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name: sdv description: "Use the Synthetic Data Vault (SDV) Python package for tabular synthetic data preparation, constraints, synthesis, and evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SDV repo skill

Use this repo skill when a task involves the Synthetic Data Vault (SDV) package: preparing tabular data and metadata, defining constraints, generating synthetic data for single-table, multi-table, or sequential data, and evaluating or visualizing synthetic data quality.

Install and smoke check

Install the public package in the task runtime:

python -m pip install sdv
# Optional Excel local I/O support:
python -m pip install "sdv[excel]"

Conda users can follow the public install route from the README:

conda install -c pytorch -c conda-forge sdv

Minimal import check:

python - <<'PY'
import sdv
from sdv.metadata import Metadata
from sdv.single_table import GaussianCopulaSynthesizer
print(sdv.__version__)
print(Metadata, GaussianCopulaSynthesizer)
PY

For a broader environment diagnostic, run scripts/check_import.py from this skill directory. Use --require-dot when metadata file rendering is required and --check-cuda or --require-cuda when deep-model GPU support matters.

Route by task

User task	Read
Load demo data, read/write local CSV or Excel files, detect/edit/validate metadata, visualize metadata, clean foreign-key references, inspect logs, or subset sequential rows before modeling.	data-preparation
Add or debug built-in CAG constraints, legacy tabular constraints, custom constraints, programmable constraints, constraint JSON files, or synthesizer constraint methods.	constraints
Fit/sample one pandas DataFrame with GaussianCopula, CTGAN, TVAE, CopulaGAN, single-table DayZ parameters, or legacy SingleTablePreset.	single-table
Fit/sample relational datasets represented as dict[str, pandas.DataFrame] with HMA or multi-table DayZ parameters.	multi-table
Model one sequence-keyed table with PAR, sequence keys/indexes, context columns, or sample_sequential_columns.	sequential
Run quality reports, diagnostic reports, single/multi-table plots, or cardinality plots after synthetic data is generated.	evaluation

Important operating boundaries

• SDV is primarily a Python API workflow in this snapshot. Do not assume an sdv command-line interface is usable unless the target installed package separately verifies it.
• Prepare and validate Metadata before choosing a synthesizer; most confusing fit/sample errors come from mismatched sdtypes, keys, or relationships.
• Add constraints before fitting whenever possible; if constraints or transformers are changed after fit, refit before sampling.
• Use enable_gpu=False for portable CPU-only single-table deep-model artifacts and cuda=False for portable PAR artifacts. Use GPU only after torch/CUDA is verified in the task runtime.
• SDV quality reports measure statistical similarity and validity, not privacy guarantees. Add separate privacy or downstream-utility checks when requested.
• Keep generated data, model pickle files, metadata JSON, plots, and logs outside the skill directory.

Shared references

• Repository provenance: source commit, version, evidence paths, and refresh checks.
• Root API reference: public module map, dependency notes, and top-level workflow surfaces.
• Root workflows: end-to-end routing patterns that combine multiple sub-skills.
• Root troubleshooting: cross-cutting installation, import, backend, and routing failures.