What problem does it solve? Code reviews often produce noisy, low-confidence feedback or miss real defects like silent failures, security holes, and contradictions with past decisions. This Skill runs a structured multi-role review that filters out false positives and surfaces only high-confidence findings a senior engineer would actually raise. ## Core Features & Use Cases - Specialized reviewer roles: Delegates to focused roles covering correctness, silent failures, type design, test coverage, comments, security, git history, and prior PR history. - Confidence scoring and filtering: Scores every finding 0-100, drops anything below 80, and excludes pre-existing issues, linter-enforced style, and duplicates of existing PR comments. - Two output modes: Emits a local severity-ranked report for fix loops by default, or posts a summary plus inline GitHub PR comments with --comment. - Use Case: Before opening a pull request, run the review to catch a swallowed exception, an untested error path, and a guard removed that git history shows was added deliberately — each with a concrete fix. ## Quick Start Ask the AI to review the current branch's changes against the base branch and produce a severity-ranked findings report before opening the pull request.