`pyperf system {tune,show}` on macOS

With the advent of LLM tools (eg: Claude, Codex), small optimizations are a gateway drug to contributing to the ecosystem. Many people, myself included, use a MacBook as a daily driver. However, there are many mechanisms in these machines and macOS itself that might make posted numbers not really trustworthy, even with --rigorous. I'm thinking about thermal pressure / throttling, coalescing, P/E cores, different power sources, low/high power mode, memory pressure, virtualization, noisy daemons in the background, etc. Of course, there's also pyperformance. Perhaps there's real value in extending system {tune,show} to this platform.

My understanding is that there's no macOS API for CPU affinity, but we can still check stuff (eg: pmset), provide actionable advice. There's taskpolicy and pthread_set_qos_class_self_np. The model is per worker, not global.

Perhaps the real issue here is improving our harness on macOS.

Start with the existing system tune and system show entry points and run them on macOS to document their current behavior. Research the macOS mechanisms named here, including pmset, taskpolicy, power modes, thermal pressure, and worker-level scheduling. Done should mean a defined macOS harness improvement with actionable output or controls and tests covering the supported behavior.