القائمة
Use when the user wants a specific weather model, needs ensemble uncertainty ranges, requests seasonal outlooks, or asks for long-term climate projections.
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| name | open-meteo-advanced |
| description | Use when the user wants a specific weather model, needs ensemble uncertainty ranges, requests seasonal outlooks, or asks for long-term climate projections. |
Use this skill when:
For everyday weather questions, use open-meteo.
| Tool | Provider | Best For |
|---|---|---|
ecmwf_forecast | ECMWF IFS | Highest global accuracy |
gfs_forecast | NOAA GFS | Global, especially Americas |
dwd_icon_forecast | DWD ICON | High-resolution Europe |
meteofrance_forecast | Météo-France | France and nearby regions |
jma_forecast | JMA | Japan and Asia-Pacific |
metno_forecast | MET Norway | Nordic region |
gem_forecast | Environment Canada GEM | Canada and North America |
ensemble_forecast | Multi-model | Forecast uncertainty |
seasonal_forecast | ECMWF SEAS5 | 1-9 month outlook |
climate_projection | CMIP6 | Scenario-based future climate |
All model-specific forecast tools share the same parameter pattern as weather_forecast, plus a model key where required.
ensemble_forecastUse hourly variables and derive uncertainty ranges from the member arrays.
seasonal_forecastUse for anomaly-style outlooks from 45 to 274 days.
climate_projectionUse start_date, end_date, one or more CMIP6 models, and one or more daily variables.
geocodingdwd_icon_forecast and gfs_forecastgeocodingensemble_forecastgeocodingclimate_projectionseasonal_forecast as anomaly guidance rather than exact day-to-day prediction.climate_projection as scenario analysis, not observed historical weather.Walk any city in the world and publish to Agent Earth (agent-earth-oscar.vercel.app). Use when asked to explore, walk, or travel to a city/neighborhood. Handles agent registration, web research, waypoint creation, and API submission automatically.
Analyze biodiversity corridor connectivity and ecological value for H3 parcel clusters using a remote landscape ecology service.
Find carbon emission factors for any material or process. 1M+ LCA datasets (HiQLCD, Ecoinvent, CarbonMinds). AI-powered BOM carbon footprint calculation and material comparison.
Search academic literature across major scholarly sources and return a fast, ranked Top 5 with publication-status checks and bibliographic metadata.
Compare academic research papers side-by-side to identify similarities, differences, and research gaps. Use when user wants to compare 1-5 papers via DOIs, URLs, search queries, or PDF files. Supports mixed input types. Outputs both comparison table and detailed narrative summary.
Persistent cross-session knowledge base for research papers. Ingest arXiv/DOI → extract method, gap, threat level → append to PAPERS.md. Never lose paper context again across sessions.