I believe skills are much more than a simple markdown file with instructions. They are a very powerful script engine. How I organize it is that of course there's a markdown with instructions but I split the work in a hybrid of script + instructions. So all the work that can be deterministic is a python script api surface and all the logical or thinking work is in instructions. and agent is also instructed on how to use the api of the python helper functions. This makes it almost like a normal script but the runtime is a harness and the business logic can be any combination of code + human-level intelligence.

So I like to do all the edge case handling and validation etc via a helper function, and the agent is simply instructed to call the function to do something. It is extremely powerful and a completely different way of automating things. I am constantly forced to re-think how computers are supposed to work and its limitations.

My genuine question is:

Are there any "skills" at all that have proven to be useful? And if so, what's the context?

Because, for me anyway, LLMs usually do one thing, and that then produces a durable artifact. So the prompt that got me there by that point expired and is not really needed anymore.

I also occasionally have recurring tasks (rarely though), but there, the prompt to do stuff is embedded in code that orchestrates the doing, so I have no use-case for that either.

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For the "add this endpoint" example you've described, I just throw commit IDs at the clanker and say "go do that again". That works, and doesn't decouple knowledge from code.

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