There is a benefit of a skill though. If an AI keeps encoding common tasks as skills and scripts, the LLM eventually just becomes a dumb routing mechanism for ambiguous user requests, which ultimately drives down token usage.

If everything you want an LLM do is already captured as code or simple skills, you can switch to dumber models which know enough about selecting the appropriate skill for a given user input, and not much else. You would only have to tap into more expensive heavy duty LLMs when you are trying to do something that hasn’t been done before.

Naturally, AI companies with vested interest in making sure you use as many tokens as possible will do everything they can to steer you away from this type of architecture. It’s a cache for LLM reasoning.

AI companies don't want you to waste tokens, they benefit when you use them efficiently because they can serve more users on the infra that's the main bottleneck for them. It's Jevons' paradox in action.