auditing-synthetic-data-utility

What problem does it solve?

This Skill helps you decide whether tabular synthetic data is actually useful for downstream modeling on real-world data, instead of relying on misleading synth-only checks that can hide joint-structure failure.

Core Features & Use Cases

• Utility certification: Compares TSTR against the TRTR baseline so you can measure whether models trained on synthetic data still perform on held-out real data.
• Failure diagnosis: Flags marginal collapse, correlation collapse, rare-class loss, over-smoothing, and memorization so you know why utility dropped.
• Practical decision-making: Produces a clear use-as-real, use-with-caveats, or reject verdict for sharing, benchmarking, or research collaboration.
• Use case: A data scientist generates CTGAN output for patient records and needs to know whether a partner can train a classifier on the synth set without losing real-world predictive performance.

Quick Start

Ask the skill to audit your synthetic tabular dataset against a held-out real test split and report the TSTR, TRTR, correlation gap, bootstrap confidence interval, and final utility verdict.