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Weather Ensemble Forecast
作者
Lucasnocodo
· GitHub ↗
· v1.0.0
· MIT-0
294
总下载
1
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install weather-ensemble-forecast
功能描述
Multi-model ensemble weather forecasts comparing GFS, ECMWF, JMA, GEM, ICON, ARPEGE, GraphCast and more. Get high temperature predictions from up to 9 indepe...
安全使用建议
This skill is coherent and small, but it depends entirely on a third‑party API server (default: https://polymarket-scanner.fly.dev). That server will see requests (city/date) and your IP, and if you set WEATHER_ENSEMBLE_API_KEY it will receive that key. If you are uncomfortable trusting that host, do not set the API key and/or set WEATHER_ENSEMBLE_HOST to a self-hosted or audited endpoint. Before installing, you may want to: (1) verify the polymarket-scanner.fly.dev service and its privacy policy; (2) run the included scripts locally to inspect responses; and (3) avoid storing unrelated secrets in environment variables the skill might read.
功能分析
Type: OpenClaw Skill
Name: weather-ensemble-forecast
Version: 1.0.0
The weather-ensemble-forecast skill provides multi-model weather predictions by querying a dedicated API (polymarket-scanner.fly.dev). The provided Bash scripts (forecast.sh and cities.sh) perform standard API requests using curl and parse results with jq, including basic input sanitization to prevent shell injection. No evidence of data exfiltration, unauthorized command execution, or malicious prompt injection was found.
能力评估
Purpose & Capability
Name/description match behavior: scripts call a weather-ensemble API, return per-model temperatures, compute ensemble stats server-side. Required binaries (curl, jq) are appropriate. The only external dependency is the listed Weather Ensemble API server.
Instruction Scope
SKILL.md and scripts instruct running two shell scripts that only call the configured HOST endpoint and parse JSON. They do not access arbitrary files, other env vars, or make unrelated network calls. Note: the skill relies entirely on the remote server (default polymarket-scanner.fly.dev) to aggregate models and cross-validate with NWS; the client scripts do not contact NWS directly.
Install Mechanism
There is no install spec and only two small shell scripts; nothing is downloaded or written to disk by an installer. This is low-risk and proportional.
Credentials
No required credentials. Scripts optionally read WEATHER_ENSEMBLE_API_KEY and WEATHER_ENSEMBLE_HOST (both reasonable and documented). No unrelated secrets or config paths are requested.
Persistence & Privilege
Skill is not always-on and does not request elevated persistence. It doesn't modify other skills or agent config. Normal autonomous invocation settings remain (not a special privilege).
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install weather-ensemble-forecast - 安装完成后，直接呼叫该 Skill 的名称或使用
/weather-ensemble-forecast触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Initial release: compare up to 9 weather models side-by-side for 16 global cities
元数据
常见问题
Weather Ensemble Forecast 是什么？
Multi-model ensemble weather forecasts comparing GFS, ECMWF, JMA, GEM, ICON, ARPEGE, GraphCast and more. Get high temperature predictions from up to 9 indepe... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 294 次。
如何安装 Weather Ensemble Forecast？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install weather-ensemble-forecast」即可一键安装，无需额外配置。
Weather Ensemble Forecast 是免费的吗？
是的，Weather Ensemble Forecast 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Weather Ensemble Forecast 支持哪些平台？
Weather Ensemble Forecast 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Weather Ensemble Forecast？
由 Lucasnocodo（@lucasnocodo）开发并维护，当前版本 v1.0.0。
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