What problem does it solve? When receiving code review feedback, AI assistants often respond with performative agreement or blindly implement suggestions without verifying them against the actual codebase, leading to broken functionality, unnecessary features, and wasted effort. ## Core Features & Use Cases - Structured Response Pattern: Enforces a read-understand-verify-evaluate-respond-implement workflow before acting on any feedback. - Source-Specific Handling: Distinguishes trusted feedback from your human partner versus external reviewer suggestions that require skeptical verification. - YAGNI and Pushback Rules: Checks whether suggested features are actually used in the codebase and provides technical reasoning patterns for pushing back on incorrect suggestions. - Use Case: A reviewer on your GitHub PR suggests removing legacy code and adding a metrics endpoint. Instead of agreeing, you verify the legacy code is needed for backward compatibility and grep the codebase to find the endpoint is never called, then respond with technical reasoning. ## Quick Start Apply the receiving-code-review skill to evaluate and respond to the review comments on my current pull request before implementing anything.
