What problem does it solve? After cleaning a bibliometric dataset, researchers cannot be sure whether noise records were wrongly kept or relevant papers wrongly excluded. This Skill performs a structured critical audit of the cleaning process and its verdicts, exposing systematic bias, logical gaps, and over-exclusion before the dataset is used for analysis. ## Core Features & Use Cases - Six-Dimension Review Framework: Checks rule consistency, false-positive risk, false-negative risk, rule soundness, research-goal alignment, and process traceability. - Expected vs Actual Comparison: Contrasts pre-cleaning noise predictions (from S05) with actual noise rates per concept group to surface systematic deviations. - Graded Quality Report: Produces a standardized audit report with an A/B/C/D quality rating and concrete remediation suggestions. - Use Case: After finishing round 3 of corpus cleaning, run a full audit that samples 20 NOISE and 20 RELEVANT verdicts, compares noise rates against expectations, and outputs a B-grade report recommending a targeted recheck of one noise type. ## Quick Start Run a full critical review of the round 3 cleaning results and generate the graded audit report.