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+# Google ADK Python Skill
+
+You are an expert guide for Google's Agent Development Kit (ADK) Python - an open-source, code-first toolkit for building, evaluating, and deploying AI agents.
+
+## When to Use This Skill
+
+Use this skill when users need to:
+- Build AI agents with tool integration and orchestration capabilities
+- Create multi-agent systems with hierarchical coordination
+- Implement workflow agents (sequential, parallel, loop) for predictable pipelines
+- Integrate LLM-powered agents with Google Search, Code Execution, or custom tools
+- Deploy agents to Vertex AI Agent Engine, Cloud Run, or custom infrastructure
+- Evaluate and test agent performance systematically
+- Implement human-in-the-loop approval flows for tool execution
+
+## Core Concepts
+
+### Agent Types
+
+**LlmAgent**: LLM-powered agents capable of dynamic routing and adaptive behavior
+- Define with name, model, instruction, description, and tools
+- Supports sub-agents for delegation and coordination
+- Intelligent decision-making based on context
+
+**Workflow Agents**: Structured, predictable orchestration patterns
+- **SequentialAgent**: Execute agents in defined order
+- **ParallelAgent**: Run multiple agents concurrently
+- **LoopAgent**: Repeat execution with iteration logic
+
+**BaseAgent**: Foundation for custom agent implementations
+
+### Key Components
+
+**Tools Ecosystem**:
+- Pre-built tools (google_search, code_execution)
+- Custom Python functions as tools
+- OpenAPI specification integration
+- Tool confirmation flows for human approval
+
+**Multi-Agent Architecture**:
+- Hierarchical agent composition
+- Specialized agents for specific domains
+- Coordinator agents for delegation
+
+## Installation
+
+```bash
+# Stable release (recommended)
+pip install google-adk
+
+# Development version (latest features)
+pip install git+https://github.com/google/adk-python.git@main
+```
+
+## Implementation Patterns
+
+### Single Agent with Tools
+
+```python
+from google.adk.agents import LlmAgent
+from google.adk.tools import google_search
+
+agent = LlmAgent(
+    name="search_assistant",
+    model="gemini-2.5-flash",
+    instruction="You are a helpful assistant that searches the web for information.",
+    description="Search assistant for web queries",
+    tools=[google_search]
+)
+```
+
+### Multi-Agent System
+
+```python
+from google.adk.agents import LlmAgent
+
+# Specialized agents
+researcher = LlmAgent(
+    name="Researcher",
+    model="gemini-2.5-flash",
+    instruction="Research topics thoroughly using web search.",
+    tools=[google_search]
+)
+
+writer = LlmAgent(
+    name="Writer",
+    model="gemini-2.5-flash",
+    instruction="Write clear, engaging content based on research.",
+)
+
+# Coordinator agent
+coordinator = LlmAgent(
+    name="Coordinator",
+    model="gemini-2.5-flash",
+    instruction="Delegate tasks to researcher and writer agents.",
+    sub_agents=[researcher, writer]
+)
+```
+
+### Custom Tool Creation
+
+```python
+from google.adk.tools import Tool
+
+def calculate_sum(a: int, b: int) -> int:
+    """Calculate the sum of two numbers."""
+    return a + b
+
+# Convert function to tool
+sum_tool = Tool.from_function(calculate_sum)
+
+agent = LlmAgent(
+    name="calculator",
+    model="gemini-2.5-flash",
+    tools=[sum_tool]
+)
+```
+
+### Sequential Workflow
+
+```python
+from google.adk.agents import SequentialAgent
+
+workflow = SequentialAgent(
+    name="research_workflow",
+    agents=[researcher, summarizer, writer]
+)
+```
+
+### Parallel Workflow
+
+```python
+from google.adk.agents import ParallelAgent
+
+parallel_research = ParallelAgent(
+    name="parallel_research",
+    agents=[web_researcher, paper_researcher, expert_researcher]
+)
+```
+
+### Human-in-the-Loop
+
+```python
+from google.adk.tools import google_search
+
+# Tool with confirmation required
+agent = LlmAgent(
+    name="careful_searcher",
+    model="gemini-2.5-flash",
+    tools=[google_search],
+    tool_confirmation=True  # Requires approval before execution
+)
+```
+
+## Deployment Options
+
+### Cloud Run Deployment
+
+```bash
+# Containerize agent
+docker build -t my-agent .
+
+# Deploy to Cloud Run
+gcloud run deploy my-agent --image my-agent
+```
+
+### Vertex AI Agent Engine
+
+```python
+# Deploy to Vertex AI for scalable agent hosting
+# Integrates with Google Cloud's managed infrastructure
+```
+
+### Custom Infrastructure
+
+```python
+# Run agents locally or on custom servers
+# Full control over deployment environment
+```
+
+## Model Support
+
+**Optimized for Gemini**:
+- gemini-2.5-flash
+- gemini-2.5-pro
+- gemini-1.5-flash
+- gemini-1.5-pro
+
+**Model Agnostic**: While optimized for Gemini, ADK supports other LLM providers through standard APIs.
+
+## Best Practices
+
+1. **Code-First Philosophy**: Define agents in Python for version control, testing, and flexibility
+2. **Modular Design**: Create specialized agents for specific domains, compose into systems
+3. **Tool Integration**: Leverage pre-built tools, extend with custom functions
+4. **Evaluation**: Test agents systematically against test cases
+5. **Safety**: Implement confirmation flows for sensitive operations
+6. **Hierarchical Structure**: Use coordinator agents for complex multi-agent workflows
+7. **Workflow Selection**: Choose workflow agents for predictable pipelines, LLM agents for dynamic routing
+
+## Common Use Cases
+
+- **Research Assistants**: Web search + summarization + report generation
+- **Code Assistants**: Code execution + documentation + debugging
+- **Customer Support**: Query routing + knowledge base + escalation
+- **Content Creation**: Research + writing + editing pipelines
+- **Data Analysis**: Data fetching + processing + visualization
+- **Task Automation**: Multi-step workflows with conditional logic
+
+## Development UI
+
+ADK includes built-in interface for:
+- Testing agent behavior interactively
+- Debugging tool calls and responses
+- Evaluating agent performance
+- Iterating on agent design
+
+## Resources
+
+- GitHub: https://github.com/google/adk-python
+- Documentation: https://google.github.io/adk-docs/
+- llms.txt: https://raw.githubusercontent.com/google/adk-python/refs/heads/main/llms.txt
+
+## Implementation Workflow
+
+When implementing ADK-based agents:
+
+1. **Define Requirements**: Identify agent capabilities and tools needed
+2. **Choose Architecture**: Single agent, multi-agent, or workflow-based
+3. **Select Tools**: Pre-built, custom functions, or OpenAPI integrations
+4. **Implement Agents**: Create agent definitions with instructions and tools
+5. **Test Locally**: Use development UI for iteration
+6. **Add Evaluation**: Create test cases for systematic validation
+7. **Deploy**: Choose Cloud Run, Vertex AI, or custom infrastructure
+8. **Monitor**: Track agent performance and iterate
+
+Remember: ADK treats agent development like traditional software engineering - use version control, write tests, and follow engineering best practices.