From sdg-hub
Runs synthetic data generation via sdg_hub flows: detects environment, executes generation, and presents results with row counts and error details.
How this skill is triggered — by the user, by Claude, or both
Slash command
/sdg-hub:data-generationThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Execute synthetic data generation using sdg_hub flows. For approach selection, custom flow authoring, and block reference, consult the `synthetic-data-generation` skill.
Execute synthetic data generation using sdg_hub flows. For approach selection, custom flow authoring, and block reference, consult the synthetic-data-generation skill.
"${CLAUDE_PLUGIN_ROOT}/scripts/sdg_detect.sh"
library=missing or config=missing: invoke the setup-guide skill.library=installed, config=found)Proceed to Step 2.
If the user doesn't specify a flow, invoke the flow-browser skill to find one.
Recommend starting with --sample 2 for a dry run.
"${CLAUDE_PLUGIN_ROOT}/scripts/sdg_generate.sh" $ARGUMENTS
If generation failed, consult the synthetic-data-generation skill for troubleshooting.
npx claudepluginhub red-hat-ai-innovation-team/sdg_hub --plugin sdg-hubSets up sdg_hub for synthetic data generation: detects environment, installs if needed, collects API keys and model config.
Generates synthetic data for ML training, testing, and privacy. Covers LLM-based generation, tabular synthesis, and quality validation. Activated by references to synthetic data, fake data, SDV, Gretel.
Generates realistic synthetic data on Databricks using Spark and Faker. Supports serverless execution, multiple output formats (Parquet, JSON, CSV, Delta), and scales from thousands to millions of rows.