On top of everything you've described, one more advantage is that you can use the agents themselves to edit / improve / add to the skills. One easy one to do is something like "take the key points from this session and add the learnings as a skill". It works both on good sessions with new paths/functionality and on "bad" sessions where you had to hand-hold the agent. And they're pretty good at summarising and extracting tidbits. And you can always skim the files and do quick edits.
Compared to MCPs, this is a much faster and more approachable flow to add "capabilities" to your agents.
I think taking key points from a session and making a new skill is less useful than "precaching" by disseminating the key findings and updating related or affected skills, eliminating the need for a new skill (in most cases).
On the other hand, from a pure functional coding appeal, new skills that don't have leaking roles can be more atomic and efficient in the long run. Both have their pros/cons.
Add reinforcement learning to figure out which skills are actually useful, and you're really cooking.
DSPy with GEPA should work nicely, yeah. Haven't tried yet but I'll add it to my list. I think a way to share within teams is also low-hanging fruit in this space (outside of just adding them to the repo). Something more org-generic.
> DSPy with GEPA should work nicely
I think that would be a really really interesting thing to do on a bunch of different tasks involving developer tooling (e.g. git, jj, linters, etc.)
Combine that with retrying the same task again but with the improved skills in some sort of train loop that learns to bake in the skills natively to obviate the need for them.
The path to recursive self-improvement seems to be emerging.