Skill UI

This skill scrapes Substack Notes analytics by extracting key engagement metrics (likes, comments, restacks) and content details. It overcomes the lack of a public API, allowing users to analyze performance data from a specified date range into a highly formatted, actionable Excel spreadsheet. The output includes detailed per-note data, total engagement calculations, and summary statistics.

A read-only tool designed to compare prediction-market baskets or themes against the user's existing knowledge base, portfolio notes, research memos, or financial context. It performs gap analysis, flagging potential conflicts, stale assumptions, and areas of missing research. This skill provides structured insights into fit and exposure without offering any investment or trading advice.

This guide provides comprehensive guidelines for preparing high-quality, publication-ready tables and figures specifically tailored for Journal of Banking & Finance (JBF) submissions. It covers everything from structuring descriptive statistics and regression results to designing event-study plots and ensuring proper notes and execution workflows.

A comprehensive guide detailing the conventions for constructing high-quality empirical tables and figures, especially for corporate finance and econometrics journals. Covers best practices for reporting summary statistics, fixed effects, clustering, event-study/CAR plots, and ensuring self-contained notes for academic publication.

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.

A comprehensive guide for formatting and creating high-impact tables and figures for academic submissions, particularly adhering to The Journal of Law and Economics (JLE) standards. It teaches how to visually present the main effect of a legal rule (e.g., event-studies, RD, DiD) clearly and concisely, ensuring the core identification is legible at a glance. Covers best practices for standard errors, clustering, and self-contained academic notes.

This comprehensive guide details professional standards for presenting complex empirical economic results (tables and figures) for top-tier journals like The Journal of World Economy. It instructs users on simplifying main regression tables, standardizing notes (clustering, significance), and correctly presenting specialized open-economy objects such as event studies, gravity regressions, and GVC decompositions, ensuring research meets publication quality.

This guide assists researchers in evaluating the fit of their manuscripts for the journal Scientific Decision Making. It covers journal scope, required methodologies (e.g., system modeling, policy evaluation), structural improvements, common formatting pitfalls (footnotes, Arabic numerals), and cross-journal selection criteria to maximize acceptance chances.

This skill maps chronic conditions extracted from clinical notes to CMS-HCC V28 categories. It estimates the Risk Adjustment Factor (RAF) score, which is crucial for Medicare Advantage payments. It serves as decision support for coders by identifying candidate HCCs, applying complex coding hierarchies, and checking for MEAT documentation evidence for human validation.

This skill facilitates the computation of Electronic Clinical Quality Measures (eCQMs) by leveraging CQL (Clinical Quality Language) logic against the QDM (Quality Data Model). It is designed to extract crucial facts—such as counseling provided, symptoms, or documented exclusions—from unstructured clinical notes. By lifting these facts, it supplements structured EHR data, ensuring that numerators and valid exclusions are accurately captured for quality measure evaluation. Note that this tool augments data capture and does not replace certified measure engines.

This skill facilitates the ingestion of unstructured clinical notes and reports from FHIR R4 EHR APIs (e.g., Epic, Cerner). It handles complex tasks including paginating large search results, decoding base64 attachments (PDF, text), and extracting narrative content. The extracted text is then passed to the OpenMed pipeline for HIPAA-compliant de-identification and subsequent NLP analysis, enabling structured data insights from raw clinical records.

This skill replaces detected Protected Health Information (PHI) in clinical notes with realistic, type-matched synthetic surrogates. Unlike simple redaction, surrogates maintain the natural readability and structure of the text, ensuring the data remains parseable for downstream NLP tasks, QA, or human review. It supports consistent, reproducible fake identities and allows custom identifier generation for proprietary formats.