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nemo-data-designer-plugin

Construct portable NeMo Data Designer configurations for synthetic dataset generation.

Updated Apr 16, 2026

Search Tags:#synthetic-data #dataset-generation #inference-gateway #data-designer #nemo-platform #persona-sampling #model-configs

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyarrow, data_designer.

What problem does it solve?

Data teams need a safe, repeatable way to generate synthetic datasets and build data generation pipelines for testing, model evaluation, and prototyping without touching production data.

Core Features & Use Cases

Programmatic configuration of data workflows via a Python interface that returns a DataDesignerConfigBuilder, enabling portable local and cluster workflows.
Supports model_configs, persona sampling, seed sources, validation, preview, and generation steps within the NeMo Platform plugin.
Typical use cases include generating synthetic customer records, QA datasets for model testing, and end-to-end data pipelines for experimentation.

Quick Start

Create a Python script containing load_config_builder() that returns a DataDesignerConfigBuilder to configure and generate your dataset.

Frequently Asked Questions about nemo-data-designer-plugin

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

FAQPage Schema

How do I generate synthetic datasets for testing without touching production data?▼

You can generate synthetic datasets safely by defining a Python configuration that returns a DataDesignerConfigBuilder, enabling repeatable data generation pipelines for testing and prototyping without production access.

What is persona sampling in synthetic data generation pipelines?▼

Persona sampling is a configuration feature within synthetic data generation that lets you define distinct user profiles or personas, ensuring generated datasets reflect varied demographic or behavioral characteristics across records.

Can I build repeatable data generation workflows that run on both local and cluster environments?▼

Yes, constructing a portable DataDesignerConfigBuilder produces configuration objects that execute identically across local development and cluster environments within the NeMo Platform, ensuring repeatable synthetic data generation.

Do I need pyarrow installed to configure synthetic dataset pipelines?▼

Yes, pyarrow is a required dependency alongside the data_designer module, providing the underlying columnar data processing capabilities needed to validate, preview, and generate synthetic datasets.

How do I integrate model configurations into synthetic dataset generation?▼

You can integrate model configurations by passing them into the DataDesignerConfigBuilder, allowing the synthetic data generation pipeline to align dataset outputs with specific model inference requirements during validation and preview steps.

What is the best way to prototype QA datasets for large language model evaluation?▼

Using a programmatic configuration builder to generate synthetic QA datasets provides a repeatable, safe method for creating evaluation data, supporting seed management and persona sampling for diverse test scenarios.