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sdg-hub

Name: sdg-hub
Author: red-hat-ai-innovation-team

By Red-Hat-AI-Innovation-Team

Generate LLM training datasets using sdg_hub with composable blocks and YAML-defined flows. Discovers and installs the environment, browses flow catalogs, configures API keys and models, then executes generation with row counts and error details.

Publisher marketplacesdg-hub@sdg-hub · marketplace and plugin share one repository (red-hat-ai-innovation-team/sdg_hub)

npx claudepluginhub red-hat-ai-innovation-team/sdg_hub --plugin sdg-hub

Popularity

Stars

Top 10%

149

Med: 0·Avg: 862

Copy clicks

Med: 0·Avg: 2

What's Inside

Skills4

data-generation

/data-generation

Use when the user wants to run synthetic data generation via scripts — detect environment, execute a flow, and present results. For detailed guidance on approaches, blocks, flow authoring, and troubleshooting, consult the synthetic-data-generation skill.

flow-browser

/flow-browser

Use when the user wants to list, search, or inspect available SDG flows and data generation pipelines. Applies to browsing flow catalogs, finding flows by use case, or understanding what a specific flow does.

setup-guide

/setup-guide

Use when the user wants to set up synthetic data generation for the first time, or when sdg_hub is not yet installed/configured in the current environment.

synthetic-data-generation

/synthetic-data-generation

Generate synthetic data using sdg_hub with composable blocks and YAML flows. Use when the user wants to create training datasets, generate QA pairs, run data generation pipelines, build custom flows, produce synthetic data from documents, use agent frameworks for data generation, or distill MCP tool-use traces. Supports pre-built flows, custom Python scripts, and YAML flow authoring with 20+ blocks, agent connectors (Langflow, LangGraph), MCP tool-use, and 100+ LLM providers via LiteLLM.

README

Stats

Version0.9.4

ReleasedJun 9, 2026

LanguagePython

Stars149

Forks59

MaintenanceExcellent

LicenseApache-2.0

Last CommitJun 28, 2026

AddedJun 4, 2026

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Available In

sdg-hub149

Get Started

pip install sdg-hub

from sdg_hub import FlowRegistry, Flow

# Discover and load a built-in flow
FlowRegistry.discover_flows()
flow = Flow.from_yaml(FlowRegistry.get_flow_path("MCP Server Distillation"))

# Configure and run
flow.set_model_config(model="openai/gpt-4o")
result = flow.generate(dataset)

See the Quick Start for a full walkthrough, or browse all built-in flows.

Documentation

Full documentation at ai-innovation.team/sdg_hub

Installation -- setup, optional dependencies, development install
Quick Start -- end-to-end walkthrough from loading a flow to generating data
Core Concepts -- blocks, flows, registries, and dataset handling
Block Reference -- LLM, parsing, transform, filtering, agent, and custom blocks
Flow Reference -- YAML schema, built-in flows, custom flows
API Reference -- auto-generated from source
Contributing -- development setup and contribution guidelines

Coding Agent Plugin

SDG Hub is available as a plugin for two coding agents, bringing synthetic data generation directly into your coding workflow.

Claude Code

Via org marketplace (recommended — includes all Red Hat AI plugins):

/plugin marketplace add Red-Hat-AI-Innovation-Team/plugins
/plugin install sdg-hub@Red-Hat-AI-Innovation-Team/plugins

Via this repo directly:

/plugin marketplace add Red-Hat-AI-Innovation-Team/sdg_hub
/plugin install sdg-hub@Red-Hat-AI-Innovation-Team/sdg_hub

From a local clone:

git clone https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub.git
/plugin marketplace add /path/to/sdg_hub

Codex CLI

codex plugin marketplace add Red-Hat-AI-Innovation-Team/plugins

Then install the plugin from the marketplace. See .codex-plugin/INSTALL.md for manual installation.

After Installing

Invoke the setup-guide skill to configure your LLM provider and model.

Skill	Description
`setup-guide`	Guided first-time configuration
`data-generation`	Run synthetic data generation using a flow
`flow-browser`	Browse and inspect available flows

License

Apache License 2.0 -- see LICENSE.

Built by the Red Hat AI Innovation Team

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