weather-ensemble-forecastMulti-model ensemble weather forecasts comparing GFS, ECMWF, JMA, GEM, ICON, ARPEGE, GraphCast and more. Get high temperature predictions from up to 9 indepe...
Install via ClawdBot CLI:
clawdbot install Lucasnocodo/weather-ensemble-forecastGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Sends data to undocumented external endpoint (potential exfiltration)
POST → https://polymarket-scanner.fly.dev/keys/freeCalls external URL not in known-safe list
https://polymarket-scanner.fly.dev/keys/freeAI Analysis
The skill's external API calls are consistent with its stated purpose of fetching weather forecasts, and the documented endpoint for obtaining a free API key appears functional rather than malicious. However, the lack of full endpoint documentation and the potential for sending location data (city queries) to an external server warrants a low-risk classification for transparency.
Audited Apr 16, 2026 · audit v1.0
Generated Mar 22, 2026
Event planners can use this skill to compare multiple weather models for a specific city and date, such as a wedding day, to assess forecast confidence. By analyzing ensemble standard deviation, they can identify high-risk days where models disagree and make informed decisions about backup plans or date changes.
Farmers can leverage the ensemble forecasts to predict temperature extremes for their region, helping to schedule irrigation or frost protection measures. The multi-model comparison provides a more reliable forecast than single sources, reducing crop loss risks due to unexpected weather changes.
Travel agencies and individual travelers can use this skill to check high-confidence temperature forecasts for destinations like London or Seoul, ensuring better packing and activity planning. The agreement analysis warns of uncertain weather, allowing for flexible itineraries in case of model disagreements.
Utility companies can integrate this skill to predict temperature trends for cities like Chicago or Toronto, aiding in energy demand forecasting. The ensemble data helps optimize grid management by providing a range of predictions, improving reliability during peak usage periods.
Researchers can access raw multi-model data from sources like GFS and ECMWF for cities such as Buenos Aires, enabling comparative studies on forecast accuracy. This supports academic projects in climate science by providing easy access to ensemble statistics for analysis.
Offer a free tier with limited cities (e.g., NYC, Chicago, Miami) and basic features, while charging for full access to all 16 cities and advanced analytics. Revenue can be generated through subscription plans for businesses needing reliable, high-volume forecast data.
License the ensemble forecast data to industries like agriculture, energy, or logistics for integration into their internal systems. This model involves one-time or recurring fees based on data usage, providing tailored datasets for operational decision-making.
Provide consulting services to event planners or prediction market traders, using the skill's analysis to offer insights on forecast uncertainty and risk assessment. Revenue comes from project-based fees or ongoing advisory contracts.
💬 Integration Tip
Ensure the system has curl and jq installed, and set the API key via environment variable for full city access to avoid rate limits.
Scored Apr 19, 2026
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天气顾问。智能天气顾问。实时天气查询、未来7天预报、穿衣建议与出行活动推荐 Keywords: 天气查询, weather, 穿衣建议, 出行提醒.
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