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Synthetic Data Generation

Create synthetic datasets with modular Python and YAML pipelines.

155|61|Updated Nov 12, 2024
Search Tags:#data generation#synthetic data#python framework#data augmentation#llm pipelines#yaml flows
One-click install
npx skills add https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub --skill synthetic-data-generation-red-hat-ai-innovation-team
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Synthetic Data Generation
Source: https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub/tree/main/.claude/skills/synthetic-data-generation
Command: npx skills add https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub --skill synthetic-data-generation-red-hat-ai-innovation-team

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the creation of synthetic datasets by providing a modular framework for building complex data generation pipelines, reducing the time and effort required for data preparation and augmentation.

Core Features & Use Cases

  • Composable Blocks: Mix and match LLM-powered and traditional data processing blocks to create custom workflows.
  • YAML Flows: Define and manage data generation pipelines using declarative YAML configurations.
  • Python Scripting: Develop custom data generation logic with Python for ad-hoc experiments and complex tasks.
  • Use Case: Generate realistic training data for an AI model by defining a flow that uses LLMs to create diverse text examples, then parses and structures them into a usable dataset.

Quick Start

Use the synthetic data generation skill to create a flow from the 'my_flow.yaml' file and generate data from the 'input.csv' file.

Frequently Asked Questions about Synthetic Data Generation

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

FAQPage Schema
How do I build synthetic data generation pipelines using YAML?▼

You can build synthetic data generation pipelines by defining declarative YAML configurations that compose LLM and traditional data processing blocks into structured workflows for automated data creation.

Can I use custom Python scripts for data augmentation instead of YAML flows?▼

Yes, you can develop custom data generation logic using Python scripts for ad-hoc experiments and complex tasks, providing an alternative to declarative YAML flows for deterministic task execution.

What is the best way to generate realistic training data for LLM pipelines?▼

The best way to generate realistic training data is using composable blocks that mix LLM-powered generation with traditional data processing to create diverse text examples and structure them into datasets.

Does this synthetic data framework require any external dependencies?▼

No, this synthetic data generation framework operates without external dependencies, allowing you to immediately create modular Python frameworks for complex data preparation and augmentation tasks.

How do composable blocks work in a modular Python framework for synthetic data?▼

Composable blocks work by allowing you to mix and match LLM-powered and traditional data processing components, enabling you to create custom workflows tailored for flexible data generation scenarios.

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