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 for every piece of review feedback. - Pushback Guidance: Provides concrete criteria for when and how to challenge incorrect or context-lacking reviewer suggestions with technical reasoning. - YAGNI Enforcement: Checks whether suggested features are actually used in the codebase before implementing them. - Use Case: A reviewer on your pull request suggests removing legacy compatibility code. Instead of agreeing immediately, you verify the build targets and discover the code is still required, then reply with the technical evidence. ## Quick Start Apply the receiving-code-review skill to evaluate the review comments on my current pull request before implementing any changes.