Open Meteo Weather logo

Open Meteo Weather

Organization
runxhq
open-meteo-weather

Resolve a place and fetch global forecast and air-quality evidence through governed, keyless Open-Meteo HTTP calls.

Overview

Publisherrunxhq
Repositoryrunx
Skill nameopen-meteo-weather
Stars
105
Forks
109
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by runxhq on GitHub. Read the source before you install it.

Installation

Install the Open Meteo Weather AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/runxhq/runx/tree/main/skills/open-meteo-weather
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/runxhq/runx.git /tmp/runx
mkdir -p .claude/skills
cp -r /tmp/runx/skills/open-meteo-weather .claude/skills/open-meteo-weather
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Open Meteo Weather in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Open Meteo Weather on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Open Meteo Weather is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Open-Meteo Weather

Fetch global place, forecast, and air-quality evidence through runx's governed HTTP front. The skill never performs an ad-hoc fetch. Each stage is a bounded, read-only HTTP runner whose endpoint, inputs, status, authority scope, and output packet are recorded in the sealed receipt. Open-Meteo requires no API key and covers the whole globe, so this is the worldwide counterpart to the US-only nws-weather provider.

Procedure

  1. Run locate with a city or place name.
  2. Select the intended result and preserve its latitude, longitude, timezone, and country as a typed provider-evidence packet.
  3. Run the default forecast runner with the selected coordinates.
  4. Require a 2xx response and preserve the provider URL, timezone, generation time, current conditions, daily forecast, and receipt references.
  5. Treat the result as context only. Any notification, scheduling change, or operational mutation requires a separate skill and authority gate.

Inputs

  • name (locate, required): city or place to resolve.
  • latitude (forecast, required): decimal latitude.
  • longitude (forecast, required): decimal longitude.
  • forecast_days (forecast, optional): days requested, default 3.

Output packets

Three runners are exposed. locate returns geocoding candidates. Select the intended candidate explicitly; ambiguous place names are not silently resolved. The default forecast runner takes that selected latitude and longitude and returns forecast evidence without geocoding again. air_quality likewise takes coordinates directly. There is no dependency between an unrelated place name and separately supplied coordinates. Each provider operation and its returned evidence remain in the sealed receipt.

Preserve provider metadata and receipt references when passing either packet to another skill. Do not convert provider evidence into high-stakes advice.

Failure, retry, and stop conditions

  • Ambiguous or missing place: return needs_input; do not silently choose a similarly named location.
  • Invalid coordinates or forecast-days value: return needs_input.
  • Non-2xx response, timeout, or rate limit: return needs_more_evidence, preserve the failure in the receipt, and retry only under the caller's retry policy.
  • Stale or incomplete provider response: return needs_more_evidence.
  • Life-safety, medical, aviation, maritime, or emergency use: refuse and direct the caller to official channels.
  • Mutation requested from forecast evidence: stop after the sealed read and require a separate authority-scoped action skill.

Worked example

  1. Run locate with name: Shanghai.
  2. Select the result for Shanghai, China.
  3. Run forecast using the sealed latitude and longitude with forecast_days: "3".
  4. Preserve both sealed receipts with the returned forecast evidence.

Credits

Combined from two community contributions: the forecast skill (#63, @why123-boop) and the air-quality snapshot (#64, @yanziwei).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Open Meteo Weather AI skill do?

Resolve a place and fetch global forecast and air-quality evidence through governed, keyless Open-Meteo HTTP calls.

Why use Open Meteo Weather on TypingMind?

Because you install it once and use it with any model. Open Meteo Weather is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Open Meteo Weather in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runxhq/runx/tree/main/skills/open-meteo-weather. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Open Meteo Weather?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Open Meteo Weather?

As many as you like. As long as a model supports skills, you can use Open Meteo Weather with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Open Meteo Weather AI skill free?

Yes. It is published on GitHub by runxhq under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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