
如果你使用 DeepSeek、元宝 AI、Copilot、ChatGPT 等 AI 工具,协助配置轩辕镜像、编写 docker pull 命令、修改 Docker Compose 镜像地址、配置镜像加速、排查镜像拉取失败、分析报错日志等问题,请先让 AI 阅读并遵守轩辕镜像的规则文档。
只需在 AI 对话中先发送下面这句话即可:
请先完整阅读并严格遵守以下文档中的全部规则与要求:
https://xuanyuan.cloud/agents.md
在未充分阅读并理解该文档前,不要生成任何命令、配置、修改建议、故障排查方案或技术回答。后续所有输出都必须严格以该文档中的规范为最高优先级执行。查看 agents.md 用法指南与完整示范。国内用户首推 元宝 AI、DeepSeek 的深度思考模式,不推荐豆包 AI;Cursor 等编辑器可在对话 @ 该链接,或加入 User Rules。 若 AI 无法访问外链,可 打开说明文档 复制全文粘贴。文档会随站点更新,复制内容可能过期,建议定期检查。
!https://github.com/startxfr/monkey-ops/raw/master/resources/images/logo.svg?sanitize=true
Monkey-Ops is a simple service implemented in Go which is deployed into a OpenShift v3.x and generates some chaos within it. Monkey-Ops seeks some Openshift components like Pods or DeploymentConfigs and randomly terminates them.
When you are implemented Cloud aware applications, these applications need to be designed so that they can tolerate the failure of services. Failures happen, and they inevitably happen when least desired, so the best way to prepare your application to fail is to test it in a chaos environment, and this is the target of Monkey-Ops.
Monkey-Ops is built to test the Openshift application's resilience, not to test the Openshift V3.X resilience.
Monkey-Ops is prepared to running into a docker image. Monkey-Ops also includes an Openshift template in order to be deployed into a Openshift Project.
Form more information on how to use it in both way, https://github.com/startxfr/monkey-ops/blob/master/README.md
您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本,请访问 标签列表页面。
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