# Data Synthesis Generation

> Use when generating synthetic data for ML training.

- Skill: `loopyluci/data-synthesis-generation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add loopyluci/data-synthesis-generation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/data-synthesis-generation/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: LoopyLuci (https://skillmd.com/u/loopyluci)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/loopyluci/data-synthesis-generation

---


# Data Synthesis and Generation

Generating synthetic data for ML training — from tabular data synthesis through image generation with GANs/diffusion models and privacy-preserving synthetic data.

## When to Use

- Insufficient real data for ML training
- Privacy concerns with real data (HIPAA, GDPR)
- Balancing class distributions (rare events, edge cases)
- Testing systems with controlled data properties

## Synthesis Methods

```python
SYNTHESIS_METHODS = {
    'tabular': 'CTGAN, TVAE, SDV — preserve statistical properties of real data',
    'image': 'Stable Diffusion, StyleGAN, DALL-E — photorealistic or domain-specific',
    'text': 'LLM-based generation — prompts to generate labeled text data',
    'time_series': 'DoppelGANger, TimeGAN — temporal pattern preservation',
    'privacy': 'Differentially private synthetic data — mathematical privacy guarantee',
}

def generate_tabular_synthetic(real_data: pd.DataFrame, n_rows: int = 1000) -> pd.DataFrame:
    """Generate synthetic tabular data using SDV."""
    from sdv.tabular import CTGAN
    model = CTGAN(epochs=50)
    model.fit(real_data)
    return model.sample(n_rows)
```

## Verification Checklist

- [ ] Synthetic data preserves key statistical properties (means, correlations, distributions)
- [ ] No identity leakage from training data (privacy audit)
- [ ] Downstream model performance matches real-data training
- [ ] Edge cases and rare events adequately represented
- [ ] Synthetic data quality metrics measured (statistical similarity, utility)
- [ ] Privacy guarantees documented (differential privacy epsilon if applicable)