Built on the trusted obra/superpowers workflow and refined through research into LLM agent behavior, it adds automatic 3-tier workflow routing, proactive safety hooks, self-consistency verification at critical decision points, cross-session memory, and adversarial red-teaming — everything the original does, plus the discipline layer it was missing. Cross-session memory changes the experience fundamentally. Without it, every session starts blind: the AI re-explores structure it already mapped, re-proposes approaches that were already rejected, re-debugs errors that were already solved. With the four-file memory stack, it arrives knowing what was tried, what was decided, and why — and builds forward instead of sideways. Five research-backed principles run throughout: *less is more* (minimal always-on instructions), *fresh context beats accumulated context* (subagents get clean scoped prompts, not polluted history), *compliance ≠ competence* (instructions must be carefully engineered, not just comprehensive), *verify your own reasoning* (multi-path self-consistency catches confident-but-wrong failures before they become expensive), and *accountability drives accuracy* (agents that know their output has real downstream consequences perform better).