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Language: en
You're probably using Claude for basic
chats and maybe some coding help. But if
that's all you're doing, you're missing
out on what might be the biggest AI
productivity breakthrough of 2025. I've
spent the last few days diving deep into
agent skills since Anthropic just
dropped them, analyzing the technical
documentation and running through every
example I could find. And here's what
shocked me. This isn't just another AI
feature update. It's fundamentally
changing how AI agents work. and most
people don't even know it exists yet.
Welcome back to bitbias.ai where we do
the research so you don't have to. So,
in this video, I'll break down exactly
what agent skills are, show you how
they're already helping companies like
Recruitin/ their workload from days to
hours and reveal why this approach is
completely different from what OpenAI
and Google are doing. We'll explore the
examples Anthropic has shared, dive into
the architecture that makes this
possible, and I'll show you how to start
using skills yourself.
First up, let's understand what agent
skills actually are. Because once you
see this in action, you'll never use AI
the same way again.
What agent skills really are.
Imagine having an AI assistant that
comes with a built-in manual for each
task. That's essentially what Anthropic
just delivered with agent skills.
But here's where it gets interesting.
These aren't just prompts or templates.
Each skill is like a folder of expertise
that Claude can load on demand,
containing everything from step-by-step
instructions to actual executable code.
Think of it this way. Normally, when you
ask Claude to help with something
complex, like processing a PDF or
creating a PowerPoint, you'd have to
explain exactly what you want every
single time.
With agent skills, it's like Claude
suddenly has an onboarding guide for
that specific task. The model discovers
what skills are available, loads the
relevant one, and gains new capabilities
without you having to write a novel
length prompt. What makes this
particularly powerful is that a skill
can pack everything, company guidelines,
code tools, data schemas, essentially
turning Claude from a generalist into a
specialist for whatever domain you need.
And unlike traditional plugins that just
add API connections, skills include
actual executable scripts. So when
Claude needs to extract data from a PDF,
it doesn't guess or hallucinate. It runs
real code and gets exact results.
The key characteristics that make skills
special,
they're composable, meaning Claude can
automatically identify and orchestrate
multiple skills together. They're
portable, working everywhere. Claude.ai,
AI claude code even through the API.
They're efficient using something called
progressive loading that I'll explain in
a moment. And they're powerful combining
language understanding with actual code
execution.
The genius architecture behind agent
skills.
Now, this is where things get really
clever and understanding this will help
you see why skills are such a
breakthrough. Under the hood, Claude
runs in a sandboxed virtual machine with
a full file system and coding tools.
Each agent skill is just a directory on
this virtual machine. But the magic is
in when and how Claude loads content
from that directory. Here's the
brilliant part, and stick with me
because this is important. Skills use
what Anthropic calls progressive
disclosure. It works in three layers.
First, at startup, Claude only gets the
metadata, just the name and description
of each skill.
This is tiny, maybe a few dozen tokens
per skill, so you can have dozens of
skills available without bloating the
context. When you ask Claude to do
something, it checks if any skill might
help. If it finds one, it literally runs
a command to read the main skill file
into the chat.
But wait, it gets better. If those
instructions reference other files like
additional guides or Python scripts,
Claude only loads those if needed. And
here's the kicker. When it runs code,
only the output comes back to the model,
not the entire script. Let me give you a
concrete example to show why this
matters.
Imagine a PDF processing skill. The
skill folder might contain detailed
instructions, form filling guidelines,
and Python extraction scripts.
If you just ask Claude to summarize this
PDF, it loads the main instructions,
skips the form filling guide entirely,
and runs just the extraction script.
The entire PDF never enters the chat
context. Only the extracted text does.
This progressive loading strategy means
Claude's context window only contains
exactly what's needed for the job, while
an arbitrarily large knowledge base sits
ready on disk. Unused files consume zero
tokens. It's like having an infinite
manual where Claude only reads the
chapters it needs.
Real world applications that are already
working.
Okay, so this all sounds impressive in
theory, but what about practice?
Let me share some examples that honestly
blew my mind when I first saw them.
Rakuten, you know, the massive
e-commerce company. They've created a
custom accounting skill that follows
their exact procedures. Tasks that used
to take their team a full day are now
done in about an hour. That's not a
typo. We're talking about 8 hours of
work compressed into 60 minutes.
Their skill handles spreadsheets,
generates reports, and follows their
specific accounting workflows perfectly
every time. Box is using skills to
bridge Claude with their file storage
system. But here's the cool part.
Users can ask Claude to turn stored
documents into branded PowerPoints or
Excel files, and the skill automatically
applies Box's brand guidelines. No more
make sure to use our company colors in
every prompt. It just knows. Notion
built skills that let Claude query and
update notion pages directly. So when
someone asks, "Get action items from
these meeting notes."
Claude doesn't just tell you what they
are. It can actually create tasks in
your notion workspace.
Canvas planning something similar for
design work where you describe an image
and Claude produces a Canva template
following brand guidelines
automatically.
But wait until you see what individual
developers are doing. One person created
what they call a data journalism agent
by combining skills for fetching census
data, loading it into databases and
creating visualizations. You literally
say, "Analyze these sales figures and
make a slide deck." And Claude
autonomously loads the data analysis
skill, processes the numbers, loads the
PowerPoint skill, creates the slides,
and even applies brand guidelines if you
have that skill, too. It's like having
an entire team in one AI assistant.
The point is, any multi-step workflow
that benefits from structured steps or
external tools is a candidate for
skills.
Marketing teams are encoding brand
guides. Analysts are bundling SQL
schemas with Python scripts. And since
skills are just files, they're easy to
version control and share with
colleagues.
How agent skills compare to what
everyone else is doing.
Now, you might be thinking, okay, but
OpenAI has plugins, Google has agent
builders. How is this different?
This is actually where things get really
interesting because the approach
Anthropic took is fundamentally
different from everyone else. First,
let's talk about the execution
environment versus API calls. OpenAI's
workflows rely on external API
connections. Claude skills run entirely
inside Claude's own environment with
direct file system access and native
code execution. This might sound like a
technical detail, but it changes
everything.
When a skill needs to process data, it
runs actual Python scripts and gets
exact results, not API responses that
might fail or time out.
Then there's the difference between
loading and prompting. With Chat GPT's
custom GPTs, you're often carefully
crafting prompts or configuring UI
settings. Skills, by contrast, package
the prompt, plus all the tooling in
plain files. Claude only needs to know
where a skill is relevant. It then pulls
in all instructions automatically.
No more handcrafting prompts for every
interaction. Here's what really sets
Skills apart. Composability.
While Open AI's agent kit uses visual
workflows and SDKs, skills are textbased
and inherently composable.
You can combine skill folders at will.
In fact, the format is so generic that
you could theoretically point other AI
agents at the same skill directory and
they'd work. These aren't claude
specific. They're model agnostic
building blocks.
The governance approach is different
too.
Anthropic design skills with enterprise
control in mind. Only users or admins in
pro team or enterprise plans can add
custom skills and they emphasize
installing only from trusted sources.
Since skills are just files, companies
can audit exactly what the agent will
do. Compare that to the blackbox nature
of some API integrations. And then
there's the cost and architecture
consideration.
Claude skills are just a feature on top
of existing Claude models. There's no
new pricing tier. You just pay for the
model usage. But perhaps most
importantly, Skills give developers a
straightforward file-based toolkit.
While chat GPT plugins give you extra
APIs and OpenAI agent kit gives you
visual orchestration, skills give you
something simpler and more powerful. A
mini program that the AI follows on its
own machine.
What this means for the future of AI
agent skills
point to a clear trend that's honestly
exciting to watch unfold.
AI models are evolving from static Q and
A systems into true agents that combine
language with action
by allowing structured knowledge to be
attached as needed. We're moving away
from chat with AI toward AI that
actually does things.
But here's where it gets wild, and this
is on Anthropic's actual road map. They
envision agents that can build their own
skills.
Imagine Claude noticing you do the same
task repeatedly, then autonomously
writing the skill file for it and saving
it for future use.
This self-improvement loop could
accelerate AI adoption exponentially.
Organizations would gradually accumulate
libraries of proven skills like app
stores but for AI capabilities.
Developers are already brainstorming
what's possible and some of these ideas
are mind-blowing.
One commentator pointed out that with a
few markdown files and scripts, you
could assemble an entire specialized
agent for any domain.
We're talking about a combinatorial
explosion of capabilities that might
dwarf previous AI trends.
Someone even joked that the skills
revolution will make last year's hype
about AI memory look pedestrian by
comparison.
Of course, with great power comes
responsibility.
Since skills can execute code, security
is paramount. We'll likely see standards
emerge. signed skill bundles, sandbox
profiles, careful vetting processes.
Companies will build private skill
repositories and audit external
additions carefully.
But perhaps most fascinating is how
skills blur the line between software
and AI.
Skills are essentially many applications
that any agent with a tools interface
can use. They're just markdown plus ym
plus optional scripts. intentionally
simple so they're easy to write, share,
and iterate on.
We're already seeing communities share
skills on GitHub. Each one unlocking new
intelligence for Claude or potentially
other LLMs, getting started and what you
need to know. So, how do you actually
start using this? If you're on Claude's
Pro team or enterprise plan, you already
have access to pre-built skills for
common tasks like spreadsheet
generation, presentation creation, and
PDF processing. Just look for the skills
option in your clawed interface and
enable the ones you need. For developers
and technical teams, the real power
comes from building custom skills.
Remember, a skill is just a folder with
a skylmd
file containing ym front matter for the
name and description plus any supporting
documents or scripts you need.
You can version control them, share them
with your team, and even adapt skills
others have created.
The security consideration is crucial
though. Only install skills from sources
you trust. And if you're building them,
audit any code carefully.
Think of skills like browser extensions.
Powerful but requiring trust. Here's my
prediction. Within the next year, we'll
see entire marketplaces of skills
emerge.
Teams will share industry specific
skills.
Consultants will package their expertise
into skills. And every company will have
their own private skill library encoding
their unique workflows and knowledge.
Anthropics agent skills represent
something bigger than just another AI
feature. They're showing us a path
toward AI assistants that actually work
like smart colleagues rather than
glorified chat bots.
By modularizing expertise into simple
files, Claude can now behave like a
specialist in any domain you need,
switching contexts and capabilities on
the fly.
For anyone working with AI, this is a
development you can't afford to ignore.
Whether you're automating workflows,
building AI products, or just trying to
be more productive, skills are changing
the game. And the best part,
we're just scratching the surface of
what's possible. What kind of skill
would revolutionize your workflow? Drop
a comment below. I'm genuinely curious
what domains people want to see skills
for. And if you found this breakdown
helpful, you know what to do.
I'll be diving deep into building custom
skills in my next video, so make sure
you're subscribed for that.
Until then, go experiment with agent
skills. Trust me, once you see what they
can do, there's no going back to basic
AI chat.