Skill UI

This comprehensive checklist guides the deployment of Databricks data pipelines and jobs to a production environment. It covers critical stages including pre-deployment security hardening (Service Principals, Unity Catalog), validating infrastructure (cluster types, autoscaling), ensuring code quality (unit/integration tests, Delta Lake best practices), executing controlled deployment, and performing post-deployment health checks and monitoring setup. Ideal for CI/CD pipelines and go-live procedures.

This skill provides a comprehensive guide and implementation for deploying a production-ready, containerized integration service using Docker. It connects to the Juicebox API to manage datasets, execute advanced AI-powered analyses, and retrieve structured business insights. The service includes health check mechanisms and supports zero-downtime rolling updates, ensuring reliable real-time data processing and analysis.

Research what people actually say about any topic in the last 30 days. Pulls posts and engagement from Reddit, X, YouTube, TikTok, Hacker News, Polymarket, GitHub, and the web. Includes a doctor health check to diagnose broken or missing sources.

This module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

Generates a comprehensive and structured financial due diligence framework for any investment, M&A, or major partnership. It produces a detailed document request list, key analytical questions, red flags checklist, and a summarized financial health assessment, tailored to specific transaction types and diligence stages.

This skill integrates with Monte Carlo's observability platform to provide a comprehensive health report for data assets. It checks critical metrics including data freshness, active alerts, monitoring coverage, asset importance, and the health of upstream dependencies. Activates when users inquire about the status, reliability, or quality of a data table.

Provides a comprehensive framework for conducting and reporting robustness checks in health economics studies. It guides authors to organize findings by addressing specific threats to the core claim (e.g., selection bias, concurrent policy changes, measurement error) rather than simply appending multiple specifications. Essential for ensuring results are stable and defensible for top-tier journal submissions.

This tool guides researchers on whether their academic topic aligns with the scope of an empirical microeconomics journal, specifically the Journal of Human Resources (JHR). It emphasizes that the research must focus on policy-relevant, causal questions in core fields (e.g., labor, health, education), strictly differentiating it from purely organizational behavior or 'HR management' studies. It serves as a critical gatekeeper before formal submission.

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 guide assists researchers in determining the suitability of their study for The Lancet Public Health. It outlines the scope, emphasizing population-level interventions, global epidemiology, and health policy. It details stringent requirements for methodology, reporting guidelines (STROBE, CONSORT, PRISMA), addressing biases, and adhering to specialized journal house styles and submission protocols.