look like. So that leads me into the next part of the video, which is talking about the framework, which is how we actually build these automations. So here's a really, really simple visualization of what we're actually doing here. We've got our agent, which is Claude Code, and the agent is going to help us build workflows. Workflows meaning processes, SOPs, instructions of what we actually want to do. And inside those workflows, we're going to give it access to tools. And tools means actually executing actions. So send email would be a tool. Research a YouTube channel would be a tool. So it's really similar to the way that we have workflows and tools in NN. Here you can see is an NIN workflow for a daily news summary. And inside the workflow, which is a specific set of instructions in a specific order, so it's a deterministic process. We have different tools. We've got a tool here for Tavali to do research. We've got a tool here for an AI agent to do the newsletter writing. And we've got a tool at the end to send a Gmail message. So hopefully that all makes sense. It's going to be really simple. We're going to have a folder for workflows. And in there will be all of our processes. We're going to have a folder for tools. And in there will be all of the actual things that it can execute. And then the agent basically helps us set up those tool files and workflow files and then execute those actions. So I'm going to do is drag in this claude file. And you can see it's a claude.md. This could be called agents.mmd, gemini.mmd, whatever you want. In this case, we're using cloud code, so I'm calling it claude.md. But let me go ahead and expand this one and let's briefly read through it so you understand exactly what I just talked about with the workflows, agents, and tools. So this is the agent instructions for this specific project. You're working inside of the WAT framework, which stands for workflows, agents, tools. This is a three-layer framework and it basically separates concerns so that the probabistic AI handles reasoning while deterministic code actually handles the execution and that is what makes these systems actually reliable. So like I said layer one is the workflows the instructions. So these are markdown SOPs stored in the workflows folder which will be created in a sec. Each workflow defines the objective the required inputs which tools to use expected outputs and how to handle edge cases. It's written in completely plain language the same way that you brief someone on your team. And by the way, when I say markdown, it basically just means this structure. This is a markdown file right here where we have like headers and subheaders and bold font and things like that. Layer two is the agent. So this is the actual cloud code agent that we talk to. This is your role. You're responsible for the coordination between workflows and tools. You read the relevant workflow. You run tools in the correct sequence and you handle failures. You ask clarification questions when needed. Layer three, we have the tools. And these are actually going to be Python files. So right here you can see cloud is a markdown file. So it's claw.md. We said that our workflows were going to be markdown files. So it will be like um scrape website.md. But then in the tools which we will have another folder for over here we're going to have tools that are going to be py. So a python file. So in this case we can see there's an example tool called scrape single site. py which would be a python script that would execute an action. These can be API calls, data transformations, file operations, database queries. And a lot of times in these tools, we'll need an API key, but we're not going to actually store them in the tool code logic itself because if that got exported or we push that onto the web, then our API keys would be exposed. So, we're going to handle secrets by storing them inv files. You don't have to understand exactly what that means or how that works right now. We'll show you. So, then we talk a little bit about like why this matters, how to operate. So, you look for tools first. You learn and adapt when things fail because these agentic workflows are basically self-healing. So, as we're going through and building this workflow, you will see that it says, "Okay, I ran into an error here. Let me figure out what happened and let me fix it." So, fix the script and retest document what you learned. So, if it ran into an error and it fixed it, it will go ahead and change the workflow file so it doesn't run into that error again. So, an example could be you get rate limited on an API, you dig into the doc, so you do research, you discover a batch endpoint, you refactor the tool to use it, you verify that that works, and then you update the workflow so that it never happens again. This is once again where we talk about that self-improvement loop. We talk about the file structure and you can see that it's going to create this for us. And basically the bottom line is that you sit between what I want which are workflows and what actually gets done which are the tools. Your job is to read instructions, make smart decisions, call the right tools and keep improving the system as you go. So I know we skimmed through this kind of fast but you guys will get access to this exact same system prompt. I'll leave it in my free school community. The link for that will be down in the description. That way you can just go ahead and grab this, paste it in, and then when you want to follow along and build some workflows in Cloud Code, you've got this right here for you. So now what we need to do is just set up our environment with the different folders. So I'm going to talk to cloud code and just say initialize this project based on the claw.md file. So I'll go ahead and shoot that off. And when we talk to claude, what it does is it basically just tells us exactly what it's doing and what it's thinking. What you'll notice right here is that I'm on a mode called bypass permissions. And you might not see this initially. You'll see ask before edits, edit automatically, and plan mode. But it is really helpful to be able to turn on bypass permissions. So the way that you do that is you go to the bottom left to settings. You're going to go to settings once again. You'll type in cloud code and then you're just going to turn on this option that says allow bypass permissions mode. And that's what allows you to do that so that you can let your agent run and you don't have to approve every step. Now, as this is running, what you'll notice is on the lefth hand side, we're seeing some files and folders pop up. So, we've got a temporary folder, which just means anything that it needs to store and then like delete later, just temporarily, it can do so in there just to keep everything clean. We've got our tools folder, we've got our workflows folder, and then we have av and getit ignore. So this is going to help us just basically keep our project clean, but also the agent knows exactly where everything is. Cool. So the project is now initialized using our WAT framework and it showed us what it created. So now let's move on to section three of the video where we're going to be talking about planning and communicating with our agent. So what I'm going to do is I'm going to clear out this conversation. If I wanted to access past conversations, I could do so up here. I'm going to go to plan mode. And this is really important. Whenever you're doing something that actually involves like creating something, you need to describe the goal and you need to be able to describe it super super clearly. And it's not just the goal, you need to also describe the features that you want. And if you were to just describe something and then chuck clogged code at it and you would do bypass permissions, you probably wouldn't get a great output. So, what you always want to do when you're creating an idea is you want to go on plan mode because what you're going to see is when I'm on plan mode, it thinks extra hard and it looks at everything in the folder and it's going to ask me tons of questions that I might not have thought of, which is really, really helpful because it gets a really, really good understanding of what we want and it brainstorms options and then it actually will do it after it's confident. So, let's explain the workflow that we want to build today. Hey Claude, I need your help building an automation. I want this automation to basically scrape tons of YouTube videos and YouTube channels in my niche, which is AI and AI automation. I want to get insights about what videos are trending, what's working well, and kind of what the AI space is feeling like so that I can create more content that people want to see and that will be beneficial for them. I need your help understanding how we can actually get this data. So, look into different APIs or MCP servers. Also, let me know if there's any skills that would be helpful because after you've done this research, what I want you to do is I want you to create a slide deck for me. So, I want to get an actual deliverable that will be sent to my email using Gmail and it should be a really nice professionallook slide deck with charts and images and all of these different graphics so that I can understand what's going on in the industry. So, that's what I've got. Let me know if you have any questions or if you have any recommendations for things that I haven't thought of about this automation system. Cool. So that was my little brain dump and it's going to come back and ask me a ton of questions which is just going to help make this project a lot lot better. And so I know a lot of you guys might be looking at this and it seems overwhelming and confusing and I agree like when I first wanted to dive into claude code I watched some YouTube videos and I just it didn't click. The only way it's truly going to click is if you get in here and you do it yourself because once you send off these messages just read everything it's doing. Read every single line and you'll start to understand the way that these models think and what they try to do. And that's truly the best way. So after this video, restart it from the beginning, open up Cloud Code, and just kind of follow along with what I'm doing, and it will all start to click. I promise. And by the way, you can see that as it's making this plan for us, it's doing research. So it's not just thinking, it's also searching the web to find out how we can scrape the YouTube analytics and how we can use MCP servers and things like that. Okay, so we got some questions now from Claude. It says, "What specific YouTube channels do you want to track? Should I discover top AI automation channels automatically or do you have a list? Let's just go with autod discover top channels. Frequency is how often should this report be sent? I'm going to go ahead and do weekly. Then it asks us if we want to track all this data in sheets. Yes, absolutely. Let's do that. And then for delivery, it says what email address should the reports be sent to? And I'm going to go ahead and say send to my Gmail. So, I shut off those answers and now it's going to keep updating the plan. All right. So, the plan is finished. The objective is to build an automated system that scrapes YouTube data for the AI niche. It analyzes trends and gets performance metrics and then generates a professional slide deck with charts and visualizations and sends that to me over Gmail. We've got the workflow which is YouTube weekly report. We've got the agent layer. We've got different tools. It's going to build out these seven different Python tools that it mentioned. So fetching YouTube data, analyzing YouTube data, generating charts, generating slides, sending the email report, exporting to sheets, and discovering channels. And now it needs to actually create this workflow. So, we could obviously read through all of this and we could give it some feedback if we wanted to, but I'm just going to go ahead and accept these because I want to see how well it did with just one iteration of our plan, which took me a few minutes. So, you can see what it does is it starts a to-do list. So, it's basically just going to knock off one of these at a time. And that's really nice because it helps the agent stay on track, but it also means that you could go to your other monitor here and work on something else and just kind of keep peeking in on it and checking on the to-do list to see how much is left to run. Okay, so the to-do list is done. The workflows and tools have been built. So, here's where we're at. We've got our seven tools have been created. So, if I open up the tools folder, we should see we now have these seven Python files. And each of these, like I said, are actual Python code that will execute some sort of action. So, those have been built. We've also got the workflow. So, this is our markdown file, YouTube weekly report, which is an actual process. So, I'm not going to read this whole thing, but it has the actual steps that we would be doing here. So, now it says to get started, we have a few dependencies. So, the first one is we need to install something. The second one is to add a YouTube API key. The third one is to set up Google OOTH for Gmail and sheets. And then the fourth one is just to run the actual workflow. So a lot of times when cloud code's done and it has some action items, it actually just tells you to do some stuff that it could do itself. So right now we would obviously have to go get our YouTube API key and then we could just give it to it and say, "Hey, you go update the I don't want to touch that. You just go do it." But first, what it's doing is it's asking us to do this. So, we could obviously just install this right now, or I could just say, can you please go ahead and install the dependencies? I'll go grab my YouTube API key. Cool. So, it went ahead and installed that stuff just like I told it to. And now it's asking for a YouTube API key. So, instead of just adding it to the file, I'm just going to drop it in right here. And then the one thing I will have to go do manually is step three. So, I'll have to enable the YouTube data API and Gmail and Google Sheets and then create the credentials and just drag in the JSON file, which I will do that in a sec. And here's another thing I'm doing with my API key. It should only be added to thev file. It shouldn't be listed in the workflows or the tools. Okay, so I added everything that I needed to. And if you're confused about how to do that, just say, "Hey, where do I go? What do I click on? How do I do that?" And it'll walk you through. And now what it's doing is because it has all our credentials, it's actually just testing out if the things work. So you can see the YouTube API is working. Now, let's run the full data collection pipeline. So it's basically just testing that the flow works and then we'll give it a full run. But we can see that it just ran the full pipeline. So that was our first initial test. It found 30 channels. It fetched 187 videos. It generated analysis. It made six charts. It built a nine slide PowerPoint deck for us, exported it to Sheets, and then it emailed us the report. So, let's go take a look at all that. Okay, so here's the email that I got. AI automation YouTube analytics. So, the weekly report for Jan 20, we got 30 channels tracked, 187 videos. We have some top videos from the week. We have recommendations. And then we also have our PowerPoint right here, which we can see. We have similar information. We've got median views, median engagement, trending topics. We've got top performing videos. So, we have this laid out by title and by views. We've got top channels by subscribers. Unfortunately, I do not see my name up there. So, please hit the subscribe button. We've got engagement analytics. We've got trending topics, by keywords in the AI automation, posting patterns, and then we have some recommendations to kind of close us off here. So, keep in mind this is not perfect, and we obviously would want to come back and make this a little bit more tailored for us, but this was one prompt. Cloud code asks us questions and then I basically just sat down and then I came back over here when it was done and this is what we have ready for us. What we also see is that we got this exported to a Google sheet. So if I click on this, remember that we didn't create this sheet. We didn't create these different tabs or the actual like schema of this. But we've got three tabs. The first one is channel stats. So this pulled channel stats from today's date which is January 20th. We have the channel IDs. We have the actual channel names. And then we've got subscribers, total views, and video count. We can see nice that Nate Herk AI automation did make it in this scrape. We've also got top videos. So once again, this was ran based on today's analytics. We got the video ID. We've got the title of the videos. We've got the channel, the views, the likes, the comments, the engagement rate, which is pretty cool. And also how old the videos are. So we can see that we're getting real accurate like what's trending right now. And then we get a weekly summary. So this is supposed to run every single week. We can see the day that it ran, the channels it tracked, the videos it analyzed, the median views, the median engagement score, and the top keyword in top keyword 2, which actually, it's funnily enough, spells out claude code, which is why you're seeing this video right now. Okay, so let's recap what we've done. We have familiarized with the interface. We have built out the actual structure of our project using a claw.md file, which is like a system prompt. Now, we have our workflows. We have our tools and we have actually gone through the whole planning stage with claude code to build out the initial you know workflow automation that we need. So what comes next now is we want to talk about a few other things. We want to talk about superpowers. So MCPS and skills and then we're going to test it a little bit more and then we're going to actually deploy the automation live. So to start off with superpowers MCP servers. So I'm not going to dive super super deep into MCP servers in this video but I did want to bring it up. So, if you remember in plan mode, I basically said, "Hey, I want to scrape YouTube data. Can you just go figure out if I should use an MCB server or like an API?" And it ended up finding out that the YouTube API was going to work better. So, that's why we did it in this workflow. But essentially, just think of an MCP server as an app store. So, Gmail has an MCP server, Calendar has an MCP server, lots of these services do. And this is like one of the most common visualizations because it's like a universal micro USB port because instead of having to go to calendar's API and have one different API request to create an event, one different one to update event, one different one to delete an event, all we have to do is connect once to the whole server and then the agent can figure out how to go use different endpoints and parameters. It just simplifies the whole process. Now, what I did want to talk about a little bit more was the idea of claude skills because this is a little bit newer. So essentially skills are instructions or resources that claude can load in dynamically. And that's kind of the key piece here is that instead of just reading it every time in its system prompt, it basically understands what is the request. Let me go look at all the skills I have access to. If one of them is relevant, I'll pick that one. I'll read it all and then I'll take action. And this process basically just improves Claude's consistency, speed, and performance. And also saves you tokens. Like I said, when you ask Claude to do something, it reviews the available skills. It loads in only the relevant ones and then it applies those instructions. So, we're going to go ahead and try to implement a skill into this workflow and I'll actually show you what the skill document entails. So, then it will all start to make a little bit more sense. But before we do that, I did want to real quick cover the difference between skills and projects and skills in MCP. So, the first one is about projects. You're in a project and basically what we have is access to whatever is in here. So, it's kind of static documents and background information. And a lot of times these skills are installed globally. So what you'll notice actually in our project is that we don't have any skills in this project. Normally there will be like a thing that will be like agents and then you drill down in that folder and you'll see like agent skills or claude skills. And that's more installed on the global level. And that's actually really good because what that means is if I closed out of this project and I opened up a different one, I would still have access to all the same skills that I've already installed. So you can see right here that I just asked Claude Code, "What skills do you and it came back and showed that it has a front-end design. It has NN skills and those are the only eight that it actually has even though we don't see them in this specific project. Now we have skills versus MCP and these are also very different. MCP is basically to get data and take action. So like I said if you want to connect Claude to something like Gmail to read emails or to send emails but skills are more like knowledge custom instructions. So if you ever find yourself constantly repeating something to your cloud code agent, then maybe that's a good sign to put it either in the claw.mmd file or create your own custom skill for it. So like the example of the front-end design, if you wanted to use cloud code to build yourself a landing page or a website, using the front-end design significantly improves its ability to actually design things. So, what we're going to be doing in this example now is I want to use a skill and I'm going to be looking at this cloud code templates website which has a bunch of agents and commands and MCP servers and skills and hooks and I'm going to be looking for one that helps us create like better looking PDFs. I'll also leave a link to this in the description of the video. So, I went ahead and searched for design and you can see there's a skill right here called canvas design. And if I view details here, it says create beautiful visual art inputs using design philosophy. So, we're going to go ahead and try this one out. I've never used it before. We'll see how it works. But this is actually like the code of the skill itself. And you can see it basically is just natural language instructions. So, it's just a custom prompt that someone built or you built yourself. And now I can load this into cloud code. So, when we have it design a PDF, it can use this and it will probably just come out a lot better because it's prompted. So, we've got installation right here where we can use this code. So, what I would try is just copying this, going into VS Code. I'm going to go ahead and open up a, you know, kind of clear the conversation and just paste that in and see what happens if I drop that in there. Okay, so I dropped it in and then it actually ran the command in our terminal to install it. And it says that it's been installed and we have skill.md for the instructions for the skill. And then we've also got a bunch of fonts. And what it did is it actually created a new folder here called Claude. And then we do have skills right here. So you can see that it put it in this project. So now I'm a little confused because I don't know, okay, we have a skill here, but we also have skills globally. So I would literally just say it looks like you created this skill in this project. Is this going to be installed globally or will it only be accessible through this project? So right now it basically says yeah this was installed just locally in this project and that's fine. And if you wanted it to be global instead you would just say okay actually just make that global and then it would. So anyways going to clear out this conversation one more time. I'm going to go back into plan mode and I'm going to give it a prompt. And actually one more thing before I prompt it. I'm going to drag in the AI Automation Society Plus logo just over here on the left hand side. And you can see it's right here and the file pops up, right? So, what I'm going to do is prompt it, but I want it to actually have this logo on all of the PDFs that it generates. Hey, Claude. So, I just gave you a skill for canvas design. And instead of outputting a PowerPoint presentation, I want you to now take the same research when you do your analysis from YouTube videos, but I want you to use that canvas design skill to create a PDF. It needs to be professional, but it needs to be aesthetically pleasing. And what I want you to do is make sure you're including the AIS Plus logo PNG that I dropped in this folder as well because I want the whole presentation to be branded so I can share it with my team. So, I'm shooting this off in plan mode and I'll let you know when it comes back with some questions. Interesting. So, it came back and said that that canvas design skill that we just installed creates PDFs interactively, which means step five of our workflow changes from fully automated to semi-automated. So, how do we want to handle this? Let's just go ahead and just say keep it fully automated because that's kind of the whole point. We want to be able to push this live to run on a schedule trigger. Okay. So, the new plan is to replace the PowerPoint output with a branded PDF report. So, it's going to make a new tool to replace the generate slides tool. We have our current workflow state. We've got our logo. It has some proposed changes here. We're going to be looking at the PDF structure. And of course, what it has to do is update the actual workflow. So, it's going to look at this YouTube weekly report markdown file, which is the actual workflow. Of course, it's going to change that. It's going to update some of the other tools like the email tool. And then of course it's got some other implementation steps for us. And in this case, what I'm going to go ahead and do is just autoaccept these changes. And so right now it's just setting up a to-do list to actually implement those changes. We're not going to be running the workflow again. We're just going to make the changes and then we'll go ahead and test it. And just a reminder when you guys are in here building your own workflows. Just pay attention to what it's actually doing. It does some really interesting things. Like right here, it installed some dependencies to actually be able to create the PDF a little bit better. And then here it says the PDF was generated, but it's using a fallback using whatever this is, and it would look better if it had proper title and closing pages. So, it's going to install something else and then try it again. It's just a reminder of using this framework of an agent that sits between workflows and tools as it's building them out, as it's testing them. It's continuously improving them, seeing errors, seeing things that could be improved, and then just going ahead and doing that for you. So, that's where it's really powerful. And this testing and optimization phase is really important because once you actually deploy your automation, you're not deploying the agent. You're just basically deploying the workflow that's connected to tools. And that's important to understand. The workflow itself would be put up into the cloud where it could run on a schedule trigger, but the agent still lives locally in cloud code. Which means if a workflow which means if your workflow is running every week, it's not going to be self improving and self-healing. So if you wanted to do that, you would come over to cloud code, you edit the workflow, you'd improve it, and then you just push that version back to modal or wherever you're hosting them. But anyways, this finished up. So it created a new tool. It modified a few other things. it changed the actual workflow itself. And then what also it did is it made a test PDF just to see how that worked. And you can see here it's stored as a temporary file. So in our temp folder, which is right here, you can see right there we have a YouTube report PDF. And let me just make this bigger. We've got our logo right here. We've got our AI and automation YouTube analytics report and we have the thank you slide. So it basically just tested to see if it worked. But now we're going to go ahead and run that full workflow and then we're going to see if we're ready to actually push it up into production. So I'm on bypass permissions and I'm just going to shoot off run the YouTube analysis workflow. And it's not even called that, but it will be able to search through the workflows that it has and it's going to understand which one to run. It's going to execute all of those Python scripts in order. And then we should have a finished product. Okay. So here's the email. It has the similar structure as far as the actual body of the email, but then at the bottom we should have our PDF which we got attached right here. But what you'll notice is that it's only two pages. So it didn't actually create the right type of PDF that we were looking for. However, it did update the Google sheet. So it added, you know, those 30 more videos that we originally didn't have on this sheet. It added more videos, of course, and then it threw in one more weekly summary where it has a little bit of a different metrics. And what's interesting is that you can see that it did generate charts and it did do analysis because it actually generated all of these images over here, top channels, top videos, key performance indicators, posting patterns, all this kind of stuff. It just didn't actually include it. So once again, we would go back in natural language and say, "Hey, you know, we just got that PDF, but it was only two slides." So what I did is I said everything seemed to work except for the PDF that I received was only two slides. It was only the title and the thank you page. So, it found the issue. It fixed it. It changed the workflow. It changed the tools. And now, it's shot me off a new example with nine pages. And this time, we still have the logo. We still have the date. And we also now have all of the actual slides that we need in this PDF with the charts and things like that, recommendations, and then we still have the closing off slide. So, hopefully you guys understand now how important the planning really is because we did kind of rush through this in this example where we auto accepted changes and we just kind of like sped through things. And it's fine because we're still able to go back and forth and let Claude Code investigate and fix, but it should show the importance of if you are really really clear up front and you know exactly what you need, it will be a lot better off the jump, but it's not perfect. Okay, so now let's say we're at the spot where we're ready to basically make this workflow live where we actually want to forget about it and just let it run every Monday at 6 a.m. or whatever. So, we need to deploy it. So, the way that we're going to do that is we're going to use modal, which is AI infrastructure that developers love. Essentially, what modal is is it lets you spin up these kind of like computers in the cloud where you can host your automations and it only charges you when they actually run. So, you're not getting charged by the minute or by the day. You're only getting charged every time they actually execute. So, when you create an account, you'll get five bucks for free. And then if you add a credit card, even though it won't charge you yet, you'll get 30 bucks. And this 30 bucks will last you a long, long time. Trust me. So, what will happen is this screen will probably pop up and it will say that you need to download and configure the Python client. So you could basically copy this exact command right here and just put that into cloud code or you could just say hey cloud code I want to push this workflow to modal. So just help me get that initialized. But I'll just show you what would happen if you copied this and we came into cloud code and said awesome. I want to push the YouTube analytics workflow to modal so that it can actually run every single Monday at 6 a.m. And then I'm going to go ahead and paste in those two things that we just saw. And let's actually do this in plan mode first and just shoot that off. So what it's doing is it's going to read through the workflow structure and the tools and understand how it can package everything up so it can actually deploy it on modal as an app. So it came back with a plan to deploy this on modal. But there's one more thing that I want to ask it about before we actually do this. And this last part is security. So I basically told it to run a security review and make sure that my API keys aren't exposed and that there's no vulnerabilities because the reality is we just built a ton of code and I don't know what the code is actually doing. So, it's really important to be thinking about this before you put anything out there on the web. Are any web hooks exposed? And if they are, do you have like, you know, proper protection around that? Are secrets out there? Are API keys out there? What could people do now that this is out there? And as you start to deploy more workflows, whether that's an NEN or whether that's in code like this, you'll start to understand the things to look out for. But you also have one of the smartest reasoning and coding models right here in front of you. So, you might as well just ask it, hey, check the code and let me know if there are any risks. So the security review came back and it found three critical issues that need attention. But the good news is nothing is vulnerable and there's not a GitHub repo. So nothing's been committed out there publicly and everything is going to be stored as a modal secret. So the API keys and the JSON token. So nothing will be committed to any repository. So we're good to go. And basically from there it came back with a plan once more and I have approved it. So it's going ahead right now and it's creating the different tools and the different things that we need to actually be able to write this over to Modal. and then we'll go ahead and test it out over there. So our deployment is now complete. It had to update the scripts to make sure that they could actually have the right environment variable path. It had to create a modal deployment file. So it actually just understands the process of what it just did and schedule the cron or the schedule trigger. And then it had to create modal secrets that we could store over there. So it is now deployed and scheduled. So if I click on this link, this will bring us to our modal environment right here. And what you can see is that we have two different apps. We have the analytics and then we have the analytics manual. So it had to do a manual run just to see if it worked. So this is the actual app. So if I go back to the main dashboard, you can see that we have this app and there's kind of like the two different like endpoints. But if I open up the app, we can see the overview. We can see deployment history. So as you change something in cloud code and then push it back over here, you'll see a different version. And then you can also see the app logs when it's running. So when I click into the YouTube analytics one, the one that will be live, it says the next run will be in 5 days. So, it's scheduled at 6:00 a.m. only on Mondays, America Chicago time. But what I'm going to do just to prove to you guys that this is working or at least test if it is working is we can actually just go ahead and run one right now. So, I scheduled an immediate run. We're going to see this pop open right here. And we're going to see the fact that it's running right now. As you can see, it took 2 seconds to start up and now it's running. And then we'll see the result of that execution. And actually, I'm glad that this just failed cuz I can show you what you need to do. But this failed, right? So, we'll click into this. And when you click into each of the runs, you'll basically be able to see the logs and the executions. So in the log, this is what actually shows us like why it failed and what happened. So I don't really know what this means, right? All I'm going to do is copy this entire string of text. We're going to go back into cloud code and I'm actually going to go ahead and clear this because we're at 64% context. So just going to restart fresh. So I just tried to do a manual run of our YouTube weekly report app in modal and this is the error that I got. And then I paste in all that messy stuff and shoot it off. Okay, so because we tested so much and we were using the free tier of the YouTube data API, we actually just hit the daily limit which was about 10,000 units and we exceeded that because we were doing so much testing to see how well this thing would work. The good news is if this is actually running weekly, we will never hit that daily quota limit. So we're fine. The bad news is we're not going to test this one right now. But at least it does suggest other options and some longerterm fixes. But it's okay because I did want to end off by showing how you could deploy something with a web hook trigger rather than a schedule trigger. So what I did is I came into this other workflow that I built the other day which is a very simple lead web hook notification. So it has a web hook as the trigger. We would see a company name and some other data. We would research the company with perplexity and then send an email notification. And so I basically just said, "Hey Cloud Code, can you push this workflow onto modal as we did earlier?" And now we have this app in our modal as you can see lead-web hook. So what I'm going to do is go to Postman. So we can actually hit that web hook just to simulate what would happen. We've got the address. We've got the body. And I'll shoot this off. And what this is going to do is it's going to trigger this form endpoint in modal. So I'll click into that one. And you can see right now we have a status of pending. This one's going to start running. And then it will show that we actually get the email in Gmail. And so this is really just to show that once you have your stuff up and running in modal, it will work. And you can also do it based on web hooks rather than just doing it on a cron. So that looks like it finished up. We can see that we just got this email for the new lead Chipotle where it did some research about them and then obviously it gave us a notification here. And now what you could do is because you just went through the process of deploying a workflow to modal and you know that it works because you just validated that it's working. You have all of that history right there. And what you could do is say, "Okay, cool. Keep this stored either in my claw.md file or let's create this as a skill so that every time later when you're building a workflow and you want to actually push it to modal, you have all that information already there, whether that's a skill or whether it's in the system prompt of claude.md." So, I hope you guys at this point can see how cloud code makes this stuff really, really easy to get automations up and running. Whether that means an automation that you want to be there for and you trigger kind of to use as like a personal assistant or an automation that you actually want to host somewhere and have it run on some sort of trigger and you can tap into all of the skills that other people have been building and using because you can find those publicly and then just add those to your own instance. So now you have the super smart model like Sonnet 4.5, Opus 4.5 paired with all of these really good prompts and really good like MCP servers. So you can pretty much do anything in that environment. The more you start to use it, the more you'll realize that you don't have to actually switch around to a bunch of different Chrome tabs and different apps on your desktop. You can do a lot of the stuff that you need to do just in the cloud code environment itself. So once again, that claw.md file that you guys can access for free will be in my free school community. The link for that will be down in the description. And if you're looking to dive deeper into this kind of stuff and connect with over 3,000 members who are also kind of allin on AI and building businesses with AI, then definitely check out my plus community. The link for that is also down in the description. We've got full courses in here starting with Agent Zero for the beginners and then moving all the way up to actually monetizing AI automation knowledge. And I promise you guys, I'm going to be bringing a lot more of like anti-gravity and cloud code content into this plus community course as well. I also run one live Q&A every week, so you can ask me questions about nitn cloud code or building an AI business, all that kind of stuff. And I'd love to see you guys in the community in those live calls. But that is going to do it for today's video. So if you enjoyed or you learned something new, please give it a like. It definitely helps me out a ton. And as always, I appreciate you guys making it to the end of the video. I'll see you on the next one.