What problem does it solve? AI code review tends to over-report, hallucinate library behavior, and lose all state between sessions, so findings get ignored and the same issues are rediscovered every run. This Skill enforces a precision-first review process: every claim about a library or runtime is verified against the installed version's source or docs before it becomes a finding, and all state lives in a durable .code-review/ ledger that survives context compaction. ## Core Features & Use Cases - Evidence-verified findings: Every BLOCKER or MAJOR finding requires a quoted hunk, a concrete failure scenario, verified evidence, and an independent confidence score of 80 or higher, or it is demoted to a question. - Two review modes: Mode A reviews a change, PR, or branch in passes of at most 400 lines; Mode B audits a whole module and produces a backlog of self-contained remediation tasks for subagents. - Durable review state: Scripts scaffold a .code-review/ workspace with an append-only findings ledger using stable IDs, so regressions are detected instead of re-reported. - Use Case: After finishing a feature branch, ask for a review; the Skill detects the stack, chunks the diff, verifies each library-dependent claim against the pinned versions, and emits a verdict with tiered findings and a resumable ledger. ## Quick Start Ask the assistant to review your current uncommitted changes or a specific pull request using the principal-review skill.