We're witnessing a profound paradigm shift right
now. We're moving away from treating AI as a
simple chatbot. We're architecting autonomous
five -person marketing agencies. Yeah. And we're
doing it inside a single folder. Beat. Welcome
to the Deep Dive. Today, we're exploring a radically
different way to work. We're completely rethinking
human -computer interaction here. I mean, it's
not about writing one perfect prompt anymore.
It's about building a structured digital ecosystem.
If you're listening to this, you've probably
spent hours wrangling a generic AI. Oh, yeah.
You try to get it to write just one decent email,
and it usually ends up sounding... Right, and
then it starts with, you know, in today's fast
-paced digital landscape. It drives people insane.
It really does. Our mission today is highly specific.
We're going to unpack exactly how to turn Claude
Code into a repeatable marketing system. We'll
use a test case of a travel brand. The brand
is called Go Travel. We'll look at how this system
builds an entire campaign autonomously. Beat.
Okay, let's unpack this. I am incredibly ready
for this. The framework we are looking at is...
Brilliant. It completely solves the blank page
problem. Before we can build the software environment,
we have to conceptually define our employees.
Right. We're building an AI agency. So we need
to understand the fundamental difference between
a skill and an agent. This is the core rule of
the entire setup. You know, people try to automate
their entire business on day one. Yeah. They
end up with an AI that's like. aggressively replying
to their mom's emails, you have to start with
the foundational element. If we're setting up
this system, we start with skills. Exactly. A
skill is a repeatable workflow. It's a highly
specific playbook. You don't just ask the AI
to write a blog post. You create a custom blog
writing skill. Walk me through what that actually
looks like mechanically. It's a document containing
specific SEO rules. It includes your exact brand
voice guidelines. It has internal linking rules.
It even has an editing checklist. Wow. The AI
doesn't guess anymore. It reads that file and
follows your predefined process. It's like a
restaurant. The skills are the exact recipe cards.
Yeah. And the agents are the specific chefs working
the stations. That's a perfect way to visualize
it. The agents are the specialized team members.
They execute those specific recipes. You might
have a market researcher. You might have a creative
designer. They have entirely different cognitive
jobs. Hovering above all of this is something
called a clay .md file. Right. A .md or Markdown
file is a basic text document that strips away
messy formatting. Okay. This specific file acts
as the general manager. It gives the overarching
instructions. It tells the chefs which recipes
to use. I have to push back here. Sure. Why do
we need separate agents? Yeah. Why not just ask
one super smart AI to do everything at once?
Because mixing complex tasks leads to massive
logic degradation. If one chat window tries to
write content, analyze data, and build strategy
simultaneously, it loses focus. Its attention
mechanism gets scattered. I see. An agent must
have one clear, isolated responsibility. So the
market researcher only finds trends. They absolutely
do not design the ad visuals. Right. And just
like a real kitchen, if the fry cook tries to
bake the souffle, it's a disaster. Yeah. The
creative designer doesn't touch the campaign
data. You keep the roles strictly separated.
How do you actually focus the AI so it doesn't
wander off task? Well, you define strict limitations
clearly in its setup file. You tell it exactly
what it should never do. So you're essentially
building a highly controlled digital assembly
line. You are. And it works beautifully. Now
that we know our team's roles, where do these
digital chefs actually work? Right. They need
a highly organized kitchen. They need a permanent
workspace. For this setup, the source heavily
recommends using VS Code. That's Visual Studio
Code. A popular app where developers write code
and organize files. Exactly. Beat. But why use
a coding app instead of a normal browser chat
window? Because a chat window is basically a
goldfish. It forgets everything. Then this code
gives the AI spatial awareness. Cloud code can
actually read your local folders. It can see
your files, your skills, and your final outputs
all in one place. If I understand the source
correctly, we're dividing this digital workspace
into two distinct folder types. System folders
and working folder. That's the architectural
secret. Think of system folders as the AI's long
-term memory. What actually goes into those system
folders to build that memory? This is where you
put a context folder, an SOP folder, and a templates
folder. This teaches the AI about your business.
You drop in your brand voice guide. You add your
ideal customer personas. You include your current
marketing strategy. So GoTravel, our test brand.
needs the AI to deeply understand its target
demographic. Right. It needs to know the exact
tone to use for adventurous millennials. Without
that context folder, the AI just defaults to
generic corporate speak. With it, the AI sounds
like your best copywriter. I have to offer a
vulnerable admission here. Oh. I still wrestle
with prompt drift myself when staring at a blank
chat window. It's incredibly frustrating. It
happens to everyone. I mean, prompt drift is
when the AI forgets original instructions over
a long chat. By the 10th prompt, it's completely
lost the plot. But if we're using the system
folder structure, the context becomes permanent.
Exactly. Because VS Code reads that claudeu .md
file automatically in the background. It acts
as a persistent system prompt. The AI physically
cannot forget your brand. Those rules are injected
into its memory every single time you hit enter.
So if the system folders act as the AI's long
-term memory, where does it put its actual daily
work without cluttering up its brain? That's
where the working folders come in. You create
folders named ads, pages, reports, and social
creatives. Okay. This is where Claude saves the
polished outputs. And the ultimate routing manager
for all these folders remains that manager file.
Yeah. It tells the AI exactly what the project
is about. It maps out how the folders are organized.
Right. It tells the AI exactly where new files
should be saved. How do you prevent context overload
when the AI scans all these different folders?
You configure the manager file to direct the
AI only to specific context files when performing
specific tasks. So the routing file acts as a
protective cognitive filter. That's a great way
to put it. The digital workspace is built. Before
hiring the specific agents, we need to stock
the shelves with our workflows. Yes. We need
to craft the actual playbooks. The skills library.
This is my favorite part. This is where you store
your repeatable workflows so you never have to
explain a task twice. The source strongly advises
starting small here. Start with just three to
five tasks you repeat weekly. People always try
to build 50 skills on day one. It's a classic
mistake. Build one skill, test it, refine it,
make sure it works perfectly before moving on.
Let's look at a specific playbook from the source
material, the branded deck skill. This completely
changes the game for creating presentations.
Instead of getting a generic slide layout, you
teach the AI your exact corporate style. How
does an AI actually study a corporate style?
Well, you drop a finished PowerPoint template
into the templates folder. You ask Claude to
analyze its underlying structure. Okay. It reads
the XML code. It analyzes the specific hex colors.
It studies the typography and spacing rules.
It essentially creates a reverse -engineered,
detailed report on what makes your deck feel
uniquely on -brand. Exactly. Then you save that
report as a permanent skill. The next time you
need a strategy deck, the AI automatically applies
those exact design parameters. That is a massive
time saver. Huge. There's also the social creative
design skill. This one functions slightly differently.
Yeah, this one is about capturing a vibe. You
store strong visual references in an examples
holder. Then you pair it with a style guide document.
It studies the image direction and the visual
mood. It doesn't just copy a single template
rigidly. Right. But doesn't forcing the AI to
study past examples. kill its creativity and
make the output robotic. What's fascinating here
is that constraints actually breed creativity.
Yeah. When you give the AI a defined sandbox
of approved colors and moods, it stops generating
generic stock photo aesthetics. It starts combining
your specific brand elements in highly creative,
unexpected ways. Freedom within a strict framework.
Exactly. That makes a lot of sense. The AI needs
boundaries to be truly clever. How do you handle
updating these skills over time when your brand
strategy changes? You simply open the specific
skill text file, rewrite the outdated step, and
the AI instantly adopts a new process. So dynamic
skill files prevent the system from getting stale.
They make evolving your business incredibly seamless.
Our playbooks are officially written. Now we
use the slash agents command to hire the specialized
talent to read them. We're finally assembling
the AI team. For GoTravel, the test system created
five highly specific agents. First up is the
market researcher. This agent dives into audience
insights, seasonal friends, and competitor angles.
Second is the campaign strategist. Right. They
take that raw research and turn it into actionable
campaign ideas. offers and messaging. Third is
the content creator. They write the actual deliverables,
blog posts, social captions, and lead magnets.
Fourth is the creative designer. They handle
the visual direction and specific ad creatives.
And finally, the data analyst. Yeah. They read
the post campaign data and build performance
dashboards to see what worked. We mentioned this
briefly, but the golden rule here is keeping
roles completely separate. You have to avoid
functional overlap at all costs. The market researcher
structures their data strictly so the strategist
can read it. They don't try to draft the social
posts. It's like stacking Lego blocks of data.
Each agent perfectly builds on the previous one
without stepping on any toes. That's exactly
how the system avoids chaos. You use a built
-in command called slash agents to generate these
roles. Claude actively asks what the agent should
do, which tools it can use, and which skills
it has access to. And it saves a permanent agent
file inside your project. Yeah. That file defines
the specific working style for that digital employee.
You keep their system prompts incredibly simple.
You outline their main job. You list their specific
skills. Most importantly, you explicitly list
what they must never do. How do you stop agent
boundary bleed when marketing tasks naturally
overlap? Well, you clearly specify in the agent
system prompt which upstream or downstream agent
they must hand the work off to. So clear boundaries
prevent the digital chefs from constantly fighting.
Good fences make for very good AI agents. We
have the specialized team. We have the comprehensive
playbooks. Yep. But if you drop a massive project
on their desk, who grabs it first? We need routing
rules. This is the actual operating system of
the entire agency. Without routing rules, Claude
might call the wrong agent. Or it might use a
heavy slow agent when a simple skill would work
perfectly. We update the overarching manager
file with these logic rules. Right. The simplest
heuristic to use is this. Use a skill for simple
execution and formatting. Use an agent for tasks
needing judgment, deep thinking, or synthesis.
If you just need a format change from a blog
to an email, use a skill. Exactly. If you need
to figure out a controversial campaign angle,
triggers the campaign strategist agent. Let's
look at the ultimate test outlined in the source.
The Japan cherry blossom season campaign. This
is where the theory gets real. Yeah. We give
Claude one complex campaign task. Create a full
Japan cherry blossom season campaign for GoTravel.
We're asking for market research, a strategic
brief, social posts, ad creatives, and a fully
coded landing page. All from one prompt. Walk
us through how they pass the baton. Okay. First,
the manager reads the routing rules and activates
the market researcher. It crawls for current
travel trends. It discovers that travelers are
actually exhausted by crowded Kyoto spots. They
want hidden neighborhood gems. Next, the campaign
strategist takes that specific data block. Right.
It creates a strategic brief called Sakura Like
a Local. The ankle isn't just about pretty flowers
anymore. It's about a highly local, guided, less
touristy experience. Wow. The brief rigidly defines
the core message and the specific travel offer.
Then the content creator agent steps in. Yeah.
It's instructed to draft posts based only on
that specific brief. It can invent new offers.
It writes engaging posts about local neighborhood
etiquette or common mistakes eager tourists make
during the season. After that, the creative designer
activates the social creative design skill. It
references the style guide to make visuals matching
that exact local vibe. Finally, the landing page
skill uses all those assets to build the actual
site. It uses the exact same message, the approved
offer, and the foundational audience insight.
Ooh. Imagine scaling this to a billion queries.
Yeah. You could run hundreds of highly specialized
localized campaigns simultaneously while you
sleep. It's entirely possible today because every
final output is logically connected. It's not
a random quirky caption or a generic cherry blossom
image. Everything flows downstream from one centralized
strategic brief. How do you break down these
massive complex campaigns so that the system
doesn't crash halfway through? Well. You mandate
in the routing rules that the AI must output
a detailed step -by -step checklist before executing
any actual work. So forced planning prevents
the system from hallucinating wild ideas. It
anchors the AI to a logical sequence before it
writes a single word. This automated system works
beautifully in a vacuum. Sure. But how does a
human team actually interacts with it during
a chaotic Tuesday afternoon. You have to connect
it to the real world, use a Kanban board. The
source suggests using a simple Notion task board,
a standard layout. Yeah. To do. In progress.
Complete. Real marketing work doesn't happen
inside one clean, magical prompt. Tasks come
from client emails, Slack messages, everywhere.
Yeah. You need a centralized place where human
managers can drop tickets for the AI to pick
up. But you can't just write create social posts
on an ocean ticket and expect magic. Oh, that's
a recipe for disaster. The AI will just guess
the context. You need highly specific bounded
briefs. A good ticket looks like. Create five
carousel slides for first -time Japan travelers
based on the Sakura brief. The clearer the parameters
of the task, the better the AI executes the workflow.
And the source highlights a fascinating feature
here called slash remote control. This feature
is wild. It generates a secure temporary link
for your active workspace. Okay. You can open
it on your phone's browser. It lets you trigger
your local computer's quad session from anywhere
in the world. You could literally be buying coffee,
check your phone, and text your AI. Yeah. Grab
the highest priority Notion ticket and execute
it. The AI securely reads the Notion board API.
A digital bridge letting two different software
programs talk. Right. It picks up the top task.
It reads the writing rules to decide which agent
to use. It does the heavy lifting. It saves the
deliverables in the correct working folder. And
finally, it marks the Notion ticket as complete.
So what does this all mean? It means we've genuinely
moved from querying an AI chatbot. to managing
a digital co -worker pulling tickets from a queue.
It's a profound shift in daily productivity.
You aren't manually assigning tiny copy -paste
steps anymore. You're managing an intelligence
system. What happens if a human writes a notion
ticket that is just too vague for the system
to process? The AI will confidently generate
a highly generic, useless output, proving that
garbage in always equals garbage out. So vague
inputs still guarantee completely useless AI
output. Always. You absolutely cannot automate
bad management. Let's recap the big idea here.
Beep. The real shift isn't about writing better
single prompts. Yeah. Prompts engineering is
becoming obsolete. it's about building a structured
operating system by combining permanent brand
context rigid skills specialized agents and strict
routing rules you create something entirely new
you create a reliable system that can logically
plan meticulously create and organize aligned
marketing campaigns autonomously. It's a full
agency living inside a code editor. It completely
stops being a novelty toy. It becomes a functional
business engine. Which brings me to a final,
slightly philosophical thought. Two sec silence.
If a solo user can build a five -person agency
inside a code editor today. What happens in a
few years when your autonomous AI agency starts
independently communicating and negotiating with
other companies' autonomous AI agencies? That's
a wild thought. Full machine -to -machine commerce.
Your AI negotiating a sponsorship deal with my
AI. We'll leave you with that to ponder. For
now, grab a notebook today. Write down just three
repeatable weekly workflows that you can transform
into your very first AI playbooks. Start small.
Pick the repetitive tasks you absolutely hate
doing. Exactly. Thank you for joining us on this
deep dive. Stay curious. OTPO Music.