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name: evaluation description: "Route SDV quality, diagnostic, and visualization workflows for real versus synthetic data." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SDV evaluation router

Use this sub-skill when the task is to score synthetic data quality, run diagnostic checks, or build real-vs-synthetic plots after SDV sampling. It owns the SDV evaluation functions and their interaction with sdmetrics report/visualization objects.

Route here for

• Natural requests such as "evaluate SDV synthetic data", "quality report", "diagnostic report", evaluate_quality, run_diagnostic, get_column_plot, get_column_pair_plot, or get_cardinality_plot.
• Single-table DataFrame evaluation and multi-table dict[str, DataFrame] evaluation through the unified sdv.evaluation functions.
• Plot selection for one column, two columns, and multi-table relationship cardinality.
• Interpreting report scores/properties and debugging type, metadata, column, table, or sdtype errors in evaluation calls.

Route elsewhere

• Loading data or constructing metadata: use the data-preparation sub-skill.
• Fitting or sampling data with SDV models: use single-table, multi-table, or sequential sub-skills.
• Constraint validation and constraint-specific failures: use the constraints sub-skill.
• Metadata graph visualization, not synthetic-vs-real comparison: use data-preparation.

Operating path

1. Confirm real_data and synthetic_data use the same data shape: both pandas DataFrames for single-table workflows or both dictionaries for multi-table workflows.
2. Confirm metadata is valid and describes the real/synthetic data columns. If not, route to data-preparation before scoring.
3. Use evaluate_quality for distribution/trend similarity and run_diagnostic for data-validity/data-structure checks.
4. Use plot helpers only after confirming the target table/column names and sdtypes are supported, or provide an explicit plot_type.
5. Treat report scores as diagnostic evidence, not proof that the synthetic data is safe or private. Capture the result object and inspect its properties/details when the task needs explanations.

References

• API reference for function signatures, accepted data shapes, and return objects.
• Workflows for single-table, multi-table, and plot recipes.
• Troubleshooting for common report and visualization failures.