data-designer

Generate synthetic datasets and Python config pipelines via load_config_builder().

Updated Apr 16, 2026

Search Tags:#automation #sampling #generate #workflows #synthetic-datasets #data-designer

Ready to run

npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill data-designer-sayalinvidia

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-designer
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/data-designer
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill data-designer-sayalinvidia

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users create datasets and data-generation workflows without manual, repetitive data crafting, enabling scalable synthetic data pipelines.

Core Features & Use Cases

Generate synthetic datasets tailored to ML training, testing, and evaluation across domains.
Configure data schemas, samplers, processors, validators, and optional seed data to produce realistic, diverse records.
Build reusable data-generation pipelines that can be exported as Python config scripts via load_config_builder() for automation and reproducibility.

Quick Start

Provide a dataset description to the agent and run the Data Designer workflow to generate synthetic data.

Frequently Asked Questions about data-designer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate synthetic datasets for ML training and testing?▼

You generate synthetic datasets for ML training by providing a dataset description to the agent, which then runs the Data Designer workflow. This process configures data schemas, samplers, processors, and validators to produce realistic, diverse records tailored to your domain.

What is the best way to automate synthetic data generation pipelines?▼

The best way to automate synthetic data generation pipelines is by exporting a Python config script via load_config_builder(). This script integrates columns, samplers, processors, and validators, enabling scalable, reproducible data generation workflows without manual data crafting.

Can I use seed data and persona sampling when generating synthetic data?▼

Yes, you can use optional seed data and persona sampling when generating synthetic data. These options are supported within the Data Designer tool to help produce realistic and diverse records tailored to specific ML evaluation requirements across varied domains.

How do I configure columns, samplers, and validators for synthetic data?▼

You configure columns, samplers, and validators by defining them within the Python config script generated through load_config_builder(). The Data Designer tool uses these components to build reusable data-generation pipelines that produce synthetic datasets.

Do I need any external dependencies to build synthetic data workflows?▼

You do not need any external dependencies to build synthetic data workflows. The Skill operates independently to generate synthetic datasets and pipelines from your specifications using the built-in Data Designer tool and its configuration capabilities.