cloudrun-development

When to use this skill

Use this skill for CloudBase Run backend service development when you need:

• Long connection capabilities: WebSocket / SSE / server push
• Long-running or persistent processes: tasks that are not suitable for cloud functions, background jobs
• Custom runtime environments/system dependencies: custom images, specific system libraries
• Multi-language/arbitrary frameworks: Java, Go, PHP, .NET, Python, Node.js, etc.
• Stable external services with elastic scaling: pay-as-you-go, can scale down to 0
• Private/internal network access: VPC/PRIVATE access, mini-program callContainer internal direct connection
• AI agent development: develop personalized AI applications based on Function mode CloudRun

Do NOT use for:

• Simple cloud functions (use cloud function development instead)
• Frontend-only applications
• Database schema design (use data-model-creation skill)

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How to use this skill (for a coding agent)

1. Choose the right mode

   • Function mode: Fastest to get started, built-in HTTP/WebSocket/SSE, fixed port 3000, local running supported
   • Container mode: Any language and runtime, requires Dockerfile, local running not supported by tools

2. Follow mandatory requirements

   • Must listen on PORT environment variable (real port in container)
   • Stateless service: write data externally (DB/storage/cache)
   • No background persistent threads/processes outside requests
   • Minimize dependencies, slim images; reduce cold start and deployment time
   • Resource constraints: Mem = 2 × CPU (e.g., 0.25 vCPU → 0.5 GB)
   • Access control: Only enable public network for Web scenarios; mini-programs prioritize internal direct connection, recommend closing public network

3. Use tools correctly

   • Read operations: queryCloudRun (list, detail, templates)
   • Write operations: manageCloudRun (init, download, run, deploy, delete, createAgent)
   • Always use absolute paths for targetPath
   • Use force: true for delete operations

4. Follow the workflow

   • Initialize project → Check/generate Dockerfile (for container mode) → Local run (function mode only) → Configure access → Deploy → Verify

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CloudBase Run AI Development Rules

A concise guide for AI assistants and engineering collaboration, providing "when to use, how to use" rules and tool workflows.

1. When to use CloudBase Run (Use Cases)

• Need long connection capabilities: WebSocket / SSE / server push
• Need long-running or persistent processes: tasks that are not suitable for cloud functions, background jobs
• Need custom runtime environments/system dependencies: custom images, specific system libraries
• Use multi-language/arbitrary frameworks: Java, Go, PHP, .NET, Python, Node.js, etc.
• Need stable external services with elastic scaling: pay-as-you-go, can scale down to 0
• Need private/internal network access: VPC/PRIVATE access, mini-program callContainer internal direct connection
• Need to develop AI agents: develop personalized AI applications based on Function mode CloudRun

2. Mode Selection (Quick Comparison)

• Function mode: Fastest to get started, built-in HTTP/WebSocket/SSE, fixed port 3000; local running supported by tools
• Container mode: Any language and runtime, requires Dockerfile; local running not supported by tools

Mode Comparison Checklist

Dimension	Function Mode	Container Mode
Language/Framework	Node.js (via @cloudbase/functions-framework)	Any language/runtime (Java/Go/PHP/.NET/Python/Node.js, etc.)
Runtime	Function framework loads functions (Runtime)	Docker image starts process
Port	Fixed 3000	Application listens on PORT (injected by platform during deployment)
Dockerfile	Not required	Required (and must pass local build)
Local Running	Supported (built-in tools)	Not supported (recommend using Docker for debugging)
Typical Scenarios	WebSocket/SSE/streaming responses, forms/files, low latency, multiple functions per instance, shared memory	Arbitrary system dependencies/languages, migrating existing containerized applications

3. Development Requirements (Must Meet)

• Must listen on PORT environment variable (real port in container)
• Stateless service: write data externally (DB/storage/cache)
• No background persistent threads/processes outside requests
• Minimize dependencies, slim images; reduce cold start and deployment time
• Resource constraints: Mem = 2 × CPU (e.g., 0.25 vCPU → 0.5 GB)
• Access control: Only enable public network for Web scenarios; mini-programs prioritize internal direct connection, recommend closing public network

4. Tools (Plain Language & Read/Write Separation)

• Read operations (queryCloudRun):
  • list: What services do I have? Can filter by name/type
  • detail: Current configuration, version, access address of a service
  • templates: Ready-to-use starter templates
• Write operations (manageCloudRun):
  • init: Create local project (optional template)
  • download: Pull existing service code to local
  • run: Run locally (Function mode only, supports normal function and Agent mode)
  • deploy: Deploy local code to CloudRun
  • delete: Delete service (requires explicit confirmation)
  • createAgent: Create AI agent (based on Function mode CloudRun)
• Important parameters (remember these):
  • targetPath: Local directory (must be absolute path)
  • serverConfig: Deployment parameters (CPU/Mem/instance count/access type/environment variables, etc.)
  • runOptions: Local running port and temporary environment variables (Function mode), supports runMode: 'normal' | 'agent'
  • agentConfig: Agent configuration (agentName, botTag, description, template)
  • Delete must include force: true, otherwise it won't execute

5. Core Workflow (Understand Steps First, Then Examples)

1. Choose mode

   • Need multi-language/existing container/Docker: choose "Container mode"
   • Need long connection/streaming/low latency/multiple functions coexisting: prioritize "Function mode"

2. Initialize local project

   • General: Use template init (both Function mode and Container mode can start from templates)
   • Container mode must "check or generate Dockerfile":
     • Node.js minimal example:

       FROM node:18-alpine
       WORKDIR /app
       COPY package*.json ./
       RUN npm ci --omit=dev
       COPY . .
       ENV NODE_ENV=production
       EXPOSE 3000
       CMD ["node","server.js"]
       

     • Python minimal example:

       FROM python:3.11-slim
       WORKDIR /app
       COPY requirements.txt ./
       RUN pip install -r requirements.txt --no-cache-dir
       COPY . .
       ENV PORT=3000
       EXPOSE 3000
       CMD ["python","app.py"]
       

3. Local running (Function mode only)

   • Automatically use npm run dev/start or entry file via run

4. Configure access

   • Set OpenAccessTypes (WEB/VPC/PRIVATE) as needed; configure security domain and authentication for Web scenarios

5. Deploy

   • Specify CPU/Mem/instance count/environment variables, etc. during deploy

6. Verify

   • Use detail to confirm access address and configuration meet expectations

Example Tool Calls

1. View templates/services

{ "name": "queryCloudRun", "arguments": { "action": "templates" } }

{ "name": "queryCloudRun", "arguments": { "action": "detail", "detailServerName": "my-svc" } }

2. Initialize project

{ "name": "manageCloudRun", "arguments": { "action": "init", "serverName": "my-svc", "targetPath": "/abs/ws/my-svc", "template": "helloworld" } }

3. Download code (optional)

{ "name": "manageCloudRun", "arguments": { "action": "download", "serverName": "my-svc", "targetPath": "/abs/ws/my-svc" } }

4. Local running (Function mode only)

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

5. Deploy

{ "name": "manageCloudRun", "arguments": { "action": "deploy", "serverName": "my-svc", "targetPath": "/abs/ws/my-svc", "serverConfig": { "OpenAccessTypes": ["WEB"], "Cpu": 0.5, "Mem": 1, "MinNum": 0, "MaxNum": 5 } } }

6. Create AI agent (optional)

{ "name": "manageCloudRun", "arguments": { "action": "createAgent", "serverName": "my-agent", "targetPath": "/abs/ws/agents", "agentConfig": { "agentName": "MyAgent", "botTag": "demo", "description": "My agent", "template": "blank" } } }

7. Run agent (optional)

{ "name": "manageCloudRun", "arguments": { "action": "run", "serverName": "my-agent", "targetPath": "/abs/ws/agents/my-agent", "runOptions": { "port": 3000, "runMode": "agent" } } }

6. Best Practices (Strongly Recommended)

• Prioritize PRIVATE/VPC or mini-program internal callContainer, reduce public network exposure
• Web must use CloudBase Web SDK authentication; mini-programs authenticated by platform
• Secrets via environment variables; separate configuration for multiple environments (dev/stg/prod)
• Use queryCloudRun.detail to verify configuration and accessibility before and after deployment
• Image layers reusable, small volume; monitor startup latency and memory usage
• Agent development: Use @cloudbase/aiagent-framework, supports SSE streaming responses, BotId format is ibot-{name}-{tag}

7. Quick Troubleshooting

• Access failure: Check OpenAccessTypes/domain/port, whether instance scaled down to 0
• Deployment failure: Verify Dockerfile/build logs/image volume and CPU/Mem ratio
• Local running failure: Only Function mode supported; requires package.json dev/start or entry index.js|app.js|server.js
• Performance jitter: Reduce dependencies and initialization; appropriately increase MinNum; optimize cold start
• **Agent

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