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Summary
Core Theme
The presentation introduces "skills" as a new paradigm for extending the capabilities of AI agents, shifting focus from building monolithic agents to creating modular, reusable components that package procedural knowledge and tools.
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All right, good morning and thank you
for having us again. Last time we were
here, we're still figuring out what an
agent even is. Today, many of us are
using agents on a daily basis. But we
still notice gaps. We still have slots,
right? Agents have intelligence and
capabilities, but not always expertise
that we need for real work. I'm Barry.
This is Mahes. We created agent skills.
In this talk, we'll show you why we
stopped building agents and started
building skills instead.
A lot of things have changed since our
last talk. MCP became the standard for
agent connectivity. Cloud Code, our
first coding agent, launched to the
world and our cloud agent SDK now
provides a production ready agent out of
the box. We have a more mature ecosystem
and we're moving towards a new paradigm
for agents. That paradigm is a tighter
coupling between the model and a runtime environment.
environment.
Put simply, we think code is all we need.
need.
We used to think agents in different
domains will look very different. Each
one will need its own tools and
scaffolding and that means we'll have a
separate agent for each use case for
each domain. Well, customization is
still important for each domain. The
agent underneath is actually more
universal than we thought.
What we realized is that code is not
just a use case but the universal
interface to the digital world.
After we built cloud code, we realized
that cloud code is actually a general
purpose agent.
Think about generating a financial
report. The model can call the API to
pull in data and do research. It can
organize that data in the file system.
It can analyze it with Python and then
synthesize the insight in old file
format all through code. The core
scaffolding can suddenly become as thin
as just bash and file system which is
great and really scalable. But we very
quickly run into a different problem
and that problem is domain expertise.
Who do you want doing your taxes? Is it
going to be Mahesh, the 300 IQ
mathematical genius, or is it Barry, an
experienced tax professional, right? I
would pick Barry every time. I don't
want Mahesh to figure out the 2025 tax
code from first principles. I need
consistent execution from from a domain
expert. As agents today are a lot like
Mahes. They're brilliant, but they lack expertise.
They can do no more slow. They can do
amazing things when you really put in
the effort and give proper guidance, but
they're often missing the important
context up front. They can't really
absorb your expertise super well, and
they don't learn over time.
That's why we created agent skills.
Skills are organized collections of
files that package composable procedural
knowledge for agents.
In other words, they're folders. This
simplicity is deliberate. We want
something that anyone human or agent can
create and use as long as they have a
computer. These also work with what you
already have. You can version them in
Git, you can throw them in Google Drive
and you can zip them up and share with
your team. We have used files for uh as
a primitive for decades and we like
them. So why change now?
Because of that skills can also include
a lot of scripts as tools. Traditional
tools have pretty obvious problems. Some
tools have poorly written instructions
and are pretty ambiguous and when the
model is struggling, it can't really
make a change to the tool. So, it's just
kind of stuck with a code start problem
and they always live in the context
window. Code solves some of these
issues. It's self-documenting. It is
modifiable and can live in the file
system until they're really needed and
used. Here's an example of a script
inside of a skill. We kept seeing Claude
write the same Python script over and
over again to apply styling to slides.
So we just ask cloud to save it inside
of the skill as a tool for his version
for his future self. Now we can just run
the script and that makes everything a
lot more consistent and a lot more efficient.
At this point skills can contain a lot
of information and we want to protect
the context window so that we can fit in
hundreds of skills and make them truly
composable. That's why skills are
progressively disclosed. At runtime,
only this metadata is shown to the model
just to indicate that he has the skill.
When an agent needs to use a skill, it
can read in the rest of the skill.md,
which contains the core instruction and
directory for the rest of the folder.
Everything else is just organized for
ease of access. So that's all skills
are. They're organized folders with
scripts as tools.
Since our launch five weeks ago, this
very simple design has translated into a
very quickly growing ecosystem of
thousands of skills. And we've seen this
be split across a couple of different
types of skills. There are foundational
skills, third party skills created by
partners in the ecosystem, and skills
built within an enterprise and within teams.
teams.
To start, foundational skills are those
that give agents new general
capabilities or domain specific
capabilities that it didn't have before.
We ourselves with our launch built
document skills that give Claude the
ability to create and edit professional
quality office documents. We're also
really excited to see people like
Cadence build scientific research skills
that give Claude new capabilities like
EHR data analysis and using common
Python bioinformatics libraries better
We've also seen partners in the
ecosystem build skills that help Claude
better with their own software and their
own products. Browserbase is a pretty
good example of this. They built a skill
for their open- source browser
automation tooling, stage hand. And now
Claude equipped that this skill and with
stage hand can now go navigate the web
and use a browser more effectively to
get work done.
And notion launched a bunch of skills
that help claude better understand your
notion workspace and do deep research
And I think where I've seen the most
excitement and traction with skills is
within large enterprises. These are
company and team specific skills built
for an organization.
We've been talking to Fortune 100s that
are using skills as a way to teach
agents about their organizational best
practices and the weird and unique ways
that they use this bespoke internal software.
software.
We're also talking to really large
developer productivity teams. These are
teams serving thousands or even tens of
thousands of developers in an
organization that are using skills as a
way to deploy agents like cloud code and
teach them about code style best
practices and other ways that they want
their developers to work internally.
So all of these different types of
skills are created and consumed by
different people inside of an
organization or in the world. But what
they have in common is anyone can create
them and they give agents the new
capabilities that they didn't have before.
So, as this ecosystem has grown, we've
started to observe a couple of
interesting trends. First, skills are
starting to get more complex. The most
basic skill today can still be a
skill.md markdown file with some prompts
and some really basic instructions, but
we're starting to see skills that
package software, executables, binaries,
files, code, scripts, assets, and a lot
more. And a lot of the skills that are
being built today might take minutes or
hours to build and put into an agent.
But we think that increasingly much like
a lot of the software we use today,
these skills might take weeks or months
We're also seeing that this ecosystem of
skills is complementing the existing
ecosystem of MCP servers that was built
up over the course of this year.
Developers are using and building skills
that orchestrate workflows of multiple
MCP tools stitched together to do more
complex things with external data and
connectivity. And in these cases, MCP
MCP is providing the connection to the
outside world while skills are providing
And finally, and I think most excitingly
for me personally, is we're seeing
skills that are being built by people
that aren't technical. These are people
in functions like finance, recruiting,
accounting, legal, and a lot more. Um,
and I think this is pretty early
validation of our initial idea that
skills help people that aren't doing
coding work extend these general agents
and they make these agents more
accessible for the day-to-day of what
So tying this all together, let's talk
about how these all fit into this
emerging architecture of general agents.
First, we think this architecture is
converging on a couple of things. The
first is this agent loop that helps
manage the the model's internal context
and manages what tokens are going in and
out. And this is coupled with a runtime
environment that provides the agent with
a file system and the ability to read
and write code.
This agent, as many of us have done
throughout this year, can be connected
to MCP servers. And these are tools and
data from the outside world that make
the the agent more relevant and more effective.
effective.
And now we can give the same agent a
library of hundreds or thousands of
skills that it can decide to pull into
context only at runtime when it's
deciding to work on a particular task.
Today, giving an agent a new capability
in a new domain might just involve
equipping it with the right set of MCP
servers and the right library of skills.
And this emerging pattern of an agent
with an MCP server and a set of skills
is something that's already helping us
at Enthropic deploy Claude to new
verticals. Just after we launched skills
5 weeks ago, we immediately launched new
offerings in financial services and life
sciences. And each of these came with a
set of MCP servers and a set of skills
that immediately make Claude more
effective for professionals in each of
We're also starting to think about some
of the other open questions and areas
that we want to focus on for how skills
evolve in the future as they start to
become more complex. We really want to
support developers, enterprises, and
other skill builders by starting to
treat skills like we treat software.
This means exploring testing and
evaluation, better tooling to make sure
that these agents are loading and
triggering skills at the right time and
for the right task, and tooling to help
measure the output quality of an agent
equipped with the skill to make sure
that's on par with what the agent is
supposed to be doing.
We'd also like to focus on versioning.
as a skill evolves and the resulting
agent behavior uh evolves, we want this
to be uh clearly tracked and to have a
clear lineage over time.
And finally, we'd also like to explore
skills that can explicitly depend on and
refer to either other skills, MCP
servers, and dependencies and packages
within the agents environment. We think
that this is going to make agents a lot
more predictable in different runtime
environments. and the composability of
multiple skills together will help
agents like Claude elicit even more
complex and relevant behavior from these agents.
agents.
Overall, these set of things should
hopefully make skills easier to build
and easier to integrate into agent
Finally, a huge part of the value of
skills we think is going to come from
sharing and distribution. Barry and I
think a lot about the future of
companies that are deploying these
agents at scale. And the vision that
excites us most is one of a collecting
and collective and evolving knowledge
base of capabilities that's curated by
people and agents inside of an
organization. We think skills are a big
step towards this vision. They provide
the procedural knowledge for your agents
to do useful things. And as you interact
with an agent and give it feedback and
more institutional knowledge, it starts
to get better and all of the agents
inside your team and your org get better
as well. And when someone joins your
team and starts using Claude for the
first time, it already knows what your
team cares about. It knows about your
day-to-day and it knows about how to be
most effective for the work that you're doing.
doing.
And as this grows and this ecosystem
starts to develop even more, this was
going to this compounding value is going
to extend outside of just your organ
into the broader community. So just like
when someone else across the world
builds an MCP server that makes your
agent more useful, a skill built by
someone else in the community will help
make your own agents more capable,
reliable, and useful as well.
This vision of a evolving knowledge base
gets even more powerful when claw starts
to create these skills. We design skills
specifically as a concrete steps towards
uh continuous learning.
When you first start using cloud, this
standardized format gives a very
important guarantee. Anything that cloud
writes down can be used efficiently by a
future version of itself. This makes the
learning actually transferable.
As you build up the context skills makes
the concept of memory more tangible.
They don't capture everything. They
don't capture every type of information.
Just procedural knowledge that cloud can
use on specific tasks.
When you have worked with cloud for
quite a while, the flexibility of skills
matters even more. Cloud can acquire new
capabilities instantly, evolve them as
needed, and then drop the ones that
become obsolete. This is what we have
always known. The power of in in context
learning makes this a lot more cost-
effective for information that change on
daily basis.
Our goal is that claude on day 30 of
working with you is going to be a lot
better on cloud on day one. CL can
already create skills for you today
using our skill creator skill and we're
going to continue pushing in that direction.
We're going to conclude by comparing the
agent stack to what we have already seen computing.
computing.
In a rough analogy, models are like
processors. Both require massive
investment and contain immense
potential, but only so useful by themselves.
themselves.
Then we start building operating system.
The OS made processors far more valuable
by orchestrating the processes,
resources, and data around the
processor. In AI, we believe that agent
runtime is starting to play this role.
We're all trying to build the cleanest,
most efficient, and most scalable uh
abstractions to get the right tokens in
and out of the model.
But once we have a platform, the real
value comes from applications. A few
companies build uh processors and
operating systems, but millions of
developers like us have built software
that encoded domain expertise and our
unique points of view. We hope that
skills can help us open up this layer
for everyone. This is where we get
creative and solve concrete problem for
ourselves, for each other, and for the
world just by putting stuff in the
folder. So skills are just the starting point.
point.
To close out, we think we're now
converging on this general architecture
for general agents. We've created skills
as a new paradigm for shipping and
sharing new capabilities. So we think
it's time to stop rebuilding agents and
start building skills instead. And if
you're excited about this, come work
with us and start building some skills
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