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. - Source-Specific Handling: Applies different trust levels for feedback from your human partner versus external reviewers, with explicit verification checklists. - Pushback and YAGNI Guidance: Provides concrete rules for pushing back on technically incorrect suggestions and rejecting unused features. - Use Case: A reviewer suggests removing legacy code. Instead of agreeing, you check the build target, discover backward compatibility requirements, and respond with technical reasoning before acting. ## Quick Start Use the receiving-code-review skill to evaluate this pull request feedback before implementing any of the suggested changes.