The fastest way for an AI coding agent to create expensive work is to start coding too soon. A request arrives, the agent infers the missing requirements, selects an architecture, edits several files, and produces a plausible patch. The patch may compile. It may even pass a narrow test. But it can still solve the wrong problem.
Superpowers is a methodology and a set of composable skills intended to change that default. Rather than treating code generation as the first step, it asks the agent to clarify the outcome, develop and get approval for a design, make an implementation plan, use true red/green test-driven development, carry out the work in small tasks, and review what was built.
That is not bureaucracy for its own sake. It is a response to the fact that AI agents are very good at filling gaps with plausible assumptions.
The core idea: gates before generation
Superpowers describes a coding-agent workflow that begins when the agent recognizes it is building something. Instead of jumping into implementation, it steps back and asks what the user is really trying to accomplish. It then develops a specification, presents the design in readable chunks, and waits for sign-off before producing a detailed plan.
