Agent Skills › phuc-nt/my-translator › ck:google-adk-python

ck:google-adk-python

GitHub

Google ADK Python 技能指南，涵盖多智能体系统构建、A2A协议、MCP工具集成、工作流编排、状态管理及 Vertex AI 部署评估。

.opencode/skills/google-adk-python/SKILL.md phuc-nt/my-translator

Trigger Scenarios

构建单/多智能体系统集成 MCP 服务器作为工具配置 A2A 远程通信使用工作流代理进行管道处理部署到 Cloud Run 或 Vertex AI

Install

npx skills add phuc-nt/my-translator --skill ck:google-adk-python -g -y

More Options

Non-standard path

npx skills add https://github.com/phuc-nt/my-translator/tree/main/.opencode/skills/google-adk-python -g -y

Use without installing

npx skills use phuc-nt/my-translator@ck:google-adk-python

指定 Agent (Claude Code)

npx skills add phuc-nt/my-translator --skill ck:google-adk-python -a claude-code -g -y

安装 repo 全部 skill

npx skills add phuc-nt/my-translator --all -g -y

预览 repo 内 skill

npx skills add phuc-nt/my-translator --list

SKILL.md

Frontmatter

{
    "name": "ck:google-adk-python",
    "license": "Apache-2.0",
    "metadata": {
        "author": "claudekit",
        "version": "2.0.0"
    },
    "description": "Build AI agents with Google ADK Python. Multi-agent systems, A2A protocol, MCP tools, workflow agents, state\/memory, callbacks\/plugins, Vertex AI deployment, evaluation.",
    "argument-hint": "[agent or feature]"
}

Google ADK Python Skill

Expert guide for Google's Agent Development Kit (ADK) Python — open-source, code-first toolkit for building, evaluating, and deploying AI agents. Optimized for Gemini, model-agnostic by design.

When to Activate

Build single or multi-agent systems with tool integration
Implement A2A protocol for remote agent communication
Integrate MCP servers as agent tools
Use workflow agents (sequential, parallel, loop) for pipelines
Manage sessions, state, memory, and artifacts
Add callbacks, plugins, or observability hooks
Deploy to Cloud Run, Vertex AI Agent Engine, or GKE
Evaluate agents with adk eval framework

Agent Structure Convention (Required)

my_agent/
├── __init__.py   # MUST: from . import agent
└── agent.py      # MUST: root_agent = Agent(...) OR app = App(...)

Quick Start

pip install google-adk          # stable (weekly releases)
uv sync --all-extras            # dev setup (uv required, Python 3.10+, 3.11+ recommended)

from google.adk import Agent

root_agent = Agent(
    name="assistant",
    model="gemini-2.5-flash",
    instruction="You are a helpful assistant.",
    description="General assistant agent.",
    tools=[get_weather],
)

App Pattern (Production)

from google.adk import Agent
from google.adk.apps import App
from google.adk.apps.app import EventsCompactionConfig
from google.adk.plugins.save_files_as_artifacts_plugin import SaveFilesAsArtifactsPlugin

app = App(
    name="my_app",
    root_agent=Agent(name="my_agent", model="gemini-2.5-flash", ...),
    plugins=[SaveFilesAsArtifactsPlugin()],
    events_compaction_config=EventsCompactionConfig(compaction_interval=2),
)

Use App when needing plugins, event compaction, or custom lifecycle management.

CLI Tools

Command	Purpose
`adk web <agents_dir>`	Dev UI (recommended for development)
`adk run <agent_dir>`	Interactive CLI testing
`adk api_server <agents_dir>`	FastAPI production server
`adk eval <agent> <evalset.json>`	Run evaluation suite

Agent Types

Type	Use Case
`Agent` / `LlmAgent`	Dynamic routing, tool use, reasoning
`SequentialAgent`	Fixed-order pipeline
`ParallelAgent`	Concurrent execution
`LoopAgent`	Iterative processing
`RemoteA2aAgent`	Remote agent via A2A protocol

Key APIs

Feature	API
State	`tool_context.state[key] = value`
Artifacts	`tool_context.save_artifact(name, part)`
Callbacks	`before_agent_callback`, `after_model_callback`, etc.
MCP Tools	`MCPToolset(connection_params=StdioConnectionParams(...))`
Sub-agents	`Agent(..., sub_agents=[agent1, agent2])`
Human-in-loop	`LongRunningFunctionTool(func=my_func)`
Plugins	`App(..., plugins=[MyPlugin()])`

Model Support

Latest: gemini-2.5-flash (default), gemini-2.5-pro, gemini-2.0-flash (sunsets Mar 2026) Preview: gemini-3-flash-preview, gemini-3-pro-preview Also: Anthropic Claude, Ollama, LiteLLM, vLLM, Model Garden

Best Practices

Code-first — define agents in Python for version control and testing
Agent convention — always use root_agent or app variable in agent.py
Modular agents — specialize per domain, compose via sub_agents
Workflow selection — workflow agents for predictable, LlmAgent for dynamic
State — ToolContext.state for ephemeral, MemoryService for long-term
Safety — callbacks for guardrails, tool confirmation for sensitive ops
Evaluate — test with adk eval + evalset JSON before deployment

References

Detailed guides (load as needed):

references/agent-types-and-architecture.md — Agent types, workflows, custom agents
references/tools-and-mcp-integration.md — Custom tools, MCP, tool filtering
references/multi-agent-and-a2a-protocol.md — Sub-agents, A2A, coordinator patterns
references/sessions-state-memory-artifacts.md — State, artifacts, sessions, memory
references/callbacks-plugins-observability.md — Lifecycle hooks, plugins, tracing
references/evaluation-testing-cli.md — adk eval, CLI, evalset format
references/deployment-cloud-run-vertex-gke.md — Cloud Run, Vertex AI, GKE