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name: data-preparation description: "Prepare SDV demo data, local files, metadata, utilities, and logs before modeling." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SDV data-preparation router

Use this sub-skill when the task is to prepare SDV inputs before synthesis: load demo data, read or write local CSV/Excel data, build/edit/validate metadata, visualize or anonymize metadata, clean relational references, subset sequential rows, or inspect SDV logs.

Route here for

• Loading SDV demo datasets and demo resource files.
• Loading local CSV folders or Excel workbooks into dict[str, pandas.DataFrame].
• Detecting metadata from a single DataFrame, multiple DataFrames, CSV folders, or local file handlers.
• Editing metadata columns, primary/alternate/foreign keys, sequence keys/indexes, and column relationships.
• Running metadata.validate() plus metadata.validate_data(data_dict) for multi-table data or metadata.validate_table(dataframe, table_name=...) for one DataFrame before fitting a synthesizer.
• Saving, loading, anonymizing, visualizing, or upgrading metadata JSON.
• Using drop_unknown_references, get_random_sequence_subset, SDV logging helpers, or safe saved-synthesizer loading diagnostics.

Route elsewhere

• Model fitting, sampling, conditional sampling, save/load as a modeling workflow: use the single-table, multi-table, or sequential synthesis sub-skill.
• Constraint class design, custom constraints, and constraint serialization: use the constraints sub-skill.
• Quality reports, diagnostics, and plot comparison of real vs synthetic data: use the evaluation sub-skill.

Operating sequence

1. Identify the data modality: single-table DataFrame, multi-table dict of DataFrames, or sequential single-table data.
2. Load or create data using the narrowest I/O tool: demo API, load_csvs/save_csvs, CSVHandler, or ExcelHandler.
3. Build Metadata first when possible; only use legacy SingleTableMetadata or MultiTableMetadata when a downstream API specifically requires them.
4. Edit sdtypes and keys explicitly after auto-detection; do not assume inferred primary/foreign keys are correct.
5. Run metadata.validate() and then validate data: metadata.validate_data(data_dict) for multi-table input or metadata.validate_table(dataframe, table_name=...) for one DataFrame before routing to a synthesizer.
6. For relational data, repair unknown foreign-key references with drop_unknown_references only after confirming the rows are safe to discard.
7. Save stable metadata JSON and keep task-specific outputs outside the skill tree.

References

• API reference for signatures, return types, and important API boundaries.
• Workflows for compact end-to-end recipes.
• Troubleshooting for failure symptoms, causes, and fixes.