I'm kinda confused about why this even is something that we need an extra feature for when it's basically already built in to the agentic development feature. I just keep a folder of md files and I add whatever one is relevant when it's relevant. It's kinda straight forward to do...

Just like you I don't edit much in these files on my own. Mostly just ask the model to update an md file whenever I think we've figured out something new, so the learning sticks. I have files for test writing, backend route writing, db migration writing, frontend component writing etc. Whenever a section gets too big to live in agents.md it gets it's own file.

Because the concept of skills is not tied to code development :) Of course if that's what you're talking about, you are already very close to the "interface" that skills are presented in, and they are obvious (and perhaps not so useful)

But think of your dad or grandma using a generic agent, and simply selecting that they want to have certain skills available to it. Don't even think of it as a chat interface. This is just some option that they set in their phone assistant app. Or, rather, it may be that they actually selected "Determine the best skills based on context", and the assistant has "skill packs" which it periodically determines it needs to enable based on key moments in the conversation or latest interactions.

These are all workarounds for the problems of learning, memory...and, ultimately, limited context. But they for sure will be extremely useful.

It’s a formalisation of the method, and it’s in your global ~/.claude and also per project.

I have mine in a GitHub template so I can even use them in Claude Code for the web. And synchronise them across my various machine (which is about 6 machines atm).

I'm so excited for the future, because _clearly_ our technology has loads to improve. Even if new models don't come out, the tooling we build upon them, and the way we use them, is sure to improve.

One particular way I can imagine this is with some sort of "multipass makeshift attention system" built on top of the mechanisms we have today. I think for sure we can store the available skills in one place and look only at the last part of the query, asking the model the question: "Given this small, self-contained bit of the conversation, do you think any of these skills is a prime candidate to be used?" or "Do you need a little bit more context to make that decision?". We then pass along that model's final answer as a suggestion to the actual model creating the answer. There is a delicate balance between "leading the model on" with imperfect information (because we cut the context), and actually "focusing it" on the task at hand, and the skill selection". Well, and, of course, there's the issue of time and cost.

I actually believe we will see several solutions make use of techniques such as this, where some model determines what the "big context" model should be focusing on as part of its larger context (in which it may get lost).

In many ways, this is similar to what modern agents already do. cursor doesn't keep files in the context: it constantly re-reads only the parts it believes are important. But I think it might be useful to keep the files in the context (so we don't make an egregious mistake) at the same time that we also find what parts of the context are more important and re-feed them to the model or highlight them somehow.