Latest / AI Fire Daily / #644 Neil: 5 AI Agent Skills I Tested For Research, Code And Design

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#644 Neil: 5 AI Agent Skills I Tested For Research, Code And Design · AI Fire Daily

0:00
Think about your daily AI workflow for a second.
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You sit down with your coffee, you open a fresh
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chat window, and then, well, you paste that massive
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five paragraph wall of instructions you use every
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single day. You explain the exact same context
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to the machine again and again. That is not real
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automation. You have not built a system. You
0:20
have just hired a very forgetful assistant. Yeah,
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exactly. It feels incredibly repetitive. You
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know, you are essentially starting from zero.
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every single morning, the cognitive load of just
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managing the AI becomes totally exhausting. Welcome
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to today's Deep Dive. We are looking at a fundamental
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shift in how we work with these models. We are
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moving away from manual prompting. Today, we
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are exploring AI agent skills. These are entirely
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reusable workflows. You plug them directly into
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your environments like chat GPT work or Claude
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code. Once they are installed, you never have
0:52
to start from zero again. And I have to make
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a vulnerable admission here. I mean, I still
0:58
wrestle with prompt drift myself. Oh, it happens
1:01
to everyone. It is incredibly common. I end up
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copy pasting the exact same instructions every
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day. Prompt drift is when the AI slowly forgets
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your rules over time. It gets incredibly tedious.
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I keep telling myself there has to be a more
1:13
permanent way to set these systems up. There
1:15
absolutely is. And today we are going to prove
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it. We are going to build a complete software
1:21
project entirely from scratch. We will use five
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specific reusable skills to do it. Five skills.
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Okay. First, we tackle market research to find
1:30
a real problem. Then we secure our code environment.
1:34
Next, we engineer the actual application. After
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that, we generate a promotional video. And finally,
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we polish the user interface. We are going from
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a blank screen to a fully launched product. Okay,
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let's unpack this. We have to start with the
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research phase, because before you build any
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project, you need to know what people actually
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care about right now. Right. Building a solution,
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looking for a problem, is the fastest way to
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fail. You need to identify real, urgent pain
2:01
points in the market. And finding those pain
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points brings up what we call the 15 open tabs
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problem. We all know that feeling intimately.
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You are trying to research a hot new tech trend.
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You have Reddit open on one monitor. You are
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scanning hacker news on the other. You are digging
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through chaotic YouTube comments. You are looking
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at GitHub repositories. You read a literal mountain
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of text. But at the end of the day, you still
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do not know what real people actually think.
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It is exhausting. It is kind of like having a
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digital ear to the ground. But the ground is
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just a million people shouting at once. You are
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trying to find authentic human opinions hidden
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inside an ocean of internet noise. So, how do
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we find those insights without spending an entire
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week reading forums? We use a skill called Last
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30 Days. It is specifically designed for chat
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GPT work. It bypasses all of that manual labor.
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It autonomously scans recent community discussions
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across all those major platforms. Does it just
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give a generic summary of the hype? No. It specifically
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targets common problems, disagreements, and actionable
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takeaways, citing exact sources. filters the
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noise, but keeps the receipts? Exactly. It does
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not just summarize the general sentiment. It
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utilizes grounded search. It pulls specific source
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citations. In a recent test, it actively analyzed
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14 massive Reddit threads and 13 Hacker News
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stories simultaneously. Whoa. So it is actively
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linking its conclusions back to the source material.
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Precisely. It separates the slick marketing hype
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from actual user complaints. During the test,
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the user asked the agent to research AI agent
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skills themselves. They wanted to know what developers
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were actually installing. They wanted to see
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the common security concerns. And most importantly,
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they asked it to find opinions that actively
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disagreed with the general hype. That is such
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a crucial filter. Finding out what people hate
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is usually much more valuable than finding out
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what they like. Oh, absolutely. The model came
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back with specific, actionable data. It flagged
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workflow friction and system overload as major
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user complaints. People are annoyed by how hard
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it is to string these complex tools together.
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It pulled out the practical takeaways. Which
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gives us our project direction. We know the market
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is frustrated with workflow friction. We know
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what to build. Which brings us to the second
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phase, security. Right. Now that we know what
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to build, we need to download specialized tools
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from GitHub to help us build it. But blindly
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downloading AI tools? is a massive risk. It is
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a huge vulnerability. When you install an AI
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agent skill, you are giving third -party code
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access to your local machine. A clean readme
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file is not enough. A high number of GitHub stars
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is absolutely not a security guarantee. You hear
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a lot about supply chain attacks these days.
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A supply chain attack is when hackers infect
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legitimate software you trust. That is a perfect
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definition. It is like hiring a trusted caterer
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for a party, but their ingredient supplier poisoned
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the flower. The caterer does not know, and neither
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do you. Malicious code can hide very deep within
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seemingly popular repositories. Yeah. You are
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giving an AI agent permission to act on your
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behalf. You need to know exactly what it is allowed
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to touch. So how do we lock the front door before
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we start building? We use a tool called SkillSpectre.
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It was developed by NVIDIA. And it is a plugin
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designed specifically for Claude Cowork. SkillSpectre
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acts as your digital security guard. Okay. It
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reviews a skills GitHub repo before you even
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hit the install button. How does it actually
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work under the hood? Does it just read the code
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and guess if it is safe? It performs what is
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called static analysis. You just paste the GitHub
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repository URL into the chat. The agent scans
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the entire project structure without actually
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running the code. It meticulously maps out the
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dependencies. Let's quickly define dependencies
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for clarity. Dependencies, extra external code
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needed to run a program. Spot on. So it looks
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at those external files. Instead of just checking
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the basic instructions, it actively monitors
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things like network access and credential handling.
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It ensures your private data is not leaking.
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It checks if the code is trying to read your
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local files. I have to calmly challenge the necessity
6:19
here. I mean, isn't a clean README file and a
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lot of GitHub stars enough of a safety check?
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Traditional virus scanners look for known bad
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files. But AI tools are dynamic. They execute
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scripts on the fly. A script that quietly copies
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your API keys and sends them to an external server
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might not look like a traditional virus. You
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need a tool that understands the intent of the
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code. The testing process is very revealing.
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The user asked Skillshare to review a complex
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engineering skill called superpowers. Right.
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And it did not just return a simple yes or no.
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It returned a specific caution verdict. That
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is so interesting. It didn't find anything explicitly
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malicious. But... It flags specific behaviors
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for a manual review. It highlighted the exact
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files that interact with the local system. It
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showed which instructions caused the risk. It
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acts as an intelligent starting point for your
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own security review. What exactly triggers it
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to issue a caution? It flags any hidden scripts,
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external dependencies, or anything that interacts
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with your local files or network. Basically,
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it flags anything that talks to the outside world.
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Precisely. Okay, our environment is vetted. It
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is secure. It is finally time to actually write
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the software. We are moving to the foundation
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phase. This is where things get incredibly powerful.
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With our environment secured, we can start engineering.
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Coding agents are amazing at writing code quickly,
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but relying on single line prompts usually leads
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to chaotic code bases. Oh, absolutely. If you
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try to dictate every single step, you end up
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manually managing the entire process. Planning,
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testing, and debugging usually require separate
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manual instructions. It feels like dictating
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every single keystroke to an intern. You spend
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more time managing them than you would have spent
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just writing the code yourself. It should be
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more like stacking Lego blocks of data with a
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lead engineer. You want to collaborate on architecture,
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not micromanage the syntax. That is exactly the
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friction point. But the superpowers skill collection
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for cloud code changes that dynamic entirely.
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It is not for simple one line requests. It manages
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the full software development lifecycle. How
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is it different from just asking a standard chat
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bot to write a web app? It handles the architecture
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brainstorming. It manages the actual implementation.
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It runs the automated testing. It even handles
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bug fixing autonomously. It is the difference
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between dictating to a junior intern versus handing
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a blueprint to a master builder and walking away.
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Let's look at the test scenario. They asked Cloud
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Code to build a simple task management web app
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to solve that workflow friction we found earlier.
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It needed titles, owners, due dates, and statuses.
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It needed filtering capabilities. And it needed
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a clean interface for desktop and mobile. What
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did the agent actually do first? Well, first,
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it did not write a single line of code. It stopped
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and reviewed the requirements. Then it created
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a formal step -by -step implementation plan.
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That methodical thinking step is so crucial.
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Large language models need to break complex tasks
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down before executing them. It shows genuine
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architectural planning. After the plan was set,
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it built the application locally. But here is
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the fascinating part. It didn't just hand the
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code back and say it was done. It autonomously
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spun up a testing environment. Really? Yeah.
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It tested the main user flows on both simulated
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desktop and mobile screens. Wait, it tested its
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own work visually? Yes. And during its own testing,
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it caught a layout issue. It found the bug, it
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analyzed the root cause, and it fixed it autonomously.
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All of this happened locally on the machine.
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Nothing was pushed to the cloud. That autonomous
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workflow is what matters most. Is this full workflow
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too heavy for just tweaking a small feature?
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Yes, if you're making a tiny change, the extensive
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planning and testing steps become unnecessary
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overhead. Use it for building houses, not for
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painting a single wall. That is a great way to
10:17
think about it. You want to match the tool's
10:18
complexity to the task's actual scope. Sponsor.
10:22
This deep dive is supported by our partners.
10:24
If you are looking to streamline your own digital
10:26
workflows, consider exploring tools that integrate
10:29
seamlessly with your daily tasks. Finding the
10:32
right platform can completely transform how your
10:34
team manages data and scales operations. All
10:37
right, let's jump right back into the deep dive.
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So we have a working task management app. The
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code is solid. The tests have passed. But a great
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app sitting on your hard drive is useless if
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nobody knows about it. We need to explain this
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app to the world. We need video marketing. Video
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is essential. But jumping into complex video
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editing software can take weeks. This brings
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us to the fourth skill, bringing the project
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to life with a tool called Remotion. Remotion
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is another skill for Claude Code. It approaches
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video creation from a completely programmatic
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angle. It creates videos using React code rather
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than dragging keyframes. React code is just a
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popular language for building user interfaces.
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Meaning it writes software code to generate a
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video. Exactly. You are not manually scrubbing
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a timeline or dragging layers around. If you
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have ever tried to manually align keyframes in
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an editing bay, you know the pain. It is incredibly
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tedious pixel pushing work. Remotion allows the
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AI agent to build motion graphics mathematically.
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It builds data visuals and captions entirely
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through code. Whoa. Imagine generating thousands
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of custom motion graphics just by tweaking a
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line of code. You could personalize a promo video
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for every single user. That completely changes
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the scale of content creation. It allows for
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infinite scale and perfect brand consistency.
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For the test, they asked the agent to create
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a 15 second motion graphic promo for our new
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app. They wanted an AI assistant icon in the
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center. They wanted five feature icons appearing
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around it. The instructions were very specific.
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They requested smooth animated lines connecting
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the icons. They wanted clean typography and a
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minimal background. And the agent planned the
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animation sequence first. It established the
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scene timing. It programmed the mathematical
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curves for the main transitions. Then it rendered
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the frames locally. And just like the engineering
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agent, it reviewed its own work. It actually
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found a few layout problems during the review
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phase. Right. The research text label was physically
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hitting the main title. The bottom labels were
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sitting too close to the edge of the screen.
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The agent caught these specific visual issues.
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It adjusted the code variables. It fixed the
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spacing before executing the final render. That
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self -correction loop is just brilliant. It really
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is. Why use a coding agent for video instead
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of a standard AI video generator? Because it
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gives you perfect repeatability, precise timing,
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and exact layout control for motion graphics.
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It gives you exact control over timing, unlike
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generative video. Code -driven video gives you
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absolute mathematical control. Which brings us
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to the final phase. The lodging of our app functions
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perfectly. The promotional video is done, but
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there is still one lingering problem when you
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actually open the application. The user interface.
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It probably looks like a generic AI -generated
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template. We have all seen that exact look. Oh,
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absolutely. The buttons are weirdly spaced, the
13:31
fonts are boring, the colors are perfectly symmetrical,
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but somehow lifeless? It is functionally fine,
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but visually it feels like a high school project.
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It lacks a human touch. It lacks taste. And that
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is exactly what the fifth skill addresses. It
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is a cloud code plugin called Craft. Some developers
13:49
also refer to it as impeccable. We are moving
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from hard logic to pure aesthetics here. Craft
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focuses entirely on visual polish. It improves
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typography. It refines subtle animations. Crucially,
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it removes those obvious AI design patterns.
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It is important to understand how this specific
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tool is used. You do not use Kraft to build an
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interface from scratch. You only apply it to
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a front end that already works. It is exactly
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like staging a house before a sale. The plumbing
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works. The walls are up. The foundation is solid.
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But now you need to bring in the right furniture
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and lighting so it actually looks beautiful and
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inviting to a buyer. That is a perfect analogy.
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And Kraft has very specific, focused commands
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to act as that interior designer. You can type
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slash critique for a full user experience review.
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You can use slash typeset to fix awkward font
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pairings. You can use slash arrange to balance
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the layout perfectly. My absolute favorite command
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from this section is slash anti -slop. It is
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a brilliant feature. It actively scans the code
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and mathematically removes those generic clunky
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layout choices that Scream and AI made this.
15:01
It adjusts the visual hierarchy. Let's define
15:03
visual hierarchy quickly. Visual hierarchy, arranging
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design elements to show their level of importance.
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Right. Making sure the user's eye naturally goes
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to the most critical action button first, rather
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than getting lost in a sea of equally sized text.
15:18
During the test, the user fed craft the working
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front end of our task app. They told it to keep
15:23
the core structure completely intact. They just
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wanted improvements on that visual hierarchy,
15:27
the spacing, and the responsive behavior. The
15:30
plugin started with a short written critique.
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Then it applied the visual improvements directly
15:34
to the React code. It is that simple. Does running
15:37
these commands risk breaking the core functionality
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we just built? No, you instruct it to keep all
15:42
existing features working and purely focus on
15:44
visual hierarchy and spacing. It changes the
15:47
paint, but leaves the plumbing alone. Exactly.
15:50
We've covered a massive amount of ground today.
15:52
We walked through an entire software build. Let's
15:55
synthesize all of this. What is the overarching
15:58
theme here? AI agent skills act as a crucial
16:02
bridge. They move AI from being a neat novelty,
16:05
a chatbot you bounce ideas off of, to being a
16:08
reliable, professional tool that executes complex,
16:11
multi -step workflows. But the secret is not
16:14
hoarding plugins. You do not need to install
16:16
every single skill you can find on GitHub. That
16:18
just creates more management overhead for yourself.
16:21
The real power lies in targeted application.
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You need to pick the one skill that maps directly
16:26
to a workflow you already repeat constantly.
16:29
Exactly. If you spend hours reading community
16:32
trends, install less 30 days. If you constantly
16:35
worry about code vulnerabilities, use Skillshare.
16:38
If you manage complex engineering projects, leverage
16:41
superpowers. If you need precise marketing animations,
16:45
use Remotion. If your interfaces look generic,
16:48
apply craft. It is about matching the exact tool
16:52
to the exact friction point in your day. This
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entire workflow raises a massive question. We
16:57
are looking at a future that is arriving very
17:00
quickly. If AI agents can now autonomously plan
17:03
software architectures, if they can secure the
17:06
code, if they can build it, debug it, animate
17:08
the marketing, and polish the design, all based
17:11
on real -time market data, what happens to the
17:14
concept of a startup? That is a staggering thought.
17:17
When the friction of creating high -quality software
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drops to near zero, does the value shift entirely
17:22
away from the code itself? does it shift entirely
17:25
to the taste of the person guiding the agent?
17:29
We want you to really think about that. But for
17:32
now, we want you to try this practically. Pick
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just one repetitive task you do this week. Just
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one. Install a single AI agent skill to handle
17:41
it. See if it genuinely saves you time. Stop
17:45
explaining your exact workflow from scratch every
17:47
single morning. It is time to fire that forgetful
17:50
assistant. Thanks for joining us on this deep
17:52
dive. We will catch you on the next one.