Transcription
Just a couple of months ago, I was spending 4 hours a day on work that Claude now does in about 11 minutes. And no, it's not because I got better at prompting or anything like that. It's actually because I stopped prompting entirely.
Now, that sounds like I am exaggerating. I'm not. What actually happened was I built layers. So, persistent context, reusable skills, connected tools, autonomous agents, and at some point, something just completely flipped where I stopped explaining things to Claude and I just started working with it. And here's the weird part. The less that I prompted, the better the output that I got.
Because by the time I type a message now, Claude already knows my business. It knows my preferences. It knows my code base, my voice, my language, my standards, and the decisions that I made last Thursday. Now, most people, they think Claude is just a chatbot that you talk to. It's not. We both know that it's an operating system that you are building on. So, every document that you upload, every skill that you are writing, every connector that you turn on, it compounds. And the Claude that you use on day 100 is a completely different tool than the Claude that you use on day one. Not solely because of the model change, but more so because your layers got deeper.
And what I'm about to show you is the entire system. So every layer, every product, and how they connect, and what it also looks like when they all are running together. So by the end of this, the way that you guys think about this tool, it is going to permanently be different. Let me go ahead and show you the whole thing.
All right. So what I've done, I just laid out the entire Claude ecosystem all in one diagram so you guys can see how every piece is going to be connecting. So at the very bottom, we're going to start with the foundation of everything. This is going to be the intelligence itself. So this is the brain. Now this is going to be consisting of three different models. So we have Opus for your hardest problems. We then have Sonnet for your daily work, and we have Haiku for speed and volume. Plus, on top of that, we have the extended thinking, which just lets any of them reason deeply before they actually answer. So everything above this layer, it is only as good as this foundation. So if the brain is getting smarter, everything on top of it is going to get smarter overnight as well. You literally are changing nothing. The foundation, it just upgrades underneath you.
Moving up, right above the brain, it sits the interface layer. So this is Claude AI. So this is where most people are actually spending their time, and honestly, most of them are only using about maybe 20% of its capacity from what I have seen and experienced myself. Now you can see here, this is going to consist of projects, memory, artifacts, and connectors. Now this is where your conversations happen. This is where Claude actually learns your business and where context is going to start compounding. So, we're going to go deep on each one of these separately.
Next up, moving up the ladder is the office suite. So, this is Co-work. This is Chrome, Excel, PowerPoint, and something also called plugins that basically just bundles everything together per department. So, this is Claude embedded directly inside the tools that you are already using day-to-day. Now, there's not going to be any copy and pasting between any of the windows. You're also not going to have to experience switching any context. Claude is just going to be right there, right where you're working.
Next up, it is going to be the engine room up at the top. So, this is going to consist of Claude Code. It is also consisting of the skills, sub-agents, agent teams, and of course, Claude.md at the very end. Now within this, this is where developers, and increasingly, really all of us, non-developers, I believe at this point, through co-working autonomous systems. So this is really where AI stops being a tool that you use and starts being more so infrastructure that is running.
Now, with all of this, this is what actually ties everything together. So if we look at this bar on the left-hand side, that is MCP, Model Context Protocol. So you can think of it as the plumbing underneath practically everything. So when you use a connector inside of Claude, that is going to be MCP with a nice interface. When Claude talks to GitHub, that's also going to be MCP. Or when Claude pulls data from your CRM, MCP. Now, one universal protocol connecting Claude to really everything. And we'll get into this in detail later on.
Now, if you move over and you look at the arrow on the right-hand side, this is just intelligence flowing downward. So when Anthropic makes the brain smarter, every single layer above it, it is going to get better overnight, right? So you do not have to touch anything. It just improves underneath you. And this other arrow on the left-hand side, this green one, this is essentially just the context compounds upward. So what I mean by that is every single document that you are putting into a project, every skill that you write, every connector that you enable, it makes every layer above it even more powerful. So that is the compounding effect, and that's the key insight right there at the bottom.
So what we have is the people pulling furthest ahead, they are not using any better prompts. They're just building deeper layers, and this is the key insight right here at the bottom. So the people pulling furthest ahead, they are not using better prompts or anything like that. They're just building deeper layers. So take a screenshot, hold onto this diagram. We are going to be going through every single piece of it, and by the end of this video, every single black box that is going to mean something specific to you, and you'll also see why it has to be built this particular way. But overall, that is going to be the full map.
Now, we're going to build it layer by layer, starting with the bottom, with the brain that powers everything. Now, real quick, before we get into the layers, if you are a business owner watching this and you're thinking like, "Okay, this is powerful, but I do not want to become the person building all of this myself," that is exactly what we help companies do. We have been helping teams implement AI into their operations, train their staff on how to actually use it, and identify where these systems can save time, increase output, and create real leverage inside of the business. Now, if that's something that you want help with, then you can schedule a free AI audit with me using the link down below. And then for those of you who are watching this for more of an opportunity angle, maybe you want to learn how to do this for clients or even just build an AI business around these kinds of systems. I'm also hosting a free webinar on the 24th of this month where I'll be breaking down how people are using AI skills to start landing clients and build towards 10K a month. So that link to register is going to be down below too, and on screen right above here.
Jumping into the first portion, which is going to be the brain, of course. This is where most people are just picking one model and using it for everything. Now, that's, if you think about it, it is just like hiring one person to do brain surgery, having them also answer phones and also mop the floors. So, you're overpaying for simple stuff, and at the end of the day, you're underpowering the hard stuff. Now, the Claude 4.5 family, it gives you three different models. And understanding when to actually use each one, that is actually the first real skill inside of this ecosystem. Let's break that down a little bit.
Opus 4.6. This was the strategist. This is the strategist. So this is the deepest thinker. It's built for the hardest problems. So with it, it comes a 200,000 token context window standard, with a million token beta available. And it can also produce up to 128,000 tokens in a single response. Now, what actually makes Opus different from really anything else out there right now? There is a thing called adaptive thinking. So what this is doing, it is just deciding how deeply to actually reason about your question across four different levels. So you can ask it something simple, and it will respond in seconds, right? And then on the other hand, if you're asking it to architect a distributed system, it'll spend minutes in deep reasoning before actually giving you its output. And you can also set the effort level manually too. So, if you, of course, want to set things low for any quick stuff, or for anything that's going to be a little bit more complex, you could just boost it up to max for any harder problems.
Now, what's actually pretty wild is that Opus, it can sustain autonomous work for over 14 hours on a single complex task, which is nearly double the last generation. And that's not a chatbot anymore. Like, that is a colleague who is not sleeping. Like you are doing and getting majority of what people are getting paid hundreds of thousands of dollars to do while you are sleeping. Now, on the dev side of things, Opus, it scores over 80% on the SWE Bench verified, which it actually tests real GitHub issues, now over 65% on autonomous coding benchmarks. And these are not marketing numbers. Like these are benchmarks developers actually care about for a very specific reason.
Now, I'm not going to get very much more in-depth about all this stuff because you don't really care about all that, but just to dive a little bit further into some of these models and give you just very high-level context. So, on a 4.6, this is going to be essentially just the workhorse. So, this is the default model on Claude, and for very good reason. So, Anthropic's internal testing, it showed that developers, they preferred Sonnet over the previous generation's flagship model, and that was the one that cost five times more, nearly 60% of the time. So, if you let that sink in, the mid-tier model of this generation, it beats the best of the last generation more often than not, and it is handling extended thinking on top of that. It supports the million token beta, and it is fast enough that you never feel like you are actually going to be waiting.
And then there's Haiku 4.5. This is what I refer to as the sprinter, and this is four to five times faster than Sonnet. It is quite literally built for volume. So any sub-agents inside of Claude Code, any real-time assistance, or high-volume processing, that is where you're going to be using Haiku. So if you need Claude to chew through a thousand customer messages, or just run 10 parallel code reviews, that is where you should 100% be utilizing Haiku. So you could think of it more or less like a law firm. Opus is going to be your senior partner. You bring them in for the deal that makes or breaks the company. Sonnet, this is the seasoned associate. It handles about 90% of the real work with great judgment. And then Haiku, this is, I guess, the paralegal. So it chews through a stack of documents while you're still finishing your coffee.
Now, what most people are actually missing within all of this is that the real skill, it's not just using the best model. It's actually routing to the right model at the right times. So strategic planning, it goes to Opus. And then daily coding, any writing, any analysis, that is going to go to Sonnet. And then bulk processing and sub-agent tasks, you for that to Haiku. And with this, you'll also notice exponential differences in your pricing, of course, for the better. And with that being said, the quality on routed tasks, it's actually better than just using Opus for really everything, which is funny enough, because Haiku and Sonnet, they're just optimized for those specific lanes, so they're going to be a little bit more specialized. And every layer that we are about to be covering, so projects, even artifacts, Claude Code, and Co-work, these all run on one of these three different models. And when you actually understand the trade-offs, you stop using just one tool and you start designing an entire system.
Now, real quick, here are two features that actually change how you work with these models. And most people, they do not know they exist yet. So, that starts off with, I was discussing earlier very briefly, was the extended thinking. So this again is available on Opus 4.6 and the newest Sonnet, and previous generation models. So when you are toggling it, Claude, it is not just spitting out an answer. It is actually reasoning through the problem step by step before responding to anything at all. So you can actually watch the thinking process unfold, and it is going to be the difference between asking someone a question and getting their gut reaction versus asking them to just sit down and really think about it first. Now, on any complex strategy questions, so things like pricing, market entry, competitive positioning, you know, the extended thinking, it catches any of the edge cases and also any contradictions that a quick response might just completely miss. Now, on the development side, it produces much more coherent architecture plans. So the model, it literally catches its own mistakes mid-thought.
And then there is the fast mode. So this is any research preview on Opus, which is just two and a half times faster output than you'll regularly be getting. So this is more so when you are going to be needing Opus-level intelligence, but speed is going to also be a high priority factor for you. It's also going to be very useful for any real-time work, time-sensitive analysis, or when you're just iterating fast and you do not want to wait for anything. But with that being said, that is the entire brain.
Now, let's just go up one more layer to the interface, and this is where most people actually live, but from what I have been seeing on the internet and working with our clients, they are barely scratching the surface. So let's jump into that.
Now, here's the problem with this is that every time you open a new Claude conversation, you are essentially starting from absolute zero. So Claude, it of course will not know anything about your company. It will not know your revenue targets. It will obviously not know your tech stack or your competitors, or your team size, or your brand voice, or even the pricing decisions that you guys have been making recently. Now, every single conversation, it always starts with re-explaining who you are, what you do, providing it that context. Now, this right here is one of the single biggest reasons that people are actually underestimating Claude. This is where they are judging it without context. They are judging it without context, and projects, they are the sole fix to this.
So, project, more or less, it is just a permanent workspace where Claude is going to have ongoing access to your most important documents. It'll also have a defined role and accumulating memory of all of your decisions. So it is the difference between explaining your business to a stranger every morning versus just walking into your office where your co-founder, they already know everything. So let me show you how this actually works. They're going to be three core components.
First up is the custom instructions. Now, this is where you define Claude's role for this workspace. And you can't just write "be helpful." Like, you have to get very specific, because things like that, it is going to actually be detrimental and just completely useless. So you have to write something like, "You are the strategic advisor for my company. You understand our business model, our positioning, our current metrics, and our team capacity. I need you to push back on ideas that do not align with our stated strategy unless the reasoning is actually compelling. And when I ask about pricing, reference our existing rate card and the ROI framework that we use with our clients." Now, the more specific that you are actually making this prompt, the faster it becomes useful for you. So, it is the same principle as writing a job description for a brand new hire. You obviously would not hire someone and say, "Just be good at your stuff." Like, you tell them exactly what that role is. So, it makes zero sense why there is no leeway given the same way within AI.
Number two is the knowledge base. So this is where you're simply uploading PDFs, any Word documents, any code files, spreadsheets, images, and audio. And this is each up to about 30 megabytes. And this is where Claude is using retrieval augmented generation, better known as RAG, to just dynamically pull the most relevant sections for every single response. Now, that effectively expands your usable knowledge far beyond the base context window. So imagine your consultant could scan a room full of filing cabinets in 2 seconds and pull exactly what is relevant and what you guys need. That is more or less what RAG is doing.
Number three, this is the conversation history. So every conversation with a project, it is going to build accumulated context. Now, Claude, it is going to learn your patterns. It will learn your preferences, and even your terminology, and this is going to compound over time. Now, what most people are getting wrong is they create one project and they dump everything inside of it. You do not want to do that. You actually want to create separate projects for separate different domains and different purposes. So you have to think of it like having different specialists. So for example, you have the "Company Brain" project. Now, this is going to be all about strategy, where you have the pitch deck, the financial model, competitive analysis, maybe even quarterly OKRs. So this is where you think about the business at the highest level. Then you have another level. This is the "Codebase" project. So this is all about development, where you have the architecture docs, API specifications, database schema. So all of the technical conversations, they will live inside of this. Then you can have a "Content" project. You can have a "Sales" project, "Client" projects, where you just have your meeting notes, your contracts, whatever. So most people, they treat projects like having their filing cabinets, and they're really not meant to be looked at like that. They're more so team members. So each one, it is going to be a specialist with deep expertise in one domain. So the more focused that you are actually making them, the better that they will be performing for you.
Moving on, we are going to layer on the memory. And this is where things are going to get a little bit more interesting, where memory, it launched about late 2025, I would say, and it completely changes the game. So as you have conversations within a project, Claude, it is going to generate and store memories. So that's consisting of your project status, that's also your decisions, and even your preferences. So these, they persist across sessions. So you come back two weeks later, Claude is going to pick up right where you left off. So it would remember that you raised prices last month. Also that you're targeting healthcare this quarter, that you refactored the auth module, and that you chose Postgres over. You do not have to re-explain any of it. It is just going to be there. You could also view, edit, and delete individual memories. So memory, it is going to be siloed to each project. So any sales context, it doesn't really bleed into the codebase. So anything on team and enterprise plans, projects, it is going to support the sharing. Now, with this, you can just set permission levels, and your whole team, it works with the same contextually aware Claude.
So first off, projects, these are the foundations that just makes every other layer work better. Co-work, on the other hand, this is going to be better with your brand guidelines actually loaded in. Claude Code, it is better with your architecture loaded. Now, Claude and Excel, this would be better with your financial models loaded in. Artifacts, these are going to pull from project context, and skills, they reference the project knowledge. So context within this, it is going to be the underlying multiplier. And every feature in this ecosystem, it is going to simply get smarter the deeper your projects actually are. So this right here, this is the single highest ROI setup that you can do, and it takes less than an hour.
But okay, so now Claude knows your business. The next question is, what can it actually build for you? And I do not mean simply just writing things out. I actually mean build and doing the work for you. Like, there is a complete difference. Now, most people, they are using Claude just like a chatbot, just like a typewriter. So they ask for text, they get text, of course, they copy-paste it somewhere else, they move on. Like, that is what 90% of the population, how they actually use these tools, and that caps out very fast because text, it is inherently, it is static, and your problems are dynamic.
So when Claude actually generates something substantial, more than about 15 lines, and it's self-contained, something that you want to actually iterate upon, it creates what is called an artifact in a panel alongside the chat. And these, they're not just documents. They can be looked at as documents, but more so they are interactive. They're editable and sharable. So we're talking about live preview code running in your browser. You have full React components. You have data visualizations. You have complete websites and interactive applications. So you can think of the chat as your conversation with an architect, and the artifact panel as the blueprint that they are drawing while you're actually talking. So you can discuss what you want, and the blueprint, it'll just be updating it live, and you can point at something and say, "You know, just change this," and it'll change it without having to start over.
Now, AI-powered artifacts, they take this even further. So Claude, it can actually embed its own intelligence into what it is building. So you can ask it to build a competitive analysis tool, and it will not just hand you a static template. It will actually build an interactive app that calls Claude's own API to analyze competitors in real time. Also, pull any web search results and present everything in a formatted dashboard. So you can share with it a link, maybe a teammate opens it, uses it. There's not going to be any API key needed, and no setup whatsoever. Now, right there, that is the mindset shift. They need to stop asking like, "Okay, what should Claude actually be writing for me?" And start asking, "All right, well, what can I have it be building for me now?"
Now, quickly, let's talk about connectors, because an AI that cannot see any of your data, it gives you generic answers. So before connectors, Claude, it was brilliant, but it was just very blind. So it can reason about anything that you pasted in, but it could not see your calendar. It could not see your inbox. It couldn't see your CRM or code base unless you actually copied stuff back and forth manually. Now, connectors, they flip that and they fix it for you. So Claude, it can now have access to your real tools in real time. So there's actually over 50, as of right now, there's over 50 verified integrations, and they're all growing. So there's Google Drive, all the big ones like GitHub, Gmail, Notion, Asana, and you can actually add custom connectors just by entering any remote MCP server URL.
So the models, they are the brain. In projects, they're going to be the memory. Connectors, they are essentially the hands. So they are how Claude actually touches your work. So to give you a quick example, like you could say, "What meetings do I have tomorrow, and what should I prepare for each one?" Claude, it would check your calendar, it'll pull relevant documents from Drive, it'll give you a briefing. There's not going to be any copy-pasting, no tab switching. And you can just say, "Check the latest commits on staging and flag anything that might break the off," and it'll query GitHub directly. So with all of this, the setup realistically takes about 30 seconds per tool. So you just have to go into your settings, you go inside of connectors, you can click on connect, you click on authenticate, and that is done. It is that simple. You just have to toggle them on and off per conversation. So if you have got 10 or more active, you can just switch to on-demand mode. So Claude only fires when they are relevant.
Now, MCP apps, these are the latest evolution. So these are just interactive connectors that render live UI elements directly inside of Claude. So things like Amplitude showing analytics charts, Asana showing project timelines, Figma showing design previews. Like these are not just data feeds. Like they are interactive interfaces embedded right into your chat. Now, just a quick technical note, connectors, they are built on the MCP protocol. So we'll cover that in detail later, but you do not need to know what MCP is to start using connectors. 90% of users, they never need to see the plumbing. And even tomorrow, it looks like we may switch from MCP to utilizing CLIs once those are becoming more readily available from providers.
There's a compounding effect that you should be seeing relatively quickly. You'll have a project with your business docs, plus, you'll have the live connections to your CRM, even your email, and a project management tool. And that's not just a knowledge base anymore. Like that's a live dashboard of your entire business. And the artifacts, they pull from these connected sources. So the more Claude can see, the less that you actually have to be explaining. And with that being said, like projects, they give Claude more context about what to actually be building. And connectors, they give artifacts access to the live data. And as we will see, skills, they just make sure that everything Claude builds, it is going to be following your standards all automatically.
All right. Now, moving into the next portion. This is the office suite. So, let's go through each one now. Then we have Claude in Chrome. So, this is the official browser extension. It is just a side panel agent, and it's not a chatbot overlay. So, it actually is able to read pages, even click buttons, fill any form, do any scrolling, navigating, and automating multi-step browser workflows. Now, the three capabilities that actually matter within this are the workflow recording. So, you can do it once, you can save it, and then just replay it at any time. You also have the scheduled tasks, where you can handle things daily, weekly, any monthly automations that run without you, and console reading. So, for any developers, it can read JavaScript errors, any network requests, and DOM states directly. Now, this is available on all of the paid plans, but it is still in beta. So, treat it as supervised, not unsupervised, I would say, but it is very powerful for any repetitive browser tasks.
All right. So, now everything that I'm about to show you with Claude Code, even skills and sub-agents, it requires comfort with a terminal. You do not have to actually know how to code. You just need to be utilizing the terminal. Whether that's going inside of Cursor or just talking in plain English, it doesn't matter. You do not have to be technical, and do not refrain from just hearing that word or let it refrain you from just hearing that word. Now, I know that it immediately excludes the majority of people in any organization, like operations managers, finance teams, marketing leaders, legal reviewers. Like, they're not opening a terminal, probably ever, until they really have to when they get fired and they have to find a job where they have to be using it. And that's going to be happening, I promise you that.
But Claude Co-work is essentially just Claude Code for everybody else. So any desktop automation agents, any automation capabilities. This is just autonomous capabilities through graphical interface, not a terminal. So it is just more user-friendly. So it is able to read documents. It can manage files, generate reports, build presentations, process any inboxes, and pull data from connected tools. So if Claude Code is a developer inside of your terminal, then Co-work is an executive assistant on your desktop. So when you actually open up Co-work, you'll see templates for any common tasks. You can crunch data. You can make even a prototype. You can organize some files and send messages. Now, each one, it prefills a structured prompt. So even somebody who's never used AI can get started. So it is able to ask clarifying questions with even multiple-choice options. It is literally the same system that Claude Code uses, just presents it in a friendlier way. Now, technically, Co-work runs in an isolated VM on your computer, and files and conversation history, it is going to stay local. But with all of this, it's going to be powered by Opus with a million token context window. Now, the VM architecture, it means that it cannot accidentally break your system. So with that, it can capture screenshots, it can perceive UI elements, and control mouse and keyboard through the computer use API.
Now, where Co-work gets a little bit more interesting is through the plugins. And this is the piece that most people have not even heard about yet. So plugins, they are essentially just bundles, how I look at them. Now, instead of downloading just a skill or even an MCP server or just some instructions, a plugin, it is going to be packaging everything together for a specific domain. So any skills, any slash commands, any MCP connections, and sub-agent configurations, it is all going to be in one installable package. So think of it as a care package. So if you are installing the "Sales" plugin, you immediately get slash commands like "competitive brief," "pipeline review," "outreach draft." You then get skills for prospecting, research, call prep, and deal management. And then you get MCP connections to your CRM. So everything that a salesperson needs, it is going to be bundled and ready to go. On topic, they actually released native plugins already with some standard company functions. So let's go through those. If you haven't checked out my previous video on Claude Co-work already, let's dive into those. So, sales, legal, finance, marketing, customer support, product management, data analysis, enterprise search, bio research, and the meta plugin. So the meta plugin, this is the plugin that actually creates other plugins.
Now, to dive into a couple of them very briefly, sales, this is just going to be doing any prospecting, any call preparation, any competitive intelligence, pipeline review, deal management, or any nurture sequences. Uh, another one, let's look at, we can see marketing. So marketing, this is any campaign planning, any content drafting, SEO optimization, or performance reporting. Now, with this, I do want to say that plugins, it uses the same skills, the same MCP protocol, the same model architecture as Claude Code. So a brand voice skill that you wrote for Claude Code, it works in Co-work through a plugin. Now, the layers, they are interoperable. So we'll also be covering skills in depth in just a few minutes.
Now, code editors with AI autocomplete solve a narrow problem, which is just generating the next few lines. But building software, it is not about those individual lines. It's actually about understanding how an entire codebase is going to be fitting together. So which files need to change, how modules are going to be connecting, and what breaks when you actually modified something. So AI autocomplete, it's just a faster typewriter, but what you actually need is a collaborator who understands the whole project.
Now, Claude Code, this is a full autonomous agent that lives inside of your terminal. So, it understands your entire codebase. It navigates your file system. It reads your documents. It writes and modifies code across multiple different files simultaneously. And it runs terminal commands. It executes tests, debugs failures, manages Git workflows, and handles deployments all through natural language, which means that you're just typing on your keyboard in plain English or using speech dictation, and it's going to generate the code for you. And right now, if you're a founder who does not code, this is why technical co-founders are becoming optional for a growing number of software businesses. It's not because code does not matter, but it's because Claude Code executes on a technical vision at a speed and quality that changes the math on actually hiring. Claude Code. So, it integrates with VS Code, Cursor, Windsurf, really any IDE out there. So, VS Code, it is the foundation. Everything else, it is built off of VS Code. So, Windsurf, Cursor, those are wrappers off of VS Code. Now, all it takes is just one command to install it. There's also a web version at claude.ai/code if you do not want to be touching a terminal.
All right. Now, let me go ahead and quickly explain the core mechanic because this is what actually makes this different from everything else. So, you describe what you want, right? Claude Code, it is going to scan your projects. It is going to read your relevant files, understand the architecture and the patterns, and write or modify code across every file that needs to be changing. And it runs linting and it executes your test suite. So if anything fails, it'll read the error. It would automatically diagnose it and fix it, and then run the test again. And this loop, it is going to continue until the task actually succeeds or until it needs your input.
Let me talk about Claude.md because this is going to be mission-critical. So every Claude Code project, it should have a Claude.md file in the project root. Now, this is the command center. It contains everything that Claude Code needs to know. So anything about your tech stack, architecture decisions, how to run tests, and even how to deploy. Now, Claude Code, it reads it automatically at the start of every session. So essentially, without it, Claude Code discovers your conventions through expensive trial and error. You do not want to be wasting your time or your tokens with that. But if you are using it, every session starts with full context, which is extremely important for most organizations.
Now, I do want to mention that you should be using Claude.md not just for the rules, but also as an inventory of your codebase. So, when you're working on a large project with millions of lines of code, having a map that actually says that the authentication logic lives in these files, the API endpoints are here, the database models are here. That means Claude Code does not have to scan the entire project to find what's going to be relevant, and it can just go straight to the right files, save you time, keeps things lean. And with that, if anything is going to be bloating to 15,000 tokens, it'll eat your context window every single session, and Claude Code won't be able to follow the instructions properly.
All right, so here are some critical techniques. So, first up, we have the plan, then execute. So, this is the single most important technique. Now, do not just tell Claude Code to actually be building something. You need to tell it to plan first and switch to plan mode with /plan. So this is where it explores the codebase, identifies all relevant files and patterns, and it proposes a detailed implementation plan where you can review it, you can ask questions, suggest changes, and only when you are satisfied do you tell it to execute. So without this, like the model sometimes it just goes down the wrong paths, and especially larger codebases, and you waste time unwinding it. But with it, you can just catch your architectural mistakes before any code is going to get written. And the plan really just takes 5 minutes. And the rewrite that you avoid, it takes 5 hours. Maybe not that long, but you get the idea. So most people who try Claude Code once and get disappointed, they just completely skip this planning step.
All right. Now for phased execution. So for any bigger features, do not let Claude Code run the entire thing in one shot. You want to break this thing down into phases. So have it create a markdown tracker file with the checkboxes for each phase. And then from here, you can tell it to stop after each phase and ask for approval. So when a phase is going to be completed, you can clear the context window. You can type /clear, and then tell it to execute the next phase, referencing the tracker. Now, why you actually want to do this is because when the context window is filling up, that's when hallucinations are going to be starting, when the code is going to get sloppy, and things consequently are going to be breaking. Now, by actually clearing the context between phases, while keeping a persistent tracker file, you'll then get a fresh context window for each chunk of work. So it is an extra step, but it's really just prevents the tug-of-war with AI that is going to be wasting hours.
Now, that is where Claude Code actually sits into this entire ecosystem. It is going to be the execution layer. So the projects, they provide business context. And then Claude.md, it provides project conventions. And then the skills, it provides reusable expertise. The sub-agents, they provide the parallelism. MCP servers provide the tool access. And with that, a Claude Code session with a rich Claude.md file, loaded skills, connected MCP servers, and custom agents is a fundamentally different thing from a bare session. Now, the layers, they compound exponentially. Like I cannot even begin to translate the effects that you can have with using a foundation like this. Every one that you are adding, it makes the other ones much, much more powerful.
All right. So this next chapter here, this might just change how you work more than anything else in this video. Please pay attention. All right, so let's say you are repeating yourself to Claude every single day when you're asking it to do your processes. Maybe it's writing emails, whatever. So every time that you start a conversation, you have to explain your brand voice, your coding standards, if you're doing that, or, you know, just your formatting preferences, or how you go about some particular processes, right? So you give it the same instructions all the time, maybe slightly different words, over and over again. Now, that's not just wasted time. It's also wasted attention and wasted money, and it's a tax that is just compounding forever unless you actually do something about it. And there is a fix with that. Every instruction that you are repeating, it is simply a skill that is waiting to be written and created by you.
Now, skills, they are simply just reusable instruction sets that Claude discovers and loads automatically when they are relevant. So if you write a brand voice skill once, Claude will apply it every time it writes anything out for you. Or maybe you're creating an email skill, just giving your process on how to actually write back emails. It is going to follow that process every single time that you ask it to run that particular, that specific skill. So if you think of it this way, the same way that you would train a human employee, just by letting them read through an SOP, then they'd be able to do the process. You just train an agent on it. You give it the skill, it reads it, and then it does it. And the more that you actually use the skill, the better it is going to get.
All right, so let me go ahead and show you how these actually work. Now, a skill, this is just a folder with the skill.md file inside of it. Like, that is simply it. There's no complex setup. There's no coding required at the basic level. Now, the skill.md file, it has two different parts. So the front matter. So this is just between two sets of dashes at the top, and this is written in YAML. Now, this is going to contain the name and the description, and this is what Claude reads first to actually decide if the skill is going to be relevant. Then moving on, we have the instructions. So this is the body of the file. So, this is the actual step-by-step rules, the examples, the guidelines that Claude actually follows when the skill is actually invoked. So, if you guys want to see more on how to actually set up skills and what this is going to be containing, all that sort of stuff, then check out my previous videos on how to actually utilize Claude skills properly.
Now, how to actually know when you should be using a skill, or how does Claude even know how to be using a skill? There's going to be two trigger mechanisms. So, there's just explicit ones where you can just type a slash command like to use the skill. So you can just do /voice or /deploy staging, whatever you called it, and the skill is just going to fire directly. Or you can say, "Help me write a LinkedIn post," and Claude is going to search through the skills metadata, find the brand voice skill that it is relevant, loads it, and applies it automatically. You don't have to tell it. It is just going to know.
Now, to actually start building your skill, there is a six-step framework that I recommend. Now, step one, that is the name and trigger. So what is this going to be called? What natural language would fire this off? Number two, this is the goal. So, in just one sentence, what would the skill actually be accomplishing? Step three, here's your step-by-step process. So, if you had to do this manually, exactly what do you do? In what order? Step four, reference files. So, what context does the skill need? Is there any brand guidelines, any API documents? Step five, rules and guardrails. So, what could go wrong? You want to build in the constraints. Then step six, this is the self-improvement loop, where you can just run it, watch it work, give it feedback, and then iterate.
So, just to give you a quick example, here's what a brand voice skill would actually look like hypothetically. So we have up at the top, we have the voice principle. So something like, "Writing like a smart friend who happens to be an expert." So this is like its role that you would normally give into a prompt. And then we have the rules. So more specifics, more of the prompt saying like, "Use active voice. We built this. This was built." So on so forth. Giving it some additional context, like phrases we never use. Providing it with good and bad examples, so on so forth. Now, every time Claude writes anything inside of this project, it will check the metadata, see that the skill is relevant, load the instruction, and write or start writing in your voice.
Now, the feedback cycle, this is how things or skills are going to get better. Now, you're never going to write a perfect skill the first time. Maybe you will, but it's very unlikely. It's always going to be an iterating thing. So you watch the agent work hypothetically. You notice it does something wrong. You give it feedback. Fixes the skill, you run it again. And the first couple of times, it might be generic. And by the time you've run it 10, 20, 30 times, every single iteration, it is going to get better. And that is why it is important to watch the agent work the first few times. And that is how you identify the opportunities to speed it up and actually save tokens.
Now, when you actually have 20 well-crafted skills, like Claude starts every single session already knowing how you work. It knows your voice, it knows your standards, it knows your deployment process or your testing philosophy, your documentation style, even your proposal format. So you've essentially just stopped training Claude and you've started working with it. Because skills, it lives in Git. So what that means is when somebody new joins the team, they get the benefit of every single skill immediately. You don't have to do a month of onboarding. Claude already knows the conventions. So every time you repeat instructions to Claude, like that should be a skill. So every workflow that you run more than twice, the compounding effect, it is very real, and it starts the moment you actually write your first one.
Moving on to sub-agents and agent teams. Now, Claude Code, it is great at executing tasks, but there is a ceiling when everything is running sequentially. So the complex features, they touch multiple different concerns. So the implementation, the tests, the security review, the docs, API compatibility, all of that. So running them one after one, it is very slow. And some tasks, they just need different models. So you do not need Opus to scan a directory structure, but you definitely need it for architectural decisions. So with that, the sub-agents, they actually solve for this. So Claude Code, it spawns specialized child agents that actually handle different parts of a complex task in parallel. So each one, it is going to get its own context window optimized for its specific job. So instead of one developer doing the implementation, and then testing things, and then security review, and then the documents, all that, you have actually a team of four, each working simultaneously. You have the explorer. And these are fast, they're read-only, and it runs on Haiku. So this is the researcher, how I look at it. And this spawns to quickly scan directories, even read key files, gather context before making changes. It's cheap and it's fast. Then there's the planner. So this is the thorough research agent on Sonnet. And this is the architect. So this gathers comprehensive context. It analyzes any options. It considers.
trade-offs and produces structured implementation plans. So this is a full capability agent for complex multi-step operations. And this is the builder. It has its own tool. It can read. It can write files and it can execute commands. So think of it this way. Explorer this is the intern who runs to the filing cabinet. Plan is the senior architect who designs the building and task is the general contractor who actually builds what the architect designed.
Now moving on, you can also create custom sub agents. So you can create markdown files in your project's clawed agents directory where each one it defines a specialized agent with its own system prompt, its own allowed tools, its own model preference and preloaded skills. And here's a different approach that is actually going to be more effective than the standard way. So instead of creating agents as completely separate roles where you have a UI designer, a code reviewer, even a security auditor, you can just create agents as different parts of one specific task. So you could break apart all of those tasks into subtasks. Each is going to be executed by a different agent with its own context window. So instead of a generic UI agent, you can just create a color optimizer agent that only focuses on maybe like hex codes and color schemes and then a UX layout agent that focuses on component structure and then the user flow and a feature expander agent that identifies the next natural features that a user would actually want. Now all three they're going to be aligned on the same intensive which is just improving the front end but each one it has laser focus on their piece. So you send one prompt and all three run in parallel and the results are much more cohesive.
Now there is actually a key limitation with all these sub agents however. So they do work in parallel but they never are actually talking to each other. So they're just a bit siloed. So if you create a front-end sub agent and a backend sub agent they both are going to be working independently. So there's no way for the front end to tell the back end like, "Hey, I'm using this new library. Make sure it's actually compatible." You're essentially like the monkey in the middle getting all the updates and you're the one managing what is actually happening next. So for many tasks like that is going to be fine. But for the tasks that require coordination between the agents, that is where the agent teams come in.
So agent teams, these are disabled by default and you enable them with an environment variable. And the key difference is is that sub agents, they report only to the parent and the agent team members, they can message each other directly. So you've got one team lead that coordinates and assigns tasks. Then you've got multiple teammates each which is just a clawed code session and you have shared task list with dependency tracking. So this is a mailbox system for direct agent to agent communication. Now sub agents look at these as the contractors that you send on separate errands. Agent teams. These are a project team in the same room working on their pieces while staying in sync through conversation. So you can say like create an agent team or spawn an agent team and those magic words, they're going to be important because if you just say spawn agents, Claude might be confusing it with sub agents.
So with that being said, the team lead it looks at the desk. It decides how many teammates to spawn, whether that's three to five being the sweet spot. Beyond that really it's diminishing returns and it assigns each one a role. So each teammate it'll have its own context window, its own tools and a mailbox. And when the front-end agent finishes, it manages the backend agent. So when it says, "I'm done. Here's my API contract." The backend reads the message, it updates its endpoints, and it tells the team lead when it's actually finished.
Let me go ahead and give you some real use cases because this is where it actually gets very exciting, and you can start seeing like, okay, how can I actually use this inside of my business? Does this ever work with other like real practical use cases? So, let me go ahead and give you some. So, first one, content repurposing. So, you can just give this one YouTube transcript and spawn four different teammates. So, you can spawn a LinkedIn writer, a thread writer, even a newsletter writer, and a blog writer. Now, the lead ensures that no platform is going to lead with the same angle. So, you have each piece feeling fresh, not repetitive at all. Then you have the competitive analysis where you have four analysts each researching a different competitor independently and then you have a synthesis lead that brings it all together in a comparative report. Next is an advisory board simulation. So you could pose a business decision to five different agents where you have a market researcher. You have a financial modeler. You have a devil's advocate. Then you have competitive strategist and an audience analyst. Now they each are going to be analyzing from their perspective, from their debate and deliver a go or no go recommendation with the top risks. Then you have the full marketing campaign which is just an email marketer where you have the social media manager, the ad copywriter, the landing page creator. All of them are going to be building their pieces while communicating to maintain consistent messaging.
Now where I do want to be honest with this is that agent teams, they are experimental. The costs are significantly more since each teammate is running a full session. But it points clearly towards where autonomous development is actually heading. And beyond that, like if you're asking yourself like when should I be using what? So a simple question that I ask myself and what I tell my team to be asking themselves when they are running through these things and building out these agents is do the agents actually need to speak to each other? Okay. Well, no. Then you can use the sub agents. It's cheaper. It's faster. It's even production ready. And if it is yes, you need to ask yourself, is the task complex enough to justify the token cost. Yes, go to agent teams. No, just have a normal Claude code session. Not a big deal.
All right. So, let me just go ahead and show you what all of this actually looks like when it is all running. So, just picture running four agents in parallel. So, you got the first agent. This is a morning coffee skill that checks your calendar. It looks at your ClickUp tasks for the week and it plans your entire day. So, it blocks off the time. It eliminates any decision fatigue about what to work on. And then you have the second agent, which is just a pulse check on all projects and all commitments, catching things that might slip through the cracks just because you're busy creating content. Then you have a third agent. This is creating an Excal diagram for a video that you are planning. You have your fourth agent scraping YouTube comments and giving you an analysis of what viewers want to see next. Now, all four of these, it takes about 30 seconds to kick off. All of them, they finish while you're still getting settled. And because each one has skills loaded with full context about your business, even your projects, your channel, every output, it is personalized and not generic.
Now that is what builtup layers actually looks like in practice. So the morning coffee skill, it knows your calendar, your task management, your priorities. And the pulse check skill knows your project IDs and it delegates the ClickUp searching to a sub agent to preserve the tokens. Now the diagram skill, it knows your visual style. And the comment analysis skill. It knows your content pillars. So, none of that requires writing code for most of it, just building skills over time, one at a time, as you notice yourself actually repeating things.
Now, let me give you another example. So, you have a founder running a midsize services company. She built her layers over about 3 weeks, the company brand project loaded with financials, the team structure, the client list, the quarterly priorities, and the sales project with her with their pricing framework and the proposal template. You then have a content project with the brand voice guidelines and six months of high performing LinkedIn posts. So there's three skills. You have the brand voice, you have the client proposal and the weekly reporting. Now Claude code is going to be set up with a Claude file on their internal tools repo. So you have calendar, you have Gmail, Slack, and HubSpot connected. Now her morning, she opens Claude and asks for a briefing. Claude, it is going to pull today's meetings. It is going to flag the one with the prospects that she has been nurturing, drafting a prep doc with the prospect's recent activity from HubSpot and the proposal terms that they discussed last time. Now, that used to take 40 minutes of tab switching. Now, it just takes one message and maybe about 5 minutes at most. So, she can ask Claude to draft a LinkedIn post about a client when Claude will write it in her voice, not because she described her voice in the prompt, but because her brand voice skill, it loaded automatically. So the first draft it is 80% there and she just tweaks two sentences and then she can post it. And then by 9:00 a.m. she's going to be completely done with work that used to fill her morning until 11:00. So she spends those two hours on those client relationships, those strategy calls and even creative thinking. So the stuff that actually is going to be growing the business and she didn't even have to learn how to code. Like she didn't have to become a prompt engineer. Nobody will. She just built layers one at a time over 3 weeks and a system from there it is going to compound for her every single day.
Now, this piece, this is what I would want somebody to tell me before I built any of my workflows on this, and I'm not going to be sugar coating any of this. So, the first one, usage limits. This is the number one pain point. Even at the highest tier, heavy users, they hit rate limits during intensive sessions. So, it will get a bit expensive, but I recommend going to the $100 or $200 minimum cloud max plan. Now, that's also token costs with the agent team. So, as I mentioned earlier, they add up very fast, so be very weary of this. So hallucinations, they're still structurally inherent in all generative AI systems. So Claude's hallucination rate. It is notably low in its coding. It's under 2%. But factual claims on obscure topics, they do need some verification. Want to not always completely trust what you're going to be getting back and just understand and take things with a grain of salt every now and then. And then there's also no image generation. So Claude, it can analyze images, but it doesn't create them. So there's no dolly equivalent or anything like that. But you can make API calls or maybe if there's an FCP server. There is way around things. Also, cowork, it is still in beta. It is not exactly refined or optimized as Claude code is and you'll run into a lot of issues like it taking a while, which means it's going to be eating up your tokens. It's just taking up all your money.
Context window management. Now, this is the hidden skill that most people do not even talk about. So, when your context is filling up quality, it degrades quickly. Even hallucinations, they're going to be increasing. The AI slap creeps in and the solution. It's just going to be disciplined context management. So, make sure you're compacting regularly, clearing between your phases, keeping your Claude.md file very lean, and using sub agents for the exploration to actually preserve your main window and hardcode known values in skills instead of just making the agent search for them every time.
So at this point, you have seen what this actually looks like when Claude stops being a chatbot and starts becoming real infrastructure inside of a business. Now, if you are a business owner and you're just thinking like, "Okay, I really want this in my company, but I want help figuring out what to implement first." Go ahead and book a free AI audit with me below. We'll look at your workflows. We'll look at your team and where AI can actually create the biggest operational wins fastest. And if you're on the other side of this where you're looking at everything in this video and thinking like, "Okay, I just want to learn how to actually turn this into an offer into a service or an AI business," make sure to register for the free webinar in the description. We'll be running it next week. We are capping seats because I want to make sure that I can answer all of your guys' questions. But in there, I'll be breaking down how to package this, how to sell it, and start building real income with AI. Both links are below. Thank you guys for watching. Hope you found some real value within this. If you did, please like, subscribe, drop a comment. Let me know what you guys thought down below. With that being said, I'll see you guys in the next.