---

name: synthetic-data-generator description: "Use SDGX/Synthetic Data Generator for tabular synthetic data, metadata inspection, relationship metadata, CLI fit/sample, LLM synthesis, evaluation metrics, and plugin-oriented extension workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Synthetic Data Generator (SDGX) Repo Skill

Use this skill when a task names sdgx, Synthetic Data Generator, or asks for structured/tabular synthetic data generation with SDGX APIs or the sdgx CLI. The package centers on pandas tables, metadata inspection, default data processors, CTGAN/GaussianCopula synthesis, OpenAI-backed tabular generation, and tabular quality metrics.

Do not use this skill for generic GAN theory, unrelated synthetic image/text generation, or privacy-policy decisions that are not about operating SDGX APIs.

Fast orientation

• Public package/distribution: sdgx.
• Main import check: import sdgx; print(sdgx.__version__).
• Main CLI: sdgx with fit, sample, and list-* subcommands.
• Primary high-level API: sdgx.synthesizer.Synthesizer.
• Canonical single-table ML model: sdgx.models.ml.single_table.ctgan.CTGANSynthesizerModel.
• Statistic model available by direct import: sdgx.models.statistics.single_table.copula.GaussianCopulaSynthesizerModel.
• LLM model: sdgx.models.LLM.single_table.gpt.SingleTableGPTModel.

Read references/install-and-import.md before installing, verifying imports, or choosing CPU/CUDA/OpenAI settings. Run scripts/check_sdgx_environment.py when you need a quick registry/import/backend check in the active environment.

Quick install

pip install sdgx
python - <<'PY'
import sdgx
print(sdgx.__version__)
PY
sdgx --help

For local checkout work, pip install . or pip install '.[test]' from the repository root are the documented alternatives.

Route by task

• Data connectors, loaders, metadata, processors, cachers, inspectors, relationships, and plugins: read sub-skills/data-preparation/SKILL.md. Start here when the task is about CSV/DataFrame/generator inputs, metadata JSON, datetimes, fixed/specific column combinations, PII columns, caching, or custom extension registration.
• Fit/sample tabular synthesizers or use the sdgx CLI: read sub-skills/single-table-synthesis/SKILL.md. Start here for Synthesizer, CTGAN, GaussianCopula, model save/load, sdgx fit, sdgx sample, --json_output, and --torchrun.
• Generate tabular data with OpenAI-compatible LLMs or infer off-table features: read sub-skills/llm-synthesis/SKILL.md. Start here for SingleTableGPTModel, OPENAI_KEY, OPENAI_URL, metadata-only generation, raw-data prompting, and response parsing.
• Evaluate real vs synthetic data or adapt benchmark-style checks: read sub-skills/evaluation/SKILL.md. Start here for Jensen-Shannon divergence (JSD), mutual-information similarity (MISim), and safe metric scripts.

Common operating pattern

1. Choose a data connector or create a pandas.DataFrame.
2. Build or inspect Metadata and optionally customize column types, primary keys, specific combinations, categorical encoders, and datetime formats.
3. Choose a model and entrypoint:
   • Library: Synthesizer(model=..., data_connector=..., metadata=...) then fit() and sample(n).
   • CLI: sdgx fit ... then sdgx sample ....
   • LLM: SingleTableGPTModel with raw data or metadata, after confirming key/base URL policy.
4. Validate outputs: expected columns, row count, null/datetime/PII behavior, and metric ranges.
5. Use troubleshooting references when imports, metadata checks, cache writes, optional dependencies, CUDA, or API credentials fail.

Repo-level references

• references/component-map.md maps the SDGX architecture, managers, default processors, and public package surfaces.
• references/install-and-import.md records installation, import, environment variable, and backend guidance.
• references/troubleshooting.md covers cross-cutting failures shared by multiple workflows.
• references/repo-provenance.md records the source version and evidence paths used to create this skill.
• references/repo-routing-metadata.json is structured metadata for repo-skills-router import.

Guardrails

• Never assume the package has an installed inspection environment just because the checkout imports from the current directory; verify with the target Python.
• Do not run notebook or benchmark-scale workflows as quick checks. Use tiny fixtures and short smoke scripts unless the user explicitly requests benchmark execution.
• Treat LLM generation as a network/credential workflow. Do not send sensitive raw rows to an external API unless the user explicitly authorizes that data flow.
• When debugging an SDGX error, preserve the exact exception class from sdgx.exceptions; CLI --json_output true can surface structured exit messages.