Superpowers for Coding Agents: Turn Vague Requests Into Tested Changes
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…
When an AI coding agent begins work on a new feature, it can easily make costly mistakes by diving into coding too quickly. The Superpowers methodology aims to prevent this by introducing a series of gates that require clarification, design approval, implementation planning, and testing before any coding occurs. The process starts with the agent recognizing it needs to create something, then stepping back to understand the user's true intent.
Next, it develops a clear specification, presents the design in digestible chunks, and waits for approval before moving on to implementation. By introducing red/green test-driven development and subagent-driven development, Superpowers helps catch errors early and ensures that the final product aligns with the user's intended outcome.
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