data-generation-quality-metrics-loop

What problem does it solve?

Large-scale auto-generated data often hides quality issues that spot checks miss. This skill provides a structured approach to quantify and iteratively improve data quality using defined metrics.

Core Features & Use Cases

• Define and monitor quantitative metrics (e.g., contamination rate, long entries, empty fields, missing keys) to assess generated data quality.
• Implement an iterative generate-measure-fix loop to progressively reduce defects across datasets.
• Keep the metric calculation separate from the generation process to enable repeatable QA and easier experimentation.
• Use cases include synthetic data pipelines, training data curation, and quality assurance for data generation scripts.

Quick Start

Run an initial data generation pass, measure quality with the metrics, and apply fixes to produce v2 data for re-evaluation.