Skill UI

Automated Data Quality Checker offers guidance, best practices, and production-ready code for validating pipelines whenever you work with ETL, streaming, or data transformation scenarios.

This skill guides authors in determining the suitability of their manuscripts for the journal Genome Biology. It details the journal's scope, which requires integrating large-scale genomic or epigenomic data with novel computational methods or statistical frameworks. It emphasizes that successful submissions must provide both methodological advances and meaningful biological insights, adhering strictly to standards like open-source code, public data deposition (GEO, SRA, ENCODE), and rigorous benchmarking.

A specialized tool for applied microeconomists to assess the suitability of their empirical work—covering labor, education, health, and welfare—for the Journal of Human Resources (JHR). It evaluates the paper's fit, methodological rigor, identification strategy, and policy relevance, providing actionable advice on framing and alternative submissions.

This comprehensive skill guides authors through the specific submission requirements and editorial culture of the Journal of Machine Learning Research (JMLR). It helps authors determine if their ML method, theory paper, or dataset is appropriate for JMLR's high standards—emphasizing rigor, complete proofs, open code, and lasting scientific contribution over novelty signaling. Use this when targeting JMLR or comparing it against other top-tier ML venues.