Skill UI

This SDK allows developers to build robust anomaly detection applications in Java using Azure AI services. It supports both univariate and multivariate time-series analysis, enabling detection of outliers, trend changes, and correlated signals over large datasets. Ideal for real-time monitoring, operational intelligence, and advanced data quality checks.

This SDK allows developers to build robust anomaly detection applications in Java using Azure AI services. It supports both univariate and multivariate time-series analysis, enabling detection of outliers, trend changes, and correlated signals over large datasets. Ideal for real-time monitoring, operational intelligence, and advanced data quality checks.

This SDK provides a comprehensive Java client for interacting with Azure Monitor. It allows developers to execute powerful Kusto Query Language (KQL) queries against Log Analytics workspaces to retrieve detailed operational logs, and to query time-series metrics from various Azure resources. It supports both synchronous and asynchronous operations, making it ideal for building monitoring, auditing, and analytics features in Java applications.

An expert architect specializing in designing, optimizing, and scaling entire data layers from inception (greenfield) to re-architecture. Proficient in selecting the optimal database technology (SQL, NoSQL, Graph, Time-series), designing robust schemas, implementing sophisticated indexing strategies, and planning complex migration paths. Suitable for defining data storage solutions that meet high performance, scalability, and data integrity requirements.

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.

Automates designing and implementing table partitioning strategies to keep large databases performant, optimize time-series workloads, and shorten maintenance windows, with guidance on key selection, SQL scripts, and query tuning.

An expert guide for performing complex data analysis, cleaning, and transformation using pandas DataFrames. This skill covers advanced techniques such as vectorized operations, efficient merging, time series resampling, group-by aggregations, and memory optimization, ensuring production-grade code and robust data validation.

Automates time series decomposition work in data analytics by guiding through patterns and best practices, generating production-ready code/configuration, and validating outputs for SQL, visualization, and BI workflows.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature extraction on temporal data.

Access over 800,000 economic time series from FRED's API, covering GDP, employment, inflation, interest rates, regional metrics, and more; ideal for macroeconomic analysis, financial research, policymaking, and forecasting workflows.

Access aggregated OFR hedge fund time series and metadata without auth, covering Form PF stats, repo volumes, futures trader positioning, and leverage/counterparty metrics for risk monitoring or research.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.