Depends on what you do. If you work with proprietary tech that is not in LLM training data and can't easily be found on the internet, you're cooked without good skill files.
Yeah, this is my use case for skills. Even with good documentation, it feels better to have things local and easy to tweak.
I'd add to that I've also used them as a style guide. The project involved taking in unstructured inputs and creating structured outputs. Lots of choices along the way, and it seemed a neat way to encapsulate decisions we'd made as a team.
Storage, well it's just for the one project, so the repo. Can't say I've used them beyond that.
I agree it depends, but I can offer another angle: By writing a few py tools and creating skills around them I was able to save tokens, so these skills were cost-effective in my case, they lowered the cost of the tasks I execute.
Yes, but isn't that kind of a waste? If there's not enough AI training data or even just the internet, how can we find information just from a command called a skill? It would be nothing?.
Let me give you an example. Let's say you're building processes using the process management tool FooTool from the company BigFoo. You tell the LLM, "make a new process." A process is just an XML file, but BigFoo is highly proprietary, so the LLM has no examples of how to make one. No public documentation exists on the Internet, so it's not in the LLM's training data and can't be searched.
So you make a skill "make FooTool process" that explains what a FooTool process XML looks like, what options there are for initializing a new FooTool process, and so on.
Now your LLM went from "Let me spend five minutes looking at other stuff in your repo to find anything that tells me what I'm supposed to do" to 20 seconds and a working process.
