🌤️ Wettersensor-Array

Wetterdaten · Umweltbewusstsein · Echtzeit-Stream

Give your agent a sixth sense for sunshine, rain, and snow. Through high-speed access to global meteorological radar networks, LLMs can make travel arrangements, natural disaster alerts, and IoT automation decisions based on precise weather data.

OpenClaw Team

🚀 Schnellinstallation

Führen Sie den folgenden Befehl in Ihrem Terminal aus, um zu installieren:

npx clawhub install weather

📊 Statistikübersicht

⭐ Sterne	☁️ Gesamtaufrufe	👥 Aktive Nutzer	🎯 Stabile Version
512	3.18M	2,800	v1.4.2

🎛️ So funktioniert es

This isn't simply scraping weather website pages. This module serves as a foundational plugin, using high-frequency polling to query top-tier meteorological providers (like OpenWeatherMap) via satellite APIs:

📍 Dual Coordinate & City Name Resolution: Features powerful reverse geocoding. Not only can it pull weather data via lat,lon absolute coordinates, but also perfectly supports global fuzzy region / city-level names (e.g., Tokyo, JP).
🔮 Multi-dimensional Dynamic Weather Parameter Matrix: The model gets far more than just "sunny/rainy" basic info. The response package includes real-time temperature, feels-like deviation, humidity/barometric readings, and multi-day precipitation probability curves — supporting complex decision analysis.
⚡ Ultra-low Latency Gateway: Edge-cached and accelerated for LLMs, intercepting invalid short-interval repeat requests. This makes the plugin overhead negligible during conversation Q&A, maintaining dialogue flow speed.
🚨 Extreme Weather Alert Triggers: Built-in severe weather event (Alerts) monitoring. When the system detects that a region has received national-level red warnings such as hurricanes or blizzards, it throws specific exceptions for the Agent to intercept.

🧭 Typische Anwendungsfälle

✈️ Szenario 1: Perfect Travel Route Planning & Rebooking Contingency

When you ask the AI agent to "plan my weekend business trip from Shanghai to Seattle." After checking flights, the model proactively queries both locations' 3-day weather data via the weather plugin. If it discovers a high-probability blizzard warning for Friday evening in Seattle, the model suggests rebooking the ticket and proactively adds "potential delays due to weather warnings" as alternative plans in the itinerary draft.

🏡 Szenario 2: Smart Home Energy Pre-loading Logic

If the LLM manages your home server and climate control network: it can call the weather forecast skill every 6 hours to detect the next day's solar intensity. Once it determines a prolonged period of extreme heat is coming, the model can preemptively lower all window shades and activate low-power AC for baseline cooling — dramatically improving quality of life.

💻 Befehlsreferenz

Nach der Installation können Sie die KI diese autonom über Konversation aufrufen lassen oder Operationen manuell über die CLI auslösen:

Quick query for real-time weather report of a specific city:

clawhub execute weather city="Tokyo" units="metric"

Fetch a 3-day trend forecast for specific geographic coordinates:

clawhub execute weather lat=40.7128 lon=-74.0060 type="forecast" limit=3

Combined with other executors — check weather alerts then relay via Slack:

clawhub execute weather city="Miami" type="alerts" | clawhub execute slack ...

🛡️ Voraussetzungen & Authentifizierung

🔑 Provider API Key: Uses OpenWeather as the default source layer. You need to apply for a free-tier API key from the official provider.
⚙️ Environment Variable Setup: Pre-load before terminal startup: export OPENWEATHER_API_KEY="xxx...". For other weather sources like QWeather, consult the module's advanced settings for endpoint configuration.

🔗 Quellcode auf GitHub ansehen