Skill UI

Statsmodels is a robust Python library designed for comprehensive statistical modeling and econometric analysis. It provides tools for classical linear regression (OLS), generalized linear models (GLM like Poisson and Logistic), time series forecasting (ARIMA), and mixed-effects models. Use this skill for rigorous statistical inference, detailed diagnostic testing, and generating publication-ready statistical tables.

This skill utilizes Google's TimesFM foundational model for zero-shot time series forecasting. It processes any univariate time series (e.g., sales, sensor data, weather) without requiring custom training. Users receive point forecasts alongside calibrated prediction intervals (quantiles), enabling robust probabilistic analysis. The skill includes a mandatory preflight checker to validate system resources (RAM/GPU/Disk) before model loading.

Access the U.S. Department of the Treasury's free, open REST API to retrieve comprehensive federal financial data. This tool allows users to query key metrics such as national debt, interest rates, exchange rates, daily treasury statements, and auction data. Built with Python and Pandas, it is perfect for financial analysis, economic monitoring, and tracking public sector finances without needing an API key.

This skill connects to the DeepEar Lite platform to fetch real-time, high-frequency financial signals. It retrieves crucial data points including signal titles, sentiment summaries, confidence scores, and detailed reasoning. Use this when you require immediate, actionable insights into volatile market trends, stock performance factors, or comprehensive financial analysis.

This skill utilizes the Kronos model to perform advanced financial time-series forecasting. It generates quantitative base predictions and then integrates news sentiment analysis to adjust and refine the forecast, making it an essential tool for finance professionals needing actionable market insights.

This comprehensive guide provides advanced workflows for interacting with ClickHouse. It covers efficient bulk data insertion (CSV, JSON), complex analytical querying (funnel analysis, retention), implementing materialized views for pre-aggregation, and utilizing powerful window functions. Ideal for building high-performance OLAP dashboards and executing large-scale data transformations.

This comprehensive guide teaches users how to set up a ClickHouse database environment. It covers creating a MergeTree table, inserting structured event data using TypeScript, and executing essential analytical queries (e.g., counting, grouping, unique user metrics). Ideal for beginners learning OLAP databases, testing connections, or mastering core data warehousing concepts.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

This skill provides a comprehensive workflow for generating professional and structured financial reports. It guides the user through planning, writing, and editing various sections, and can also generate necessary chart configurations using visual tools. Ideal for condensing complex financial analysis into clear, actionable reports.

Execute comprehensive, asynchronous transcription workflows using AssemblyAI. This tool handles file upload, queues jobs, and provides advanced audio intelligence features. Capabilities include speaker diarization, sentiment analysis, named entity recognition (NER), topic categorization (IAB), content safety moderation, and sensitive PII redaction. Ideal for processing meeting recordings, interviews, and large volumes of spoken content.

This skill provides comprehensive access to Bright Data's SERP API and Web Scraper API. It allows users to collect structured search engine results (Google, Bing, Yahoo) synchronously, or trigger large-scale, asynchronous data collections with webhooks. Ideal for market research, SEO analysis, and large-scale data scraping projects.

This internal agent skill processes raw X/Twitter posts into structured bug candidates. It systematically performs parsing, content deduplication, PII redaction, reliability scoring, and classification. Finally, it clusters related reports using deterministic signatures, providing a highly organized output for efficient bug triage and analysis from large-scale social media streams.