Most attempts to make AI coding agents more reliable pile on more context — sharper prompts, more docs, bigger system messages. This project makes the opposite bet: the failure mode isn't what the agent knows, it's that it sprints straight to code. The fix is process, not knowledge — a library of skills that intercept the agent at decision points and force it to brainstorm, plan, write a failing test, and request review before it's allowed to move on.
What Sets It Apart
- Skills trigger by context, not by command. The methodology engages whether or not the developer remembers to ask for it, so discipline no longer depends on a human babysitting every prompt.
- A failing test must come first. The enforced RED-GREEN-REFACTOR loop blocks the classic agent failure of shipping code that was never actually exercised.
- Subagent coordination with two-stage review. Parallel tasks are checked by independent reviewer agents, which curbs the usual "looks done but isn't" drift as work scales.
- Portable across 11+ agents — Claude Code, Cursor, Copilot CLI, Gemini and others — so the workflow isn't tied to one vendor's tooling.
Who It's For
Great fit if you already run agents against real codebases and keep hitting the same wall: confident output that skipped design and testing. Look elsewhere if you want a quick autocomplete boost or one magic prompt — Superpowers deliberately adds friction at the brainstorm and planning gates, which pays off on multi-step features but is overkill for one-line fixes. It is also opinionated: adopting it means buying into its test-first, review-everything worldview rather than wiring up your own.
