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cloudrun-development云运行开发

Agent Skill

cloudrun-development 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

17,650

周安装

714

GitHub Stars

51

下载量

5,541
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:cloudrun-development(云运行开发)
来源仓库:https://github.com/tencentcloudbase/skills
仓库路径:skills/cloudrun-development
安装命令:
npx skills add https://github.com/tencentcloudbase/skills --skill cloudrun-development
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/tencentcloudbase/skills --skill cloudrun-development

简介

长连接、多语言支持、AI代理能力的后端服务开发。

  • 支持两种模式:函数模式(Node.js,内置HTTP/WebSocket/SSE,固定端口3000,本地运行)和容器模式(通过Docker的任何语言/运行时,无本地工具支持)
  • 需要侦听 PORT 的无状态服务
  • 环境变量和外部写入数据;资源约束强制 Mem = 2 × CPU
  • 包括读取工具(queryCloudRun)用于列出服务和模板,并编写工具(manageCloudRun) 用于初始化、部署、运行和代理创建工作流程
  • 支持使用 @cloudbase/aiagent-framework 通过 Function 模式开发 AI 代理
  • 、SSE流式响应、内部小程序直连

SKILL.md

Standalone Install Note

If this environment only installed the current skill, start from the CloudBase main entry and use the published cloudbase/references/... paths for sibling skills.

  • CloudBase main entry: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/SKILL.md
  • Current skill raw source: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/references/cloudrun-development/SKILL.md

Keep local references/... paths for files that ship with the current skill directory. When this file points to a sibling skill such as auth-tool or web-development, use the standalone fallback URL shown next to that reference.

CloudBase Run Development

Activation Contract

Use this first when

  • The task is to initialize, run, deploy, inspect, or debug a CloudBase Run service.
  • The request needs a long-lived HTTP service, SSE, WebSocket, custom system dependencies, or container-style deployment.
  • The task is to create or run an Agent service on CloudBase Run.

Read before writing code if

  • You still need to choose between Function mode and Container mode.
  • The prompt mentions queryCloudRun, manageCloudRun, Dockerfile, service domains, or public/private access.

Then also read

  • Cloud functions instead of CloudRun -> ../cloud-functions/SKILL.md (standalone fallback: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/references/cloud-functions/SKILL.md)
  • Agent SDK and AG-UI specifics -> ../cloudbase-agent/SKILL.md (standalone fallback: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/references/cloudbase-agent/SKILL.md)
  • Web authentication for browser callers -> ../auth-web/SKILL.md (standalone fallback: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/references/auth-web/SKILL.md)

Do NOT use for

  • Simple Event Function or HTTP Function workflows that fit the function model better.
  • Frontend-only projects with no backend service.
  • Database-schema design tasks.

Common mistakes / gotchas

  • Choosing CloudRun when the request only needs a normal cloud function.
  • Forgetting to listen on the platform-provided PORT.
  • Treating CloudRun as stateful app hosting and storing important state on local disk.
  • Assuming local run is available for Container mode.
  • Opening public access by default when the scenario only needs private or mini-program internal access.

Minimal checklist

  • Choose Function mode or Container mode explicitly.
  • Confirm whether the service should be public, VPC-only, or mini-program internal.
  • Keep the service stateless and externalize durable data.
  • Use absolute paths for every local project path.

Overview

Use CloudBase Run when the task needs a deployed backend service rather than a short-lived serverless function.

When CloudRun is a better fit

  • Long connections: WebSocket, SSE, server push
  • Long-running request handling or persistent service processes
  • Custom runtime environments or system libraries
  • Arbitrary languages or frameworks
  • Stable external service endpoints with elastic scaling
  • AI Agent deployment on Function mode CloudRun

Mode selection

DimensionFunction modeContainer mode
Best forFast start, Node.js service patterns, built-in framework, Agent flowsExisting containers, arbitrary runtimes, custom system dependencies
Port modelFramework-managed local mode, deployed service still follows platform rulesApp must listen on injected PORT
DockerfileNot requiredRequired
Local run through toolsSupportedNot supported
Typical useStreaming APIs, low-latency backend, Agent serviceCustom language stack, migrated container app

How to use this skill (for a coding agent)

  1. Choose mode first

- Function mode -> quickest path for HTTP/SSE/WebSocket or Agent scenarios - Container mode -> use when Docker/custom runtime is a real requirement

  1. Follow mandatory runtime rules

- Listen on PORT - Keep the service stateless - Put durable data in DB/storage/cache - Keep dependencies and image size small - Respect resource ratio guidance: Mem = 2 × CPU

  1. Use the correct tools

- Read operations -> queryCloudRun - Write operations -> manageCloudRun - Delete requires explicit confirmation and force: true - Always use absolute targetPath

  1. Follow the deployment sequence

- Initialize or download code - For Container mode, verify Dockerfile - Local run when available - Configure access model - Deploy and verify detail output

Tool routing

Read operations

  • queryCloudRun(action="list") -> list services
  • queryCloudRun(action="detail") -> inspect one service and its latest deploy status when available
  • queryCloudRun(action="templates") -> see available starters
  • queryCloudRun(action="getDeployLog") -> retrieve the latest deploy log or a specified buildId

Write operations

  • manageCloudRun(action="init") -> create local project
  • manageCloudRun(action="download") -> pull remote code
  • manageCloudRun(action="run") -> local run for Function mode
  • manageCloudRun(action="deploy") -> deploy local project
  • manageCloudRun(action="delete") -> delete service
  • manageCloudRun(action="createAgent") -> create Agent service

Access guidance

  • Web/public scenarios -> enable WEB access intentionally and pair it with the right auth flow.
  • Mini Program -> prefer internal direct connection and avoid unnecessary public exposure.
  • Private/VPC scenarios -> keep public access off unless the product requirement clearly needs it.

Quick examples

Initialize

{ "action": "init", "serverName": "my-svc", "targetPath": "/abs/ws/my-svc" }

Local run (Function mode)

{ "action": "run", "serverName": "my-svc", "targetPath": "/abs/ws/my-svc", "runOptions": { "port": 3000 } }

Deploy

{
  "action": "deploy",
  "serverName": "my-svc",
  "targetPath": "/abs/ws/my-svc",
  "serverConfig": {
    "OpenAccessTypes": ["WEB"],
    "Cpu": 0.5,
    "Mem": 1,
    "MinNum": 1,
    "MaxNum": 5
  }
}

MinNum: 1 is the recommended default when you want to reduce cold-start latency. If the user explicitly prefers lower cost and accepts more cold starts, explain the tradeoff and let them reduce MinNum to 0.

Best practices

  1. Prefer PRIVATE/VPC or mini-program internal access when possible.
  2. Use environment variables for secrets and per-environment configuration.
  3. Verify configuration before and after deployment with queryCloudRun(action="detail").
  4. Keep startup work small to reduce cold-start impact.
  5. For Agent scenarios, use the Agent SDK skill for protocol and adapter details instead of duplicating them here.

Troubleshooting hints

  • Access failure -> check access type, domain setup, and whether the instance scaled to zero.
  • Deployment failure -> inspect Dockerfile, build logs, and CPU/memory ratio.
  • Local run failure -> remember only Function mode is supported by local-run tools.
  • Performance issues -> reduce dependencies, optimize initialization, and tune minimum instances.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

30.61%
按下载量换算1,696

Cursor

24.59%
按下载量换算1,363

Codex

15.64%
按下载量换算867

trae

13.32%
按下载量换算738

codebuddy

7.35%
按下载量换算407

trae-cn

3.33%
按下载量换算185

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

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