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Generate Sci-Evo style AI4S training data from structured paper content.
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Based on SOC occupation classification
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
Review AI4S generated data for structure, scientific consistency, and hallucination risk.
Use MinerU to parse scientific PDFs into structured JSON for AI4S data generation.
Coordinate crawler, parser, synthesizer, and reviewer agents for AI4S data production.
| name | sci-evo-generation |
| description | Generate Sci-Evo style AI4S training data from structured paper content. |
Use this skill when converting parsed scientific papers into AI4S training examples.
Generated samples should be complete, internally consistent, and suitable for the downstream quality review workflow.
