# Mullzhang Skills Sdv Synthetic Data > SDV Synthetic Data Generation - Skill: `tomevault-io/mullzhang-skills-sdv-synthetic-data` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/mullzhang-skills-sdv-synthetic-data` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/mullzhang-skills-sdv-synthetic-data/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/mullzhang-skills-sdv-synthetic-data --- # SDV Synthetic Data Generation Use SDV (Synthetic Data Vault) to generate high-quality synthetic data that learns patterns from real data. --- ## **Most Important: Data Privacy Restrictions** **Always read this section first and strictly follow the rules below.** ### Prohibited Actions (Never Do These) 1. **Do not read data files with the `Read` tool** - Restricted extensions: `.csv`, `.xlsx`, `.xls`, `.json`, `.pkl`, `.html`, `.txt` - Do not inspect or display the user's data content - Do not inspect generated synthetic data content either 2. **Do not generate data preview code** - Do not include `print(data)`, `print(df)`, `data.head()`, `data.tail()`, `data.sample()`, etc. 3. **Do not log actual data values** ### No Pre-Inspection of Data Is Required The scripts automatically detect data structure, so **do not inspect data file contents or schema in advance**. Follow the workflow, ask the user only for required confirmations (number of rows, seed value, etc.), and run the scripts directly. --- ## Supported File Formats ### Data Input (Supported Formats) | Format | Extension | Read Method | |------|--------|-------------| | CSV | `.csv` | `pd.read_csv()` | | Excel | `.xlsx`, `.xls` | `pd.read_excel()` | | JSON | `.json` | `pd.read_json()` | ### SDV Output Files | File Type | Extension | Description | |-------------|--------|------| | Synthetic data | `.csv`, `.xlsx`, `.json` | Generated synthetic data | | Synthesizer | `.pkl` | Trained model | | Metadata | `.json` | Data structure definition | | Quality report | `.html`, `.txt` | Evaluation report | ## Execution Environment Check and Run Method This skill should **check the Python environment of the target project and run scripts in that environment**. Guideline (priority order): - If `uv.lock` or `pyproject.toml` exists, prioritize the `uv` environment and run with `uv run python scripts/...` - If `.venv/` exists, use that virtual environment's Python (example: `.venv/bin/python scripts/...`) - If neither exists, ask the user which environment should be used Always choose execution commands to match the **target project's environment** (uv/venv/poetry/pipenv, etc.). ## Workflow Follow these steps when generating synthetic data. All scripts accept options as runtime arguments, so complete all confirmations in Steps 1 to 4 before executing Step 5. 1. **Confirm number of rows**: Ask the user how many rows to generate (`num_rows`). Use the `AskUserQuestion` tool with the following prompt: - Question: "How many rows of synthetic data should be generated?" - Header: "Row Count" - Options: - "Same as source data (Recommended)" - Generate the same number of rows as the source data - "1,000 rows" - Generate 1,000 rows - "10,000 rows" - Generate 10,000 rows - "100,000 rows" - Generate 100,000 rows - If the user selects "Other", use the numeric value they enter 2. **Confirm seed value**: Confirm the seed value (`seed`) for reproducibility. Use the `AskUserQuestion` tool with the following prompt: - Question: "Do you want to specify a seed value for reproducibility?" - Header: "Seed Value" - Options: - "Do not specify (Random) (Recommended)" - Generate randomly without a seed - "42" - Use seed 42 (common default) - "123" - Use seed 123 - "0" - Use seed 0 - If the user selects "Other", use the numeric value they enter - If a seed is provided, set random seeds and pass it only when `sample` supports the `seed` argument (see script implementation) 3. **Select synthesizer**: Confirm the synthesizer type if needed 4. **Confirm model and metadata saving**: Ask whether trained artifacts should be saved for reuse. Use the `AskUserQuestion` tool with the following prompt: - Question: "Do you want to save the trained synthesizer and metadata?" - Header: "Model Save" - Options: - "Save both (Recommended)" - Save both synthesizer and metadata - "Synthesizer only" - Save trained synthesizer only - "Do not save" - Finish without saving - See script options (`--save-model`, `--save-metadata`) for save/load details 5. **Generate data**: Execute scripts by passing all confirmed options from Steps 1 to 4 (row count, seed value, synthesizer, save settings) as arguments ## Quick Start Use `scripts/generate_single_table.py` to generate synthetic data for a single table. **Note**: Scripts exist under `.codex/skills/sdv-synthetic-data/scripts/`. Do not create new scripts; execute existing scripts directly (example: `uv run python .codex/skills/sdv-synthetic-data/scripts/generate_single_table.py ...`). ## Synthesizer Selection | Synthesizer | Characteristics | Recommended Use | |---|---|---| | `GaussianCopulaSynthesizer` | Fast, transparent, customizable | **Default choice** | | `CTGANSynthesizer` | Uses GAN, high fidelity | More complex patterns | | `TVAESynthesizer` | Uses VAE, high fidelity | Complex patterns | | `CopulaGANSynthesizer` | GaussianCopula + CTGAN | Hybrid | For exact selection behavior, refer to the `--synthesizer` option in `generate_single_table.py`. ## Metadata Configuration ### Auto Detection `generate_single_table.py` and `generate_multi_table.py` use `Metadata.detect_from_dataframe(...)`. ### Manual Configuration If manual adjustment is needed, save metadata with `--save-metadata`, edit it, and load it later. ### sdtype List - `numerical`: Numeric - `datetime`: Datetime (`datetime_format` required) - `categorical`: Categorical - `boolean`: Boolean - `id`: Identifier (`regex_format` can define patterns) - `email`, `phone_number`, `ssn`, etc.: PII auto-anonymization ## Constraint Configuration Add constraints to enforce business rules 100%. Constraint setup requires metadata edits or additional implementation. Extend scripts when needed. ## Multi-Table (Relational) Use `generate_multi_table.py`. Specify `tables` and `relationships` in the config file, and control generation volume with `--scale`. `generate_multi_table.py` auto-detects each table with `metadata.detect_from_dataframe(..., table_name=...)` and applies `primary_key` and `relationships` from the config file. If needed, save metadata with `--save-metadata` and tune it later. ## Time-Series Data Time-series generation is currently unsupported by existing scripts. Add a new script if needed. ## Quality Evaluation Use `evaluate_quality.py` for quality evaluation. Diagnostic reports are reviewed at execution time and can be explicitly output with `--diagnostic` or `--diagnostic-output`. ## Model Save/Load Use each script's `--save-model` / `--save-metadata` options for save/load workflows. ## Scripts See reusable scripts in `scripts/`: - `generate_single_table.py`: Single-table synthetic data generation (`--rows`, `--seed`, `--synthesizer`, `--epochs`, `--save-model`, `--save-metadata`) - `generate_multi_table.py`: Multi-table synthetic data generation (`--config`, `--output-dir`, `--output-format`, `--scale`, `--seed`, `--save-model`, `--save-metadata`) - `evaluate_quality.py`: Quality report generation (`--output`, `--diagnostic`, `--diagnostic-output`) - `sample_rows.py`: Sample rows from input data (`--rows` or `--fraction`, `--replace`, `--seed`, `--sheet`) --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/mullzhang) — claim your Tome and manage your conversions.