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itechmeat/llm-code92 installs

open-meteo

Integrate Open-Meteo Weather Forecast, Air Quality, and Geocoding APIs: query design, variable selection, timezone/timeformat/units, multi-location batching, and robust error handling. Use when fetching weather forecasts, air quality/pollen data, or geocoding place names to coordinates via Open-Meteo. Keywords: Open-Meteo, /v1/forecast, /v1/air-quality, geocoding-api, hourly, daily, current, timezone=auto, timeformat=unixtime, models, WMO weather_code, CAMS, GeoNames, httpx, FastAPI, pytest.

How do I install this agent skill?

npx skills add https://github.com/itechmeat/llm-code --skill open-meteo
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    This skill provides a standard integration for the Open-Meteo weather service, allowing the agent to look up weather forecasts and air quality data. The skill includes Python examples for making network requests to the service. While the skill is safe and uses a well-known weather provider, the process of fetching external data always presents a minor risk of indirect prompt injection. No malicious patterns or security vulnerabilities were identified.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

  • Runlayerpass

    1/7 files flagged

What does this agent skill do?

Open Meteo

Goal

Provide a reliable, production-friendly way to call Open-Meteo APIs (Forecast, Air Quality, Geocoding), choose variables, control time/units/timezone, and parse responses consistently.

Steps

  1. Pick the correct API and base URL

    • Forecast: https://api.open-meteo.com/v1/forecast
    • Air Quality: https://air-quality-api.open-meteo.com/v1/air-quality
    • Geocoding: https://geocoding-api.open-meteo.com/v1/search
  2. Resolve coordinates (if you only have a name)

    • Call Geocoding with name and optional language, countryCode, count.
    • Use the returned latitude, longitude, and timezone for subsequent calls.
  3. Design your time axis (timezone, timeformat, and range)

    • Prefer timezone=auto when results must align to local midnight.
    • If you request daily=..., set timezone (docs: daily requires timezone).
    • Choose timeformat=iso8601 for readability, or timeformat=unixtime for compactness.
      • If using unixtime, remember timestamps are GMT+0 and you must apply utc_offset_seconds for correct local dates.
    • Choose range controls:
      • forecast_days and optional past_days, or
      • explicit start_date/end_date (YYYY-MM-DD), and for sub-daily start_hour/end_hour.
  4. Choose variables minimally (avoid "download everything")

    • Forecast: request only the variables you need via hourly=..., daily=..., current=....
    • Air Quality: request only the variables you need via hourly=..., current=....
    • Keep variable names exact; typos return a JSON error with error: true.
  5. Choose units and model selection deliberately

    • Forecast units:
      • temperature_unit (celsius / fahrenheit)
      • wind_speed_unit (kmh / ms / mph / kn)
      • precipitation_unit (mm / inch)
    • Forecast model selection:
      • default models=auto / “Best match” combines the best models.
      • you can explicitly request models via models=....
      • provider-specific forecast endpoints also exist (provider implied by path). See references/models.md (section "Endpoints vs models=") for examples and doc links.
      • for provider/model-specific selection tradeoffs, see references/models.md.
    • Air Quality domain selection:
      • domains=auto (default) or cams_europe / cams_global.
  6. Implement robust request/response handling

    • Treat HTTP errors and JSON-level errors separately.
    • JSON error format is:
      • {"error": true, "reason": "..."}
    • When requesting multiple locations (comma-separated coordinates), expect the JSON output shape to change to a list of structures.
    • Optionally use format=csv or format=xlsx when you need data export.
  7. Validate correctness with a “known city” check

    • Geocode “Berlin” → Forecast hourly=temperature_2m for 1–2 days → verify timezone and array lengths.
    • Air Quality hourly=pm10,pm2_5,european_aqi → verify units and presence of hourly_units.

Critical prohibitions

  • Do not include out-of-scope APIs in this skill’s implementation guidance: Historical Weather, Ensemble Models, Seasonal Forecast, Climate Change, Marine, Satellite Radiation, Elevation, Flood.
  • Do not omit timezone when requesting daily variables (per docs).
  • Do not assume unixtime timestamps are local time; they are GMT+0 and require utc_offset_seconds adjustment.
  • Do not silently ignore {"error": true} responses; fail fast with the provided reason.
  • Do not request huge variable sets by default; keep queries minimal to reduce payload and avoid accidental overuse.

Definition of done

  • You can geocode a place name and obtain coordinates/timezone.
  • You can fetch Forecast data with at least one hourly, one daily (with timezone), and one current variable.
  • You can fetch Air Quality data for at least one pollutant and one AQI metric.
  • Your client code handles both HTTP-level failures and JSON-level error: true with clear messages.
  • Attribution requirements from the docs are captured for Air Quality (CAMS) and Geocoding (GeoNames).

Links

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/itechmeat/llm-code/open-meteo">View open-meteo on skillZs</a>