Skill UI

Master the creation of robust ELT (Extract, Load, Transform) data pipelines within Snowflake. This guide demonstrates advanced data orchestration using Snowflake Streams for Change Data Capture (CDC), Tasks for scheduled transformations and DAG management, and Dynamic Tables for declarative, auto-refreshing data models. Ideal for building sophisticated, scalable data warehousing solutions.

This guide provides best practices for creating high-impact tables and figures (exhibits) for academic journals, especially in behavioral economics. It emphasizes visualizing the core behavioral comparison—such as treatment effects, dynamic changes over rounds, or underlying distributions—so the main finding is immediately visible before reading any text. Key practices include using confidence intervals over significance stars, ensuring grayscale readability, and providing self-contained captions.

This guide outlines the rigorous standards for presenting tables and figures in manuscripts submitted to The Journal of Finance (JF). It emphasizes the need for self-contained, accessible exhibits, ensuring that readers from any subfield can interpret the results solely from the caption and notes. Key standards include using economic units (bps, % of market cap), proper numbering schemes (Roman/Arabic), and separating decisive findings from exhaustive details in the Internet Appendix.

This guide helps researchers adhere to the strict submission guidelines of the Journal of Monetary Economics (JME). It details how to manage and optimize the presentation of empirical evidence, ensuring the manuscript stays within the combined limit of 10 tables and figures, and under the 40-page length cap. It guides the decision of which exhibits belong in the main body versus the online supplementary appendix.

Provides detailed guidelines for authors submitting manuscripts to academic journals, specifically addressing the strict limit of 10 tables and figure panels, along with a 10-page supplementary cap. This guide teaches authors how to critically select, consolidate, and present key research results—such as marginal effects, event studies, or decomposition plots—to ensure every exhibit is self-contained, highly clear, and robust enough to pass expert review.

This skill guides the creation of computable phenotypes and patient cohorts using the OHDSI/OMOP CDM standards. It allows users to define precise, executable criteria by combining standard concept sets (like PheKB) with advanced Natural Language Processing (NLP) features extracted from free clinical text. Use this when structured codes alone are insufficient to capture critical health information (e.g., symptoms, social context).

This skill automatically detects and extracts tabular laboratory panels from diverse sources, including scanned images, photos, and delimited files (CSV/TSV). It converts complex visual data into clean, structured rows, capturing the test name, measured value, unit, reference range, and abnormal flag. It is essential for preparing medical data for subsequent standardization using LOINC and UCUM, and for mapping into FHIR resources.