Skill UI

Use Declarative Automation Bundles (DABs) to manage and deploy Databricks assets, including jobs, DLT pipelines, and ML models, using an Infrastructure-as-Code approach. This skill allows defining resources and deployment targets (dev, staging, prod) in a single YAML file, enabling robust CI/CD practices and controlled promotion of changes across environments.

Enables Claude to apply anomaly-detection-system workflows for spotting outliers, fraud, or unusual patterns across datasets, selecting tailored algorithms like Isolation Forest and summarizing deviations for any data monitoring or security use case.

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.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This skill provides comprehensive commands for creating, managing, versioning, and querying structured datasets within the Arize platform. It covers dataset CRUD operations, appending examples, and exporting data in various formats (JSON, CSV, Parquet) using the `ax` CLI. Ideal for preparing test data, generating evaluation examples, or retrieving golden datasets for LLM development and model testing.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

A comprehensive skill to extract and transcribe subtitles from any YouTube video URL. It utilizes a two-stage approach: first, attempting fast extraction via the yt-dlp CLI tool; failing that, it falls back to advanced browser automation using Chrome DevTools MCP to scrape the transcript panel content directly from the webpage DOM. The output is always a clean, timestamped plain text file.

This tool allows users to export comprehensive marketing data—including KPIs, contact records, campaign results, and performance snapshots—to external data stores. It supports integration with BigQuery for advanced data warehousing, Google Sheets for stakeholder collaboration, and Supabase for custom application development. The process includes mandatory PII handling, schema transformation, and detailed execution summaries to ensure compliance and data integrity for downstream analytics and reporting.

This guide outlines the scope and submission requirements for the journal China Sport Science and Technology. It focuses specifically on application-oriented exercise science, covering competitive training, physical conditioning, physiological monitoring, technical analysis, and sports rehabilitation. It helps researchers determine the best fit for their work, distinguishing between purely theoretical research and practice-guided, evidence-based studies for athletic development.

A comprehensive guide for developing high-rigor, reader-ready tables and figures for Human Resource Management (HRM) manuscripts. This skill covers the strict conventions required for presenting descriptive statistics, correlation matrices, nested model build-up (HLM/SEM), interaction plots, and theoretical model mapping. It ensures that the visual exhibits not only present numbers but also clearly carry the research argument, adhering to top-tier academic standards.

This guide provides best practices for presenting empirical results in development economics, emphasizing a 'figure-forward' approach suitable for top-tier journals. It teaches researchers how to transform complex statistical models (like DID, RDD, and ITT) into clear, self-contained, and impactful visual exhibits (plots and maps). The goal is to ensure that the headline findings are immediately legible to expert referees, moving beyond traditional, complex coefficient tables.

This comprehensive guide assists in developing rigorous research designs for family science. It covers quantitative methodologies (longitudinal, dyadic, life-course, event history), qualitative approaches, and mixed-methods integration. The focus is on strengthening the design's defensibility by explicitly addressing units of analysis, interdependencies, and critical statistical challenges like selection bias and non-independence.