What problem does it solve? Automated code reviews often produce noise, surface irrelevant issues, and keep re-raising findings after fixes. This Skill runs a convergent, high-signal review where every candidate finding is raised, filed, or refuted with proof, and re-reviews reconcile against a durable ledger instead of re-scanning from scratch. ## Core Features & Use Cases - Staged subagent pipeline: Scope, Correctness, Security, and Quality finders run in parallel, followed by Verification and a Judge, each as a dedicated subagent on one configurable model (default Claude Opus 4.8). - Shared finding taxonomy: every finding is classified by severity, commonality, origin, and type, carries a verification status and a stable ID, and routes to a review comment, a GitHub issue proposal, or none. - First review and re-review modes: re-reviews anchor to the stamped commit SHA, reconcile prior findings as fixed, refuted, dismissed, or re-raised, and catch new defects introduced by fix commits. - Use Case: After resolving merge conflicts on a pull request, run the skill to post a GitHub review with inline findings; after pushing fixes, run it again so it verifies each fix and only re-raises what is still broken. ## Quick Start Ask the AI to run the vstorm-code-review skill on the current pull request, optionally passing a model such as claude-sonnet-4-5 for a cheaper pass.