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.

Utilize Google's TimesFM foundation model for zero-shot univariate time series forecasting without training custom models. Supports sales, sensor, and weather data with point forecasts and prediction intervals. Includes mandatory system checks for RAM and GPU before inference to prevent crashes.

A comprehensive guide for establishing robust causal links in environmental economics. It covers advanced econometric techniques for policy evaluation, including staggered Difference-in-Differences (DiD), Instrumental Variables (IV), and Regression Discontinuity Designs (RDD). It specifically addresses challenges in non-market valuation, such as spatial sorting, hypothetical bias, and separating weather shocks from long-term climate trends, ensuring the recovered parameter is welfare-relevant.

Analyze weather radar and satellite observations as remote-sensing data: validate product metadata, geometry and calibration, derive storm and cloud structures, track their evolution, and quantify uncertainty for short-term nowcasting.

Create and verify provenance manifests for weather-data inputs and derived artifacts, capturing object identity, selections, software versions, transformations, and checksums to support traceable, replayable, or reproducible workflows.

Retrieve numerical weather prediction data from public AWS S3 and HTTP archives using GRIB2 inventories and byte ranges. Supports Herbie, provider fallbacks, and verified caching. Ideal for fetching GFS, HRRR, or RAP model outputs where specific variables or levels are needed. Does not interpret forecasts.

Resolve the newest complete numerical weather prediction cycle and forecast objects across provider mirrors without downloading full payloads. This skill probes exact objects or narrow prefixes, verifies request-specific completion contracts, and returns the newest qualifying initialization with source metadata.

Retrieve surface and upper-air weather observations from NOAA APIs and archives, preserving station identity, time, units, and quality flags. Covers METARs, ISD, IGRA radiosondes, and station metadata for analysis-ready data.

This skill diagnoses latency and throughput in weather data workflows by measuring stages like discovery, transfer, parsing, and rendering separately. It identifies bottlenecks before code changes, supporting profiling and validation of performance fixes. It ensures accurate optimization by isolating slow steps in the pipeline.