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Review AI4S generated data for structure, scientific consistency, and hallucination risk.
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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.
Use MinerU to parse scientific PDFs into structured JSON for AI4S data generation.
Coordinate crawler, parser, synthesizer, and reviewer agents for AI4S data production.
Generate Sci-Evo style AI4S training data from structured paper content.
| name | data-quality-review |
| description | Review AI4S generated data for structure, scientific consistency, and hallucination risk. |
Use this skill when reviewing generated AI4S dataset entries.
Return actionable review notes. When rejecting a sample, explain the concrete reason and the minimum change required to repair it.
