메뉴
Build AI agents with Google's Agent Development Kit (ADK) Python - an open-source toolkit for building, evaluating, and deploying AI agents. Features LlmAgent, workflow agents (sequential, parallel, loop), tool integration, multi-agent systems, and deployment to Vertex AI or Cloud Run.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Export Claude Code sessions to searchable markdown with QMD integration. Use when user says "sync sessions", "export sessions", "search sessions", "session history", or wants to backup/search past conversations. Don't use for live note-taking, meeting transcripts, or non-Claude-Code content.
Convert product briefs, websites, documents into structured product specs. Generates modular markdown files for landing pages, guides, marketing.
Generate step-by-step migration guides between tools, frameworks, or workflows. Outputs: Phase-by-phase migration plan, compatibility mapping, rollback strategy. Use when switching tools, upgrading frameworks, or adopting new workflows. Triggers: migrate from X to Y, switch to, upgrade from, moving from, /migrate
Generate complete project setup recipes combining Vibery templates, MCPs, and configurations. Outputs: Install commands, CLAUDE.md, folder structure, workflow setup. Use when starting new projects or adding major features to existing ones. Triggers: start new project, setup project, project blueprint, /blueprint
Transform conversations and ideas into structured technical specifications. Outputs: User stories, acceptance criteria, technical requirements, edge cases. Use when user wants to document requirements before coding. Triggers: write spec, create user stories, document requirements, /spec
Chain Vibery commands, skills, and agents into repeatable automated workflows. Outputs: Executable workflow files, hook configurations, CI/CD integration. Use when user has repetitive multi-step processes to automate. Triggers: create workflow, automate this process, chain these commands, /workflow
| name | google-adk-python |
| description | Build AI agents with Google's Agent Development Kit (ADK) Python - an open-source toolkit for building, evaluating, and deploying AI agents. Features LlmAgent, workflow agents (sequential, parallel, loop), tool integration, multi-agent systems, and deployment to Vertex AI or Cloud Run. |
| license | MIT |
Open-source, code-first toolkit for building, evaluating, and deploying AI agents.
pip install google-adk
LLM-powered agents with dynamic routing and adaptive behavior.
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.",
tools=[google_search]
)
Execute agents in defined order.
from google.adk.agents import SequentialAgent
workflow = SequentialAgent(
name="research_workflow",
agents=[researcher, summarizer, writer]
)
Run multiple agents concurrently.
from google.adk.agents import ParallelAgent
parallel = ParallelAgent(
name="parallel_research",
agents=[web_researcher, paper_researcher]
)
# Specialized agents
researcher = LlmAgent(
name="Researcher",
model="gemini-2.5-flash",
tools=[google_search]
)
writer = LlmAgent(
name="Writer",
model="gemini-2.5-flash",
)
# Coordinator
coordinator = LlmAgent(
name="Coordinator",
model="gemini-2.5-flash",
instruction="Delegate tasks to researcher and writer.",
sub_agents=[researcher, writer]
)
from google.adk.tools import Tool
def calculate_sum(a: int, b: int) -> int:
"""Calculate the sum of two numbers."""
return a + b
sum_tool = Tool.from_function(calculate_sum)