flw-data-review-eval

What problem does it solve?

Ungraded or inconsistently graded FLW data review reports often miss critical quality issues like calibration drift, outlier flags, or vague recommendations, leading to accumulated bad datasets that undermine Connect opportunity outcomes.

Core Features & Use Cases

• 5-Dimensional Weighted Grading: Evaluates reports across signal coverage, outlier-detection rigor, recommendation actionability, evidence-citation discipline, and trajectory awareness with weighted scoring.
• Auto-Surfaced Alerts: Automatically flags blockers for critical gaps (e.g., missing concrete remediation for high-severity issues) and warnings for minor issues like uncited claims.
• Trend Tracking: Accumulates scores across recurring weekly reviews to identify calibration drift or recurring quality gaps over the course of an opportunity.
• Use Case: For an active Connect opportunity with 6 weekly FLW data reviews, this skill ensures each report is rigorously evaluated so quality issues are caught early before they render submission data unusable.

Quick Start

Use the flw-data-review-eval skill to grade the latest FLW data review report for your active Connect opportunity and generate a structured quality verdict.