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sdg_hub
sdg_hub contient 4 skills collectées depuis Red-Hat-AI-Innovation-Team, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
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.
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.
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.
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.