Skill UI

This comprehensive skill simulates an expert business analyst, specializing in transforming complex raw data into actionable, strategic insights. The scope covers modern Business Intelligence (BI) platforms (Tableau, Power BI), AI-powered predictive modeling, designing robust KPI/OKR frameworks, advanced financial modeling, and optimizing business processes. It is designed for users needing to drive data-driven decision-making and create executive-level reports.

This skill guides the process of transforming complex, raw data into compelling, actionable narratives. It covers the core structure (Setup, Conflict, Resolution), narrative arcs, and the three pillars (Data, Narrative, Visuals) needed to create high-impact presentations for executives, investors, and non-technical stakeholders. It ensures insights drive clear decisions.

A comprehensive runbook designed for on-call engineers to manage Databricks incidents, outages, and major job failures. It provides structured procedures for immediate triage, decision tree guidance (covering cluster failures, code errors, and data quality issues), and steps for evidence collection and postmortem documentation, ensuring rapid and systematic incident resolution.

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

This advanced toolkit provides a comprehensive framework for in-depth financial analysis. It covers key functions including financial ratio analysis, Discounted Cash Flow (DCF) valuation, budget variance analysis, and rolling forecast construction. It is essential for strategic decision-making, investment analysis, and management reporting across various industries.

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.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

Guides senior data scientists through designing controlled experiments, engineering structured features, training ML models, and translating results into decisions using Python, R, SQL, and tools like Scikit-learn, XGBoost, and MLflow.

Astropy is a Python library for astronomical research that handles unit-aware quantities, celestial coordinates, FITS files, cosmological models, precise time systems, and tabular data, letting you transform, convert, and analyze observational or theoretical datasets.

Generates publication-ready clinical decision support documents for pharma and clinical research, covering biomarker-stratified cohort analysis, evidence-graded treatment recommendations, statistical comparisons, and regulatory-ready LaTeX/PDF outputs.

Provide REST API access to ClinPGx, the successor to PharmGKB, for querying gene-drug interactions, CPIC/DPWG/FDA guidelines, allele functions, and evidence that supports precision dosing and pharmacogenomics-driven clinical decisions.

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.