ai-code-review

# AI Code Review Skill Human code fails where the human got tired or didn't know; AI code fails where *plausibility diverged from correctness* — and it fails fluently, with confident naming, clean formatting, and tests that pass without testing anything. Reviewing it with human-code instincts ("looks careful, probably is careful") is how the new bug class ships. This skill reviews for the failure modes that are characteristically AI. ## What This Skill Produces - A **review of the change** organised by AI-characteristic risk, each finding with file/line and severity - **Verification steps** the reviewer must actually run (not read) per risk class - A **team checklist** for AI-authored PRs, calibrated to this codebase ## Required Inputs Ask for (if not already provided): - **The diff or PR** (or the files changed) - **Provenance honestly**: fully agent-written, human-piloted, or mixed — and whether the *author reviewed it themselves* before requesting review - **The codebase context**: existing conventions/utilities the AI may not have known, and what the change claims to do - **Test infrastructure**: what CI actually runs (the AI may have written tests CI never executes) ## The AI-Characteristic Failure Modes Review in this order — most damaging first: 1. **Plausible-but-wrong logic.** The code reads correctly and does something subtly different: inverted edge conditions, off-by-one on boundaries the prompt never mentioned, the right algorithm for a slightly different...