Agree. I'd add that a aha moment to skills is AI agents are pretty good at writing skills. Let's say you have developed an involved prompt that explains how to hit an API (possibly with the complexity of reading credentials from an env var or config file) or run a tool locally to get some output you want the agent to analyze (example, downloading two versions of python packages and diffing them to analyze changes). Usually the agent reading the prompt it's going to leverage local tools to do it (curl, shell + stdout, git, whatever) every single time. Every time you execute that prompt there is a lot thinking spent on deciding to run these commands and you are burning tokens (and time!). As an eng you know that this is a relatively consistent and deterministic process to fetch the data. And if you were consuming it yourself, you'd write a script to automate it.

So you read about skills (prompt + scripts) to make this more repeatable and reduce time spent thinking. At that point there are two paths you can go down -- write the skill and prompt yourself for the agent to execute -- or better -- just tell the agent to write the skill and prompt and then you lightly edit it and commit it.

This may seem obvious to some, but I've seen engineers create skills from scratch because they have a mental model around skills being something that people must build for the agent, whereas IMO skills are you just bridging a productivity gap that the agent can't figure out itself (for now), which is instructing it to write tools to automate its own day to day tedium.

CLIs are really good when you can use them. self-documenting, agents already have shell tools, they tend to solve fine-grained auth, etc.

feels like the right layer of abstraction for remote APIs

If only there was a way to progressively disclose the API in MCP instead of presenting the full laundry list up front.