Spend some time digging around in his https://github.com/obra/Superpowers repo.
I wrote some notes on this last night: https://simonwillison.net/2025/Oct/10/superpowers/
The packaged collection is very cool and so is the idea of automatically adding new abilities, but I’m not fully convinced that this concept of skills is that much better than having custom commands+sub-agents. I’ll have to play around with it these next few days and compare.
Some of these skills are probably better as programmed workflows that the LLM is forced to go through to improve reliability/consistency, that's what I've found in my own agents, rather than using English to guide the LLM and trusting it to follow the prescribed set of steps needed. Some mix of LLMs (choosing skills, executing the fuzzy parts of them) and just plain code (orchestration of skills) seems like the best bet to me and what I'm pursuing.
The ability to isolate context-noisy subtasks (like agentically searching through a large codebase by grepping through dozens of irrelevant files to find the one you actually need) unlocks much longer-running loops, and therefore much more complex tasks.
And you don't need a system this complicated to take advantage of it. Literally just a simple "codebase-searcher" agent (and Claude can vibe the agent definition for you) is enough to see the benefit first-hand. Once you see it, if you're like me, you will see opportunities for subagents everywhere.
Using them in a way that doesn't waste tokens is something I haven't fully figured out out yet!
What am I missing?
Also, memory itself can be a tool the subagent calls to retrieve only the stuff it needs.