What problem does it solve? Single-reviewer code reviews miss blind spots and inflate nitpicks. This Skill runs the same diff through several independent LLM reviewers, then applies lead-reviewer judgment to separate real issues from noise so you know what actually needs fixing. ## Core Features & Use Cases - Multi-Model Adversarial Review: Spawns one reviewer per configured model, each applying the same rubric and code-quality lens to the same diff. - Consensus Synthesis: Deduplicates findings, highlights issues raised by 2+ models as high-confidence signal, and maps where models agree or diverge. - Lead Judgment Filtering: Categorizes every finding as act on, consider, noted, or dismissed, with rationale, so the verdict is actionable rather than exhaustive. - Use Case: Before merging a feature branch, ask for an adversarial review of git diff main...HEAD; receive a structured verdict listing the handful of issues that would block a real PR plus the dismissed nitpicks and why. ## Quick Start Ask the agent to interrogate the changes on your current feature branch and produce a synthesized adversarial review verdict.