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openmeteo-sh-weather-advanced

Advanced weather from free OpenMeteo API: historical data, detailed variable selection, model choice, past-days, and in-depth forecasts. Use when the user asks about historical weather, specific weather models, niche variables (pressure, dew point, snow depth, etc.), or needs fine-grained control beyond simple current/forecast queries.

Why use this skill?

Integrate advanced weather data into OpenClaw. Query historical records from 1940, detailed forecasts, and niche meteorological variables with this professional-grade skill.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/lstpsche/openmeteo-sh-weather-advanced
Or

What This Skill Does

The openmeteo-sh-weather-advanced skill serves as a high-precision bridge between OpenClaw and the extensive OpenMeteo API. Unlike basic weather checkers that only provide a brief temperature summary, this tool enables deep meteorological data retrieval. Users can query precise historical records dating back to 1940, generate detailed 16-day forecasts, and perform granular variable selection for parameters like dew point, snow depth, and barometric pressure. It is designed for researchers, developers, and enthusiasts who require raw data integrity, model flexibility, and fine-tuned units without the overhead of API keys or complex authentication.

Installation

To integrate this advanced weather monitoring into your environment, use the OpenClaw hub CLI tool: clawhub install openclaw/skills/skills/lstpsche/openmeteo-sh-weather-advanced

Use Cases

This skill is indispensable for scenarios requiring more than basic surface information. Use it to analyze historical crop growing conditions for specific farms, verify climate patterns at exact GPS coordinates for civil engineering projects, or conduct comparative analysis between different weather prediction models. It is particularly powerful for those needing to slice weather data by specific time windows or specific units (e.g., knots for maritime planning or Fahrenheit for US regional reporting).

Example Prompts

  1. "Check the weather in Tokyo for the next 10 days, but specifically show me the humidity and dew point every 3 hours."
  2. "What was the precipitation total and average temperature in Seattle throughout January 2023?"
  3. "Show me the current weather at 34.0522, -118.2437 using the ECMWF model and give me the wind speed in knots."

Tips & Limitations

  • Efficiency: Always use the --llm flag. This ensures the output is formatted as a compact TSV (Tab-Separated Values) file, which is significantly easier for LLMs to parse and interpret compared to raw JSON or unstructured text.
  • Granularity: If you are unsure of specific variable names for your queries, you can append help to your parameter flags (e.g., openmeteo weather help --hourly-params) to view a list of supported data fields directly in your console.
  • Defaults: By default, the tool uses an auto-detected best-match model. Only specify a --model flag if you have a specific scientific requirement for data sources like CERRA or ERA5.
  • Scope: While this tool is powerful, it is intended for meteorological data only. It does not provide radar imagery, satellite photos, or severe weather alerts/push notifications.

Metadata

Author@lstpsche
Stars1601
Views0
Updated2026-02-27
View Author Profile
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-lstpsche-openmeteo-sh-weather-advanced": {
      "enabled": true,
      "auto_update": true
    }
  }
}

Tags(AI)

#weather#meteorology#climate#history#forecast
Safety Score: 5/5

Flags: network-access, external-api