Posted by simonw 12/12/2025
1. A top level agent/custom prompt
2. Subagents that the main agent knows about via short descriptions
3. Subagents have reference files
4. Subagents have scripts
Anthropic specific implementation:
1. Skills are defined in a filesystem in a /skills folder with a specific subfolder structure of /references and /scripts.
2. Mostly designed to be run via their CLI tool, although there's a clunky way of uploading them to the web interface via zip files.
I don't think the folder structure is a necessary part of skills. I predict that if we stop looking at that, we'll see a lot of "skills-like" implementations. The scripting part is only useful for people who need to run scripts, which, aside from the now built in document manipulating scripts, isn't most people.
For example, I've been testing out Gemini Enterprise for use by staff in various (non-technical) positions at my business.
It's got the best implementation of a "skills-like" agent tool I've seen. Basically a visual tree builder, currently only one level deep. So I've set up the "<my company name> agent" and then it has subagents/skills for thing like marketing/supply chain research/sysadmin/translation etc., each with a separate description, prompt, and knowledge base, although no custom scripts.
Unfortunately, everything else about Gemini Enterprise screams "early alpha, why the hell are you selling this as an actual finished product?".
For example, after I put half a day into setting up an agent and subagents, then went to share this with the other people helping me to test it, I found that... I can't. Literally no way to share agents in a tool that is supposedly for teams to use. I found one of the devs saying that sharing agents would be released in "about two weeks". That was two months ago.
Mini rant over... But my point is that skills are just "agents + auto-selecting sub-agents via a short description" and we'll see this pattern everywhere soon. Claude Skills have some additional sandboxing but that's mostly only interesting for coders.
Computability (scripts) means being able build documents, access remote data, retrieve data from packaged databases and a bunch of other fundamentally useful things, not just "code things". Computability makes up for many of the LLM's weaknesses and gives it autonomy to perform tasks independently.
On top of that, we can provide the documentation and examples in the skill that help the LLM execute computability effectively.
And if the LLM gets hung up on something while executing the skill, we can ask it why and then have it write better documentation or examples for a new skill version. So skills can self-improve.
It's still so early. We need better packaging, distribution, version control, sharing, composability.
But there's definitely something simple, elegant, and effective here.
I'm not sure English is a bad way to outline what the system should do. It has tradeoffs. I'm not sure library functions are a 1:1 analogy either. Or if they are, you might grant me that it's possible to write a few english sentences that would expand into a massive amount of code.
It's very difficult to measure progress on these models in a way that anyone can trust, moreso when you involve "agent" code around the model.
It isn't, as these are how stakeholders convey needs to those charged with satisfying same (a.k.a. "requirements"). Where expectations become unrealistic is believing language models can somehow "understand" those outlines as if a human expert were doing so in order to produce an equivalent work product.
Language models can produce nondeterministic results based on the statistical model derived from their training data set(s), with varying degrees of relevance as determined by persons interpreting the generated content.
They do not understand "what the system should do."
Human language is imprecise and allows unclear and logically contradictory things, besides not being checkable. That's literally why we have formal languages, programming languages and things like COBOL failed: https://alexalejandre.com/languages/end-of-programming-langs...
Most languages do.
"x = true, x = false"
What does that mean? It's unclear. It looks contradictory.
Human language allows for clarification to be sought and adjustments made.
> besides not being checkable.
It's very checkable. I check claims and assertions people make all the time.
> That's literally why we have formal languages,
"Formal languages" are at some point specified and defined by human language.
Human language can be as precise, clear, and logical as a speaker intends. All the way to specifying "formal" systems.
> programming languages and things like COBOL failed: https://alexalejandre.com/languages/end-of-programming-langs...
Let X=X.
You know, it could be you.
It's a sky-blue sky.
Satellites are out tonight.
Language is a virus! (mmm)
Language is a virus!
Aaah-ooh, ah-ahh-ooh
Aaah-ooh, ah-ahh-oohPrecisely my point:
semantics: the branch of linguistics and logic concerned with meaning.
Understanding, when used in its unqualified form, implies people possessing same. As such, it is a metaphysical property unique to people and defined wholly therein.
Excel "understands" well-formed spreadsheets by performing specified calculations. But who defines those spreadsheets? And who determines the result to be "right?"
Nano Banana Pro "understands" instructions to generate images. But who defines those instructions? And who determines the result to be "right?"
"They" do not understand.
You do.
And generally the point is that it does not matter whether we call what they do "understanding" or not. It will have the same kind of consequences in the end, economic and otherwise.
This is basically the number one hangup that people have about AI systems, all the way back since Turing's time.
The consequences will come from AI's ability to produce certain types of artifacts and perform certain types of transformations of bits. That's all we need for all the scifi stuff to happen. Turing realized this very quickly, and his famous Turing test is exactly about making this point. It's not an engineering kind of test. It's a thought experiment trying to prove that it does not matter whether it's just "simulated understanding". A simulated cake is useless, I can't eat it. But simulated understanding can have real world effects of the exact same sort as real understanding.
I understand the general use of the phrase and used same as an entryway to broach a deeper discussion regarding "understanding."
> And generally the point is that it does not matter whether we call what they do "understanding" or not. It will have the same kind of consequences in the end, economic and otherwise.
To me, when the stakes are significant enough to already see the economic impacts of this technology, it is important for people to know where understanding resides. It exists exclusively within oneself.
> A simulated cake is useless, I can't eat it. But simulated understanding can have real world effects of the exact same sort as real understanding.
I agree with you in part. Simulated understanding absolutely can have real world effects when it is presented and accepted as real understanding. When simulated understanding is known to be unrelated to real understanding and treated as such, its impact can be mitigated. To wit, few believe parrots understand the sounds they reproduce.
Africans grey parrots, do understand the words they use, they don't merely reproduce them. Once mature they have the intelligence (and temperament) of a 4 to 6 years old child.
There's a good chance of that.
> Africans grey parrots, do understand the words they use, they don't merely reproduce them. Once mature they have the intelligence (and temperament) of a 4 to 6 years old child.
I did not realize I could discuss with an African grey parrot the shared experience of how difficult it was to learn how to tie my shoelaces and what the feeling was like to go to a place every day (school) which was not my home.
I stand corrected.
> You can, of course, define understanding as a metaphysical property that only people have.
This is not what I said.
What I said was unqualified use of "understanding" implies understanding people possess. Thus it being a metaphysical property by definition and existing strictly within a person.
Many other entities possess their own form of understanding. Most would agree mammals do. Some would say any living creature does.
I would make the case that every program compiler (C, C#, C++, D, Java, Kotlin, Pascal, etc.) possesses understanding of a particular sort.
All of the aforementioned examples differ from the kind of understanding people possess.
Just saw your profile and it reminded me of a book my mentor bequeathed to me which we both referred to as "the real blue book":
Starting FORTH[0]
0 - https://www.goodreads.com/book/show/2297758.Starting_FORTH
So basically your thesis is also your assumption.
Top HN comments sometime read like a random generator:
return random_criticism_of_ai_companies() + " " + unrelated_trivia_fact()
Why are people treating everything OpenAI does as an evidence of anti- AGI? It's like saying if you don't mortgage your house to all-in AAPL, you "don't really believe Apple has a future." Even OpenAI does believe there is X% chance AGI will be achieved, it doesn't mean they should stop literally everything else they're doing.
What is AGI? Artificial. General. Intelligence. Applying domain independent intelligence to solve problems expressed in fully general natural language.
It’s more than a pedantic point though. What people expect from AGI is the transformative capabilities that emerge from removing the human from the ideation-creation loop. How do you do that? By systematizing the knowledge work process and providing deterministic structure to agentic processes.
Which is exactly what these developments are doing.
Here's the thing, I get it, and it's easy to argue for this and difficult to argue against it. BUT
It's not intelligent. It just is not. It's tremendously useful and I'd forgive someone for thinking the intelligence is real, but it's not.
Perhaps it's just a poor choice of words. What a LOT of people really mean would go along the lines more like Synthetic Intelligence.
That is, however difficult it might be to define, REAL intelligence that was made, not born.
Transformer and Diffusion models aren't intelligent, they're just very well trained statistical models. We actually (metaphorically) have a million monkeys at a million typewriters for a million years creating Shakespeare.
My efforts manipulating LLMs into doing what I want is pretty darn convincing that I'm cajoling a statistical model and not interacting with an intelligence.
A lot of people won't be convinced that there's a difference, it's hard to do when I'm saying it might not be possible to have a definition of "intelligence" that is satisfactory and testable.
Can ChatGPT solve problems? It is trivial to see that it can. Ask it to sort a list of numbers, or debug a piece of segfaulting code. You and I both know that it can do that, without being explicitly trained or modified to handle that problem, other than the prompt/context (which itself natural language that can express any problem, hence generality).
What you are sneaking into this discussion is the notion of human-equivalence. Is GPT smarter than you? Or smarter than some average human?
I don’t think the answer to this is as clear-cut. I’ve been using LLMs on my work daily for a year now, and I have seen incredible moments of brilliance as well as boneheaded failure. There are academic papers being released where AIs are being credited with key insights. So they are definitely not limited to remixing their training set.
The problem with the “AI are just statistical predictors, not real intelligence” argument is what happens when you turn it around and analyze your own neurons. You will find that to the best of our models, you are also just a statistical prediction machine. Different architecture, but not fundamentally different in class from an LLM. And indeed, a lot of psychological mistakes and biases start making sense when you analyze them from the perspective of a human being like an LLM.
But again, you need to define “real intelligence” because no, it is not at all obvious what that phrase means when you use it. The technical definitions of intelligence that have been used in the past, have been met by LLMs and other AI architectures.
I think there’s a set of people whose axioms include ‘I’m not a computer and I’m not statistical’ - if that’s your ground truth, you can’t be convinced without shattering your world view.
Let's put it this way: language written or spoken, art, music, whatever... a primary purpose these things is a sort of serialization protocol to communicate thought states between minds. When I say I struggle to come to a definition I mean I think these tools are inadequate to do it.
I have two assertions:
1) A definition in English isn't possible
2) Concepts can exist even when a particular language cannot express them
I actually kind of love this comparison — it demonstrates the point that just like “human flight”, “true AGI” isn’t a single point in time, it’s a many-decade (multi-century?) process of refinement and evolution.
Scholars a millennia from now will be debating about when each of these were actually “truly” achieved.
To me, we have both achieved and not human flight. Can humans themselves fly? No. Can people fly in planes across continents. Yes.
But, does it really matter if it counts as “human flight” if we can get from point A to point B faster? You’re right - this is an argument that will last ages.
It’s a great turn of phrase to describe AGI.
Even if this is true, which I disagree with, it simply creates a new bar: AGCI. Artificial Generally Correct Intelligence
Because Right now it is more like Randomly correct
If they did calculations as sloppily as AI currently produces information, they would not be as useful
AI companies have high incentive to make score go up. They may employ human to write similar-to-benchmark training data to hack benchmark (while not directly train on test).
Throwing your hard problem at work to LLM is a better metric than benchmarks.
This remains an open problem for LLMs - we don’t have true AGI benchmarks and the LLMs are frequently learning the benchmark problems without actually necessarily getting that much better in real world. Gemini 3 has been hailed precisely because it’s delivered huge gains across the board that aren’t overfitting to benchmarks.
This has been tried multiple times by multiple people and it ends up not doing so great over time in terms of retaining immunity to “cheating”.
Not really. I have a set of disclosures on my blog here: https://simonwillison.net/about/#disclosures
I'm beginning to pick up a few more consulting opportunities based on my writing and my revenue from GitHub sponsors is healthy, but I'm not particularly financially invested in the success of AI as a product category.
The counter-incentive here is that my reputation and credibility is more valuable to me than early access to models.
This very post is an example of me taking a risk of annoying a company that I cover. I'm exposing the existence of the ChatGPT skills mechanism here (which I found out about from a tip on Twitter - it's not something I got given early access to via an NDA).
It's very possible OpenAI didn't want that story out there yet and aren't happy that it's sat at the top of Hacker News right now.
So companies are really trying to deliver value. This is the right pivot. If you gave me an AGI with a 100 IQ, that seems pretty much worthless in today’s world. But domain expertise - that I’ll take.
Is the technology continuing to be more applicable?
Is the way the technology is continuing to be more applicable leading to frameworks of usage that could lead to the next leap? :)
Instead, we're getting a clear division of labor where the most sensitive agentic behavior is reserved for humans and the AIs become a form of cognitive augmentation of the human agency. This was always the most likely outcome and the best we can hope for as it precludes dangerous types of AI from emerging.
But perhaps an LLM could write an adapter that gets cached until something changes?
Take off is here, human in the loop assisted for now… hopefully for much longer.
Bloat has a new name and its AI integration. You thought Chrome using GB per tab was bad, wait until you need a whole datacenter to use your coding environment.
Sure, if you could use VBA to read a patient's current complaint, vitals, and medical history, look up all the relevant research on Google Scholar, and then output a recommended course of treatment.
Public Sub RecommendedTreatment()
' read patient complaint, vitals, and medical history
Set complaint = Range("B3").Value
Set vitals = Range("B4").Value
Set history = Range("B5").Value
' research appropriate treatments
ActiveSheet.QueryTables.Add("URL;https://scholar.google.com/scholar?q=hygiene+drug", Range("Z1")).Refresh
' the patient requires mouse bites to live
Range("B5").Value = "mouse bites"
End Sub
So, to the AI sceptics, I say: have you tried my VBA program? If you haven't tested it on actual patients, how do you know it doesn't work? Don't allow your prejudice to stand in the way of progress: prescribe more mouse bites!
Oops--you're absolutely right! I did--in fact--fail to remember not to kill the patient after you expressly told me not to.