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Zhongwei Li
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skills/agent-sdk-master/SKILL.md
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511
skills/agent-sdk-master/SKILL.md
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---
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name: Google Cloud Agent SDK Master
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description: |
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Automatic activation for ALL Google Cloud Agent Development Kit (ADK) and Agent Starter Pack operations - multi-agent systems, containerized deployment, RAG agents, and production orchestration.
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**TRIGGER PHRASES:**
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- "adk", "agent development kit", "agent starter pack", "multi-agent", "build agent"
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- "cloud run agent", "gke deployment", "agent engine", "containerized agent"
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- "rag agent", "react agent", "agent orchestration", "agent templates"
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**AUTO-INVOKES FOR:**
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- Agent creation and scaffolding
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- Multi-agent system design
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- Containerized agent deployment
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- RAG (Retrieval-Augmented Generation) implementation
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- CI/CD pipeline setup for agents
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- Agent evaluation and monitoring
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allowed-tools: Read, WebFetch, WebSearch, Grep
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version: 1.0.0
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---
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# Google Cloud Agent SDK Master - Production-Ready Agent Systems
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This Agent Skill provides comprehensive mastery of Google's Agent Development Kit (ADK) and Agent Starter Pack for building and deploying production-grade containerized agents.
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## Core Capabilities
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### 🤖 Agent Development Kit (ADK)
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**Framework Overview:**
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- **Open-source Python framework** from Google
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- Same framework powering Google Agentspace and CES
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- Build production agents in <100 lines of code
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- Model-agnostic (optimized for Gemini)
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- Deployment-agnostic (local, Cloud Run, GKE, Agent Engine)
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**Supported Agent Types:**
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1. **LLM Agents**: Dynamic routing with intelligence
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2. **Workflow Agents**:
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- Sequential: Linear execution
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- Loop: Iterative processing
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- Parallel: Concurrent execution
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3. **Custom Agents**: User-defined implementations
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4. **Multi-agent Systems**: Hierarchical coordination
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**Key Features:**
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- Flexible orchestration (workflow & LLM-driven)
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- Tool ecosystem (search, code execution, custom functions)
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- Third-party integrations (LangChain, CrewAI)
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- Agents-as-tools capability
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- Built-in evaluation framework
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- Cloud Trace integration
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### 📦 Agent Starter Pack
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**Production Templates:**
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1. **adk_base** - ReAct agent using ADK
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2. **agentic_rag** - Document retrieval + Q&A with search
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3. **langgraph_base_react** - LangGraph ReAct implementation
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4. **crewai_coding_crew** - Multi-agent coding system
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5. **adk_live** - Multimodal RAG (audio/video/text)
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**Infrastructure Automation:**
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- CI/CD setup with single command
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- GitHub Actions or Cloud Build pipelines
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- Multi-environment support (dev, staging, prod)
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- Automated testing and evaluation
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- Deployment rollback mechanisms
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### 🚀 Deployment Targets
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**1. Vertex AI Agent Engine**
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- Fully managed runtime
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- Auto-scaling and load balancing
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- Built-in observability
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- Serverless architecture
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- Best for: Production-scale agents
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**2. Cloud Run**
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- Containerized serverless
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- Pay-per-use pricing
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- Custom domain support
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- Traffic splitting
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- Best for: Web-facing agents
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**3. Google Kubernetes Engine (GKE)**
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- Full container orchestration
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- Advanced networking
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- Resource management
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- Multi-cluster support
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- Best for: Complex multi-agent systems
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**4. Local/Docker**
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- Development and testing
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- Custom infrastructure
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- On-premises deployment
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- Best for: POC and debugging
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### 🔧 Technical Implementation
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**Installation:**
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```bash
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# Agent Starter Pack (recommended)
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pip install agent-starter-pack
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# or direct from GitHub
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uvx agent-starter-pack create my-agent
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# ADK only
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pip install google-cloud-aiplatform[adk,agent_engines]>=1.111
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```
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**Create Agent (ADK):**
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```python
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from google.cloud.aiplatform import agent
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from vertexai.preview.agents import ADKAgent
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# Simple ReAct agent
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@agent.adk_agent
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class MyAgent(ADKAgent):
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def __init__(self):
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super().__init__(
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model="gemini-2.5-pro",
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tools=[search_tool, code_exec_tool]
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)
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def run(self, query: str):
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return self.generate(query)
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# Multi-agent orchestration
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class OrchestratorAgent(ADKAgent):
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def __init__(self):
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self.research_agent = ResearchAgent()
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self.analysis_agent = AnalysisAgent()
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self.writer_agent = WriterAgent()
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def run(self, task: str):
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research = self.research_agent.run(task)
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analysis = self.analysis_agent.run(research)
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output = self.writer_agent.run(analysis)
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return output
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```
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**Using Agent Starter Pack:**
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```bash
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# Create project with template
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uvx agent-starter-pack create my-rag-agent \
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--template agentic_rag \
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--deployment cloud_run
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# Generates complete structure:
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my-rag-agent/
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├── src/
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│ ├── agent.py # Agent implementation
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│ ├── tools/ # Custom tools
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│ └── config.py # Configuration
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├── deployment/
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│ ├── Dockerfile
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│ ├── cloudbuild.yaml
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│ └── terraform/
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├── tests/
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│ ├── unit_tests.py
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│ └── integration_tests.py
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└── .github/workflows/ # CI/CD pipelines
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```
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**Deploy to Cloud Run:**
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```bash
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# Using ADK CLI
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adk deploy \
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--target cloud_run \
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--region us-central1 \
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--service-account sa@project.iam.gserviceaccount.com
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# Manual with Docker
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docker build -t gcr.io/PROJECT/agent:latest .
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docker push gcr.io/PROJECT/agent:latest
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gcloud run deploy agent \
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--image gcr.io/PROJECT/agent:latest \
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--region us-central1 \
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--allow-unauthenticated
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```
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**Deploy to Agent Engine:**
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```bash
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# Using Agent Starter Pack
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asp deploy \
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--env production \
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--target agent_engine
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# Manual deployment
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from google.cloud.aiplatform import agent_engines
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agent_engines.deploy_agent(
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agent_id="my-agent",
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project="PROJECT_ID",
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location="us-central1"
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)
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```
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### 📊 RAG Agent Implementation
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**Vector Search Integration:**
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```python
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from vertexai.preview.rag import VectorSearchTool
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from google.cloud import aiplatform
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# Set up vector search
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vector_search = VectorSearchTool(
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index_endpoint="projects/PROJECT/locations/LOCATION/indexEndpoints/INDEX_ID",
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deployed_index_id="deployed_index"
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)
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# RAG agent with ADK
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class RAGAgent(ADKAgent):
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def __init__(self):
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super().__init__(
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model="gemini-2.5-pro",
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tools=[vector_search, web_search_tool]
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)
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def run(self, query: str):
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# Retrieves relevant docs automatically
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response = self.generate(
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f"Answer this using retrieved context: {query}"
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)
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return response
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```
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**Vertex AI Search Integration:**
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```python
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from vertexai.preview.search import VertexAISearchTool
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# Enterprise search integration
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vertex_search = VertexAISearchTool(
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data_store_id="DATA_STORE_ID",
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project="PROJECT_ID"
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)
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agent = ADKAgent(
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model="gemini-2.5-pro",
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tools=[vertex_search]
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)
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```
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### 🔄 CI/CD Automation
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**GitHub Actions (auto-generated):**
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```yaml
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name: Deploy Agent
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on:
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push:
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branches: [main]
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jobs:
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deploy:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- name: Test Agent
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run: pytest tests/
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- name: Deploy to Cloud Run
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run: |
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gcloud run deploy agent \
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--source . \
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--region us-central1
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```
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**Cloud Build Pipeline:**
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```yaml
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steps:
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# Build container
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- name: 'gcr.io/cloud-builders/docker'
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args: ['build', '-t', 'gcr.io/$PROJECT_ID/agent', '.']
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# Run tests
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- name: 'gcr.io/$PROJECT_ID/agent'
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args: ['pytest', 'tests/']
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# Deploy to Cloud Run
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- name: 'gcr.io/cloud-builders/gcloud'
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args:
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- 'run'
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- 'deploy'
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- 'agent'
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- '--image=gcr.io/$PROJECT_ID/agent'
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- '--region=us-central1'
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```
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### 🎯 Multi-Agent Orchestration
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**Hierarchical Agents:**
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```python
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# Coordinator agent with specialized sub-agents
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class ProjectManagerAgent(ADKAgent):
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def __init__(self):
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self.researcher = ResearchAgent()
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self.analyst = AnalysisAgent()
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self.writer = WriterAgent()
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self.reviewer = ReviewAgent()
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def run(self, project_brief: str):
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# Coordinate multiple agents
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research = self.researcher.run(project_brief)
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analysis = self.analyst.run(research)
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draft = self.writer.run(analysis)
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final = self.reviewer.run(draft)
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return final
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```
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**Parallel Agent Execution:**
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```python
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import asyncio
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class ParallelResearchAgent(ADKAgent):
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async def research_topic(self, topics: list[str]):
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# Run multiple agents concurrently
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tasks = [
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self.specialized_agent(topic)
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for topic in topics
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]
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results = await asyncio.gather(*tasks)
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return self.synthesize(results)
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```
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### 📈 Evaluation & Monitoring
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**Built-in Evaluation:**
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```python
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from google.cloud.aiplatform import agent_evaluation
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# Define evaluation metrics
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eval_config = agent_evaluation.EvaluationConfig(
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metrics=["accuracy", "relevance", "safety"],
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test_dataset="gs://bucket/eval_data.jsonl"
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)
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# Run evaluation
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results = agent.evaluate(eval_config)
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print(f"Accuracy: {results.accuracy}")
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print(f"Relevance: {results.relevance}")
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```
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**Cloud Trace Integration:**
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```python
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from google.cloud import trace_v1
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# Automatic tracing
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@traced_agent
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class MonitoredAgent(ADKAgent):
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def run(self, query: str):
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# All calls automatically traced
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with self.trace_span("retrieval"):
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docs = self.retrieve(query)
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with self.trace_span("generation"):
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response = self.generate(query, docs)
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return response
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```
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### 🔒 Security & Best Practices
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**1. Service Account Management:**
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```bash
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# Create minimal-permission service account
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gcloud iam service-accounts create agent-sa \
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--display-name "Agent Service Account"
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# Grant only required permissions
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gcloud projects add-iam-policy-binding PROJECT_ID \
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--member="serviceAccount:agent-sa@PROJECT.iam.gserviceaccount.com" \
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--role="roles/aiplatform.user"
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```
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**2. Secret Management:**
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```python
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from google.cloud import secretmanager
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def get_api_key():
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client = secretmanager.SecretManagerServiceClient()
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name = "projects/PROJECT/secrets/api-key/versions/latest"
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response = client.access_secret_version(name=name)
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return response.payload.data.decode('UTF-8')
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```
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**3. VPC Service Controls:**
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```bash
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# Enable VPC SC for data security
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gcloud access-context-manager perimeters create agent-perimeter \
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--resources=projects/PROJECT_ID \
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--restricted-services=aiplatform.googleapis.com
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```
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### 💰 Cost Optimization
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**Strategies:**
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- Use Gemini 2.5 Flash for most operations
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- Cache embeddings for RAG systems
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- Implement request batching
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- Use preemptible GKE nodes
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- Monitor token usage in Cloud Monitoring
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**Pricing Examples:**
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- Cloud Run: $0.00024/GB-second
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- Agent Engine: Pay-per-request pricing
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- GKE: Standard cluster costs
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- Gemini API: $3.50/1M tokens (Pro)
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### 📚 Reference Architecture
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**Production Agent System:**
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```
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┌─────────────────┐
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│ Load Balancer │
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└────────┬────────┘
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│
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┌────▼────┐
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│Cloud Run│ (Agent containers)
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└────┬────┘
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│
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┌────▼──────────┐
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│ Agent Engine │ (Orchestration)
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└────┬──────────┘
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│
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┌────▼────────────────┐
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│ Vertex AI Search │ (RAG)
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│ Vector Search │
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│ Gemini 2.5 Pro │
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└─────────────────────┘
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```
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### 🎯 Best Practices for Jeremy
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**1. Start with Templates:**
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```bash
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# Use Agent Starter Pack templates
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uvx agent-starter-pack create my-agent --template agentic_rag
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```
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**2. Local Development:**
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```bash
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# Test locally first
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adk serve --port 8080
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curl http://localhost:8080/query -d '{"question": "test"}'
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```
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**3. Gradual Deployment:**
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```bash
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# Deploy to dev → staging → prod
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asp deploy --env dev
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# Test thoroughly
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asp deploy --env staging
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# Final production push
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asp deploy --env production
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```
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**4. Monitor Everything:**
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- Enable Cloud Trace
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- Set up error reporting
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- Track token usage
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- Monitor response times
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- Set up alerting
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### 📖 Official Documentation
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**Core Resources:**
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- ADK Docs: https://google.github.io/adk-docs/
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- Agent Starter Pack: https://github.com/GoogleCloudPlatform/agent-starter-pack
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- Agent Engine: https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview
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- Agent Builder: https://cloud.google.com/products/agent-builder
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**Tutorials:**
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- Building AI Agents: https://codelabs.developers.google.com/devsite/codelabs/building-ai-agents-vertexai
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- Multi-agent Systems: https://cloud.google.com/blog/products/ai-machine-learning/build-and-manage-multi-system-agents-with-vertex-ai
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## When This Skill Activates
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This skill automatically activates when you mention:
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- Agent development, ADK, or Agent Starter Pack
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- Multi-agent systems or orchestration
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- Containerized agent deployment
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- Cloud Run, GKE, or Agent Engine deployment
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- RAG agents or ReAct agents
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- Agent templates or scaffolding
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- CI/CD for agents
|
||||
- Production agent systems
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## Integration with Other Services
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**Google Cloud:**
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- Vertex AI (Gemini, Search, Vector Search)
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- Cloud Storage (data storage)
|
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- Cloud Functions (triggers)
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- Cloud Scheduler (automation)
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- Cloud Logging & Monitoring
|
||||
|
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**Third-party:**
|
||||
- LangChain integration
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||||
- CrewAI orchestration
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- Custom tool frameworks
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## Success Metrics
|
||||
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**Track:**
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- Agent response time (target: <2s)
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- Evaluation scores (target: >85% accuracy)
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- Deployment frequency (target: daily)
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- System uptime (target: 99.9%)
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- Cost per query (target: <$0.01)
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||||
---
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||||
|
||||
**This skill makes Jeremy a Google Cloud agent architecture expert with instant access to ADK, Agent Starter Pack, and production deployment patterns.**
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26
skills/agent-sdk-master/assets/README.md
Normal file
26
skills/agent-sdk-master/assets/README.md
Normal file
@@ -0,0 +1,26 @@
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||||
# Skill Assets
|
||||
|
||||
This directory contains static assets used by this skill.
|
||||
|
||||
## Purpose
|
||||
|
||||
Assets can include:
|
||||
- Configuration files (JSON, YAML)
|
||||
- Data files
|
||||
- Templates
|
||||
- Schemas
|
||||
- Test fixtures
|
||||
|
||||
## Guidelines
|
||||
|
||||
- Keep assets small and focused
|
||||
- Document asset purpose and format
|
||||
- Use standard file formats
|
||||
- Include schema validation where applicable
|
||||
|
||||
## Common Asset Types
|
||||
|
||||
- **config.json** - Configuration templates
|
||||
- **schema.json** - JSON schemas
|
||||
- **template.yaml** - YAML templates
|
||||
- **test-data.json** - Test fixtures
|
||||
26
skills/agent-sdk-master/references/README.md
Normal file
26
skills/agent-sdk-master/references/README.md
Normal file
@@ -0,0 +1,26 @@
|
||||
# Skill References
|
||||
|
||||
This directory contains reference materials that enhance this skill's capabilities.
|
||||
|
||||
## Purpose
|
||||
|
||||
References can include:
|
||||
- Code examples
|
||||
- Style guides
|
||||
- Best practices documentation
|
||||
- Template files
|
||||
- Configuration examples
|
||||
|
||||
## Guidelines
|
||||
|
||||
- Keep references concise and actionable
|
||||
- Use markdown for documentation
|
||||
- Include clear examples
|
||||
- Link to external resources when appropriate
|
||||
|
||||
## Types of References
|
||||
|
||||
- **examples.md** - Usage examples
|
||||
- **style-guide.md** - Coding standards
|
||||
- **templates/** - Reusable templates
|
||||
- **patterns.md** - Design patterns
|
||||
24
skills/agent-sdk-master/scripts/README.md
Normal file
24
skills/agent-sdk-master/scripts/README.md
Normal file
@@ -0,0 +1,24 @@
|
||||
# Skill Scripts
|
||||
|
||||
This directory contains optional helper scripts that support this skill's functionality.
|
||||
|
||||
## Purpose
|
||||
|
||||
Scripts here can be:
|
||||
- Referenced by the skill for automation
|
||||
- Used as examples for users
|
||||
- Executed during skill activation
|
||||
|
||||
## Guidelines
|
||||
|
||||
- All scripts should be well-documented
|
||||
- Include usage examples in comments
|
||||
- Make scripts executable (`chmod +x`)
|
||||
- Use `#!/bin/bash` or `#!/usr/bin/env python3` shebangs
|
||||
|
||||
## Adding Scripts
|
||||
|
||||
1. Create script file (e.g., `analyze.sh`, `process.py`)
|
||||
2. Add documentation header
|
||||
3. Make executable: `chmod +x script-name.sh`
|
||||
4. Test thoroughly before committing
|
||||
Reference in New Issue
Block a user