Skill UI

This skill provides zero-shot time series forecasting using Google's TimesFM foundation model. It forecasts any univariate time series (like sales, sensor data, or weather) without requiring custom model training. The model delivers point forecasts along with calibrated prediction intervals (quantiles), offering a robust and probabilistic view of future trends. It is ideal for rapid, reliable forecasting across diverse time-based datasets.

Detects abnormal access patterns across major cloud storage platforms, including AWS S3, GCS, and Azure Blob Storage. By analyzing cloud audit logs (CloudTrail, etc.), this tool identifies critical security threats such as after-hours bulk data downloads, sudden spikes in API calls (GetObject spikes), access from new geographic IPs, and potential data exfiltration. It utilizes statistical baselines and time-series anomaly detection for proactive threat hunting and security incident investigation.

This skill provides programmatic access to comprehensive global financial market data via the Alpha Vantage API. Users can retrieve time-series data, including historical prices (OHLCV) for equities, options, forex, commodities, and crypto. It also provides access to fundamental metrics (e.g., P/E ratio, market capitalization), economic indicators (e.g., GDP, CPI), and over 40 technical indicators (e.g., RSI, MACD), making it ideal for quantitative analysis and financial modeling.

Aeon is a comprehensive, scikit-learn compatible Python toolkit designed for advanced time series machine learning. It supports a wide range of temporal tasks, including classification, regression, clustering, forecasting, anomaly detection, and pattern recognition. Ideal for analyzing sequential data and extracting features from time-indexed observations.

A comprehensive research toolkit for building and auditing reproducible workflows for physiological time-series data. It specializes in preprocessing, event/interval analysis, multimodal alignment, and extracting variability and complexity features from signals like ECG and EDA. Designed strictly for academic and research use, emphasizing rigorous data contracts and method-aware validation, while strictly disclaiming any diagnostic or medical device validation role.

PyMC is a powerful Python library designed for probabilistic programming and sophisticated Bayesian modeling. It enables users to build complex models, such as hierarchical structures and time series, and perform rigorous inference using methods like MCMC (NUTS) sampling and Variational Inference. Key features include model comparison (LOO, WAIC) and comprehensive diagnostic tools for uncertainty quantification.

Statsmodels is a comprehensive Python library for rigorous statistical modeling, enabling deep inference, diagnostics, and advanced econometric analysis. Use it for tasks like linear regression (OLS), generalized linear models (GLM), time series forecasting (ARIMA), and hypothesis testing, providing detailed results for academic and scientific reporting.

Utilizes Google's TimesFM foundation model for zero-shot time series forecasting. It processes any univariate time series (e.g., sales, sensor data, weather) without requiring custom model training. The skill provides probabilistic forecasts with calibrated prediction intervals (quantiles) and includes a mandatory system checker to ensure sufficient RAM, GPU, and disk resources for safe local inference.

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 details advanced strategies for managing time-series data and data retention in vector databases (like Qdrant). It is designed for scenarios where only recent data is relevant (e.g., social media feeds, news articles). Three core methods are presented: Shard Rotation (recommended for natural time boundaries), Collection Rotation (for per-period configuration needs), and Filter-and-Delete (for simple cleanup). The guide also covers hot/cold tiering to optimize performance.

Track brand citation rates across ChatGPT, Perplexity, and other LLMs with time-series trends, prompt insights, competitor mentions, and spike/drop alerts for AI search visibility monitoring.

This tool ingests raw market data (OHLCV) for a specified symbol. It normalizes the data into relative OHLCV vectors, converts these vectors into high-dimensional embeddings, and then stores and indexes them using HNSW. This process prepares the data for fast, similarity-based pattern detection and advanced financial time series analysis.