What problem does it solve? Once an implementation direction is agreed upon, constant back-and-forth confirmation interrupts work and wastes the user's time. This Skill takes a confirmed plan and drives it to completion autonomously, maintaining quality through test-first development, repeated self-review rounds, and actual runtime verification instead of relying on user checkpoints. ## Core Features & Use Cases - Autonomous Implementation Workflow: A seven-phase process covering policy confirmation, codebase exploration, test-first development, implementation, iterative self-review, PR integration, and concise reporting. - Test-First Discipline: Writes regression and edge-case tests before implementation code, verifying they fail for the right reason before building the feature. - Iterative Self-Review: Runs a minimum of two review rounds across seven quality dimensions (correctness, conventions, test coverage, intent, simplicity, type safety, ripple effects) until no further issues are found. - Use Case: After agreeing with your AI assistant on how to fix a bug or add a feature, say "go ahead" and let it explore the affected code, write failing tests, implement the fix, self-review until clean, run lint and tests, verify the change actually works, and report back with a summary. ## Quick Start Tell the assistant to implement the agreed feature or fix autonomously and report back only when tests pass and the change is verified working.
