# Synth Data Generate

Generate synthetic datasets in a Jupyter notebook after the dataops synthetic-data...

- Repository: https://github.com/microsoft/hve-core (1591 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add microsoft/hve-core --skill synth-data-generate -g -a claude-code -y`
Codex: `npx skills add microsoft/hve-core --skill synth-data-generate -g -a codex -y`
Cursor: `npx skills add microsoft/hve-core --skill synth-data-generate -g -a cursor -y`
Gemini CLI: `npx skills add microsoft/hve-core --skill synth-data-generate -g -a gemini-cli -y`

## Description

Generate synthetic datasets in a Jupyter notebook after the dataops synthetic-data preflight passes. Use when creating new synthetic data or preparing a guarded local replacement candidate from an approved source.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

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# Synthetic Data Generator

## Goal

Generate realistic, comprehensive synthetic data for `subject` in a Jupyter notebook only after the applicable `dataops` synthetic-data operation preflight passes. Use `example_data` when the user supplies an existing data source, schema, or file to use as a strict reference for structure and patterns.

## Inputs

* `subject`: Required. User query describing the subject for synthetic data generation.
* `example_data`: Optional. Free-form input describing an existing data source, schema, or file to use as a reference for structure and patterns.

## Required steps

### Step 0: Validate synthetic-data operation preflight

The rest: https://github.com/microsoft/hve-core/blob/HEAD/.github/skills/data-science-engineering/synth-data-generate/SKILL.md