본문으로 건너뛰기
cccskills
무료GitHub에 공개

adk-python

Use Google ADK Python to build agents, Workflow graphs, tools, runtime services, CLI apps, evaluations, deployments, and ADK repository changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(41)

  • SKILL.md4.6 KB
  • references/capability-map.md4.1 KB
  • references/repo-provenance.md2.0 KB
  • references/repo-routing-metadata.json494 B
  • references/troubleshooting.md4.0 KB
  • scripts/check_adk_install.py4.8 KB
  • sub-skills/agent-construction/references/api-reference.md11.5 KB
  • sub-skills/agent-construction/references/troubleshooting.md9.2 KB
  • sub-skills/agent-construction/references/workflows.md11.7 KB
  • sub-skills/agent-construction/scripts/inspect_agent_api.py2.5 KB
  • sub-skills/agent-construction/SKILL.md4.1 KB
  • sub-skills/cli-configuration-deployment/references/cli-reference.md12.4 KB
  • sub-skills/cli-configuration-deployment/references/configuration.md5.3 KB
  • sub-skills/cli-configuration-deployment/references/troubleshooting.md5.9 KB
  • sub-skills/cli-configuration-deployment/scripts/inspect_adk_cli.py6.0 KB
  • sub-skills/cli-configuration-deployment/SKILL.md3.1 KB
  • sub-skills/evaluation-debugging/references/debugging.md9.7 KB
  • sub-skills/evaluation-debugging/references/evaluation-workflows.md12.4 KB
  • sub-skills/evaluation-debugging/references/troubleshooting.md11.3 KB
  • sub-skills/evaluation-debugging/scripts/summarize_adk_events.py10.8 KB
  • sub-skills/evaluation-debugging/SKILL.md4.1 KB
  • sub-skills/repo-development/references/development-and-testing.md11.5 KB
  • sub-skills/repo-development/references/review-checklist.md6.7 KB
  • sub-skills/repo-development/references/troubleshooting.md8.8 KB
  • sub-skills/repo-development/scripts/select_adk_tests.py16.3 KB
  • sub-skills/repo-development/SKILL.md2.9 KB
  • sub-skills/runtime-services/references/api-reference.md12.8 KB
  • sub-skills/runtime-services/references/service-patterns.md12.8 KB
  • sub-skills/runtime-services/references/troubleshooting.md13.7 KB
  • sub-skills/runtime-services/scripts/check_runtime_services.py6.7 KB
  • sub-skills/runtime-services/SKILL.md4.2 KB
  • sub-skills/tools-and-integrations/references/api-reference.md17.2 KB
  • sub-skills/tools-and-integrations/references/troubleshooting.md11.2 KB
  • sub-skills/tools-and-integrations/references/workflows.md16.7 KB
  • sub-skills/tools-and-integrations/scripts/inspect_tooling.py8.6 KB
  • sub-skills/tools-and-integrations/SKILL.md5.0 KB
  • sub-skills/workflow-orchestration/references/api-reference.md12.1 KB
  • sub-skills/workflow-orchestration/references/troubleshooting.md13.0 KB
  • sub-skills/workflow-orchestration/references/workflow-patterns.md13.5 KB
  • sub-skills/workflow-orchestration/scripts/inspect_workflow_api.py4.5 KB
  • sub-skills/workflow-orchestration/SKILL.md2.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

ADK Python

Use this repo skill when a task involves Google Agent Development Kit for Python (google-adk / google.adk), ADK 2.0 Agent and Workflow APIs, ADK CLI commands, tool integrations, runtime services, evaluation/debugging, or modifying the ADK Python repository itself.

Start Here

  • Install the package with pip install google-adk on Python 3.10+.
  • For optional integrations, install the narrow extra for the task instead of all by default, such as google-adk[db], google-adk[mcp], google-adk[eval], google-adk[gcp], or google-adk[extensions].
  • Verify the base install with python -c "import google.adk; print(google.adk.__version__)" and adk --help.
  • Run check_adk_install.py for a safe diagnostic covering imports, CLI command availability, package version, and optional-extra signals.
  • Read Repository Provenance before deciding whether this skill is current for a checkout; refresh if commit, dirty state, package version, or major evidence paths changed.

Route by Task

  • Build Python agents: Use agent-construction for Agent/LlmAgent, instructions, model settings, callbacks, schemas, task/single-turn modes, sub-agents, and multi-agent delegation.
  • Build Workflow graphs: Use workflow-orchestration for Workflow, BaseNode, function nodes, graph edges, JoinNode, dynamic nodes, parallel worker, HITL, retry, checkpoint/resume, and event flow.
  • Add tools or integrations: Use tools-and-integrations for FunctionTool, ToolContext, toolsets, confirmation/long-running tools, auth, MCP, OpenAPI, Google API/cloud integrations, A2A, and optional extras.
  • Configure runtime services: Use runtime-services for Runner, App, sessions, memory, artifacts, plugins, telemetry, code executors, environments, event persistence, and service lifecycles.
  • Use CLI/config/deployment: Use cli-configuration-deployment for adk run, adk web, adk api_server, adk test, adk eval, adk deploy, app discovery, YAML config, service URIs, and safe CLI/schema inspection.
  • Evaluate or debug behavior: Use evaluation-debugging for eval sets, JSON test fixtures, adk test, adk eval, event summaries, traces, session inspection, flaky evals, and assertion design.
  • Modify this repository: Use repo-development for ADK Python source edits, style, tests, docs, samples, generated schema, review gates, and repository-specific validation.

Shared References

  • Capability Map maps common user request families to sub-skills, bundled scripts, and validation expectations.
  • Troubleshooting covers cross-cutting install/import, optional dependency, CLI, credential, server, and repository-staleness failures.
  • Repository Routing Metadata is structured metadata used by DisCo's managed repo-skills-router during import.

Safe Defaults

  • Prefer import/signature checks, --help, and dry-run diagnostics before starting servers, deployments, cloud calls, model-backed evals, or database migrations.
  • Do not assume optional extras are installed in base google-adk; diagnose missing modules and install only what the selected workflow requires.
  • Do not put API keys, OAuth secrets, service-account JSON, production database URLs, or cloud project credentials into examples or YAML files.
  • For repository-maintenance tasks, run focused tests first and avoid remote/cloud/credentialed tests unless the user explicitly asks and the environment is configured.

Minimal Examples

Create a simple agent:

from google.adk import Agent

root_agent = Agent(
    name="greeter",
    model="gemini-2.5-flash",
    instruction="Greet the user and answer briefly.",
)

Create a simple workflow:

from google.adk import Workflow

root_agent = Workflow(
    name="fruit_flow",
    edges=[("START", pick_fruit, explain_benefit)],
)

Run local checks:

python scripts/check_adk_install.py --json
adk --help
adk run path/to/my_agent --help

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

3ddfa

무료

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

3ddfa-v2

무료

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

ab3dmot

무료

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

acme

무료

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

VectorSpaceLab의 스킬 모두 보기

이 스킬의 문제 신고