It's not just a stored prompt, you can attach re-useable scripts to them to offer more determinism. ex: a script that validates that a PR follows exactly the template you want, with a max of N lines per entry.
The more determinism you have, the more consistent you can be and the more leverage you can build. (yes I understand that skill calls are non deterministic).
We do that, but keep the scripts in the code and just tell them in the markdown where the scripts are, same with "references" (docs/) for us. It never made sense to me to put those in a skill dir, many are useful across skills and for humans (many written for humans before agents were a thing)
One of the more interesting benefits to skills is that many harnesses now run the inline command(s) in backticks, shortcutting the model needing to make a tool call. This is helpful for deterministically building up context content for the skill before the agent ever sees it.
We take this further in some instances and have workflows that (1) does deterministic context gathering (2) invokes an agent (3) processes a file the agent is told to produce. This has made our PR review agent much better and removed it's access to all credential files. We have a step that gathers the diff + existing pull request comments into a .review dir, let the agent process that and create a comments.jsonl, then run a script in a new step to apply the comments against the API
+1 on all of this, it is similar to the pattern I see emerging as well. For example I have open-sourced (let's be honest, it's just my personal tooling of taste) a repo on gh to try and formalize these things. I'd rather not share out of mostly imposter syndrome but essentially it's a scaffolding tool ala "create-react-app" for the stack I generally use.
Part of that is to create standardized "docs/{architecture, decisions, contracts, operations}" folders that my harness recognizes in my repos to have a better sense of where was it.
I was solving your problem.before LLMs with my personal developers Swiss army knife (hof gen/CUE+templates). I'm somewhat keen to see how I can capture the agent markdown I want in many places, like project init, in CUE via embed and modules I can import and version, skipping the need for a markdown dependency cycle management system.
The more determinism you have, the more consistent you can be and the more leverage you can build. (yes I understand that skill calls are non deterministic).