What problem does it solve? Code reviews by a single AI model can miss real bugs or produce false positives that waste developer time. This Skill sends your code changes to an independent external reviewer (Codex CLI in headless mode), verifies every finding against the actual codebase, and automatically fixes confirmed issues through iterative review cycles until the code is approved. ## Core Features & Use Cases - Cross-model code review: Sends git diffs (uncommitted changes, commits, ranges, or specific files) to Codex, which explores the codebase and returns structured JSON findings with severity, confidence scores, and evidence. - Verification before fixing: Every finding is checked against the real code and classified as CONFIRMED or FALSE POSITIVE, so only genuine issues get fixed by the 1c-code-writer agent. - Iterative review loop: After fixes, the code is re-reviewed with accumulated context of accepted and rejected findings, repeating until approval, iteration limit, or stalemate detection. - Use Case: After implementing a feature in a 1C:Enterprise project, run the skill on your uncommitted changes to get an independent review, automatic fixes for real problems, and a full audit log in .codex-code-review/review-log.md. ## Quick Start Ask the AI to review your uncommitted changes with the codex-code-review-fix skill, for example: run /codex-code-review-fix on my uncommitted changes with a maximum of 5 iterations.