What problem does it solve? After an AI-assisted implementation lands commits on a branch, there is no objective record of code quality. This Skill audits the committed-but-unpushed changes on the current branch across four quality dimensions and appends the scores to a persistent JSON log, so code quality is measured longitudinally rather than assumed. ## Core Features & Use Cases - Four-dimension scoring: Evaluates duplication (against the rest of the repo), code bloat, inefficient code, and security issues, each as a 0–100% score of affected added lines. - Parallel subagent analysis: Launches four independent read-only subagents with self-contained briefs, then validates their arithmetic and evidence before accepting results. - Append-only eval log: Writes exactly four JSON entries per run to ~/.claude/evals/code.json with atomic read-modify-write, including corrupt-log backup handling. - Use Case: After /feature-implement commits several TDD stages on a feature branch, run the evals to get an evidence-backed score table and per-dimension recommendations for improving the implementation workflow before pushing. ## Quick Start Ask the AI to run code evals on the unpushed commits of the current branch and append the duplication, bloat, inefficiency, and security scores to the eval log.