estimate-online-covariance
Provides sklearn-style estimators for incrementally updating covariance, correlation, precision, and location matrices from streaming observations using constant work per observat…
Queries WeatherFlow Tempest station data through REST and local UDP broadcasts, including current conditions, forecasts, historical observations, and decoded real-time sensor messages.
Query hyper-local weather from a WeatherFlow Tempest station over its REST API and the hub's local UDP broadcast: current conditions, forecast, historical observations, and real-time decoded datagrams (obs_st, rapid_wind, evt_precip, evt_strike, hub_status). Use when the user asks about weather, temperature, rain, wind, humidity, or forecast data from their own Tempest/WeatherFlow station, or wants to parse the hub's UDP port 50222 broadcast. Do not use this skill for generic or city forecasts without a Tempest station (public weather services serve those), for Shakespeare's play The Tempest or other literature questions, or for weather hardware from other vendors - the REST endpoints require a personal-use token and the UDP broadcast only exists on a Tempest hub's LAN.
evals/evals.json
JSON
Other
README.md
Markdown
Reference
references/cli-worked-recipes.md
Markdown
Reference
references/observation-layouts-and-units.md
Markdown
Reference
references/rest-api-and-auth.md
Markdown
Reference
references/udp-broadcast-protocol.md
Markdown
Script
scripts/tempest
Script
scripts/test_tempest.py
Python
SKILL.md
SKILL.md
Markdown
Provides sklearn-style estimators for incrementally updating covariance, correlation, precision, and location matrices from streaming observations using constant work per observat…
Enables Python agents to update, assess, compare, and select online covariance, correlation, and precision estimators for streaming or dynamic-universe data.
Wraps positional online covariance estimators to process keyed observations whose named series can remain fixed or change dynamically over time.
Recommends covariance estimator classes for a 2-D dataset using dimension, conditioning, rank, correlation, and tail-behavior features, and exposes those features for inspection.
Guides an AI agent through end-to-end Kaggle competitions, including validation, modeling, OOF predictions, ensembling, remote notebook pipelines, artifact handoffs, submission sc…
Analyzes e-commerce reviews using 22-dimension AI tagging to extract customer profiles, pain points, VOC, product opportunities, anomaly signals, reports, dashboards, and optional…
Assesses, designs, and evolves data architectures through workload analysis, platform and modeling tradeoffs, governance, data products, data mesh, event-driven flows, and migrati…
Queries Open Library's public catalog for books, authors, works, editions, ISBNs, ratings, and cover URLs through a keyless Python CLI.