The compressed agents.md approach is interesting, but the comparison misses a key variable: what happens when the agent needs to do something outside the scope of its instructions?

With explicit skills, you can add new capabilities modularly - drop in a new skill file and the agent can use it. With a compressed blob, every extension requires regenerating the entire instruction set, which creates a versioning problem.

The real question is about failure modes. A skill-based system fails gracefully when a skill is missing - the agent knows it can't do X. A compressed system might hallucinate capabilities it doesn't actually have because the boundary between "things I can do" and "things I can't" is implicit in the training rather than explicit in the architecture.

Both approaches optimize for different things. Compressed optimizes for coherent behavior within a narrow scope. Skills optimize for extensibility and explicit capability boundaries. The right choice depends on whether you're building a specialist or a platform.