Skill UI

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.

Enables Claude to analyze historical time series patterns, select the best forecasting model, and predict future values with confidence intervals for sales, traffic, or other time-dependent metrics.

Configure automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for Juicebox AI data analysis integrations. This setup utilizes GitHub Actions to run comprehensive unit tests (with mocked datasets) and integration tests (connecting to live APIs) on every PR and merge to main. It ensures that data upload logic, analysis execution, and result parsing workflows are robust and reliable before deployment.

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.

This guide provides comprehensive patterns and best practices for designing high-impact Key Performance Indicator (KPI) dashboards. It covers everything from establishing a KPI framework (Strategic, Tactical, Operational) to defining departmental metrics (Sales, Marketing, Finance) and implementing best-practice layouts for executive summaries and SaaS metrics. Use this skill to translate raw data into actionable business insights.

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

Provides automated guidance for managing model versions in ML deployment, including serving, monitoring, and production optimization whenever someone requests the model versioning manager.

This skill provides comprehensive monitoring and comparative analysis of cross-chain bridges in the DeFi ecosystem. It tracks key metrics such as Total Value Locked (TVL), transaction volume, typical fees, and real-time transfer status across major networks (e.g., Ethereum, Arbitrum, Polygon). Use this tool to research bridge safety, compare transfer routes before depositing funds, or track specific transaction hashes across multiple protocols like Wormhole, LayerZero, and Stargate.

Guides production ML engineering with patterns for deploying models, building MLOps pipelines, monitoring drift, integrating LLMs and RAG systems, and enforcing feature-store, retraining, and cost controls in live environments.

Guides TensorFlow Serving setup with step-by-step ML deployment orchestration, enforcing best practices, ready-to-run configurations, and output validation for production inference services.

Access DepMap data to fetch CRISPR Chronos dependency scores, drug sensitivity readouts, and gene effect profiles for cancer cell lines, supporting target validation, biomarker discovery, and synthetic-lethality research via API or downloads.

A comprehensive guide to deploying and configuring osquery across diverse endpoints (Windows, macOS, Linux). This tool allows security teams to gather deep, SQL-based insights into endpoint state, including running processes, network connections, system configuration, and installed software. It is essential for threat hunting, compliance auditing, and building centralized fleet visibility.