[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6GT84X85VPXAVZC92SW05N9":3,"skill-same:01M6GT84X85VPXAVZC92SW05N9":102},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":57,"install":58,"resources":71},"01M6GT84X85VPXAVZC92SW05N9","ck:google-adk-python",null,"Build AI agents with Google ADK Python. Multi-agent systems, A2A protocol, MCP tools, workflow agents, state\u002Fmemory, callbacks\u002Fplugins, Vertex AI deployment, evaluation.","yosnap\u002Fdevdock","yosnap","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F66257788?v=4","01M6GSRTCX7DZKZ29PPYZNEFQG","Paulo Guachizaca","01M6GSRTH6M80S52Z0FSXKVYK4",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fyosnap\u002Fdevdock\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fgoogle-adk-python\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"google","python","google adk","adk","adk python","ck:google adk python","google adk python","with","MCP","multi","feature","build","Evaluation","state","deployment","workflow","memory","vertex ai","a2a","ai agents","2026-11-10 10:12:30",false,"---\nname: ck:google-adk-python\ndescription: \"Build AI agents with Google ADK Python. Multi-agent systems, A2A protocol, MCP tools, workflow agents, state\u002Fmemory, callbacks\u002Fplugins, Vertex AI deployment, evaluation.\"\nlicense: Apache-2.0\nversion: 2.0.0\nargument-hint: \"[agent or feature]\"\n---\n\n# Google ADK Python Skill\n\nExpert 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.\n\n## When to Activate\n\n- Build single or multi-agent systems with tool integration\n- Implement A2A protocol for remote agent communication\n- Integrate MCP servers as agent tools\n- Use workflow agents (sequential, parallel, loop) for pipelines\n- Manage sessions, state, memory, and artifacts\n- Add callbacks, plugins, or observability hooks\n- Deploy to Cloud Run, Vertex AI Agent Engine, or GKE\n- Evaluate agents with `adk eval` framework\n\n## Agent Structure Convention (Required)\n\n```\nmy_agent\u002F\n├── __init__.py   # MUST: from . import agent\n└── agent.py      # MUST: root_agent = Agent(...) OR app = App(...)\n```\n\n## Quick Start\n\n```bash\npip install google-adk          # stable (weekly releases)\nuv sync --all-extras            # dev setup (uv required, Python 3.10+, 3.11+ recommended)\n```\n\n```python\nfrom google.adk import Agent\n\nroot_agent = Agent(\n    name=\"assistant\",\n    model=\"gemini-2.5-flash\",\n    instruction=\"You are a helpful assistant.\",\n    description=\"General assistant agent.\",\n    tools=[get_weather],\n)\n```\n\n## App Pattern (Production)\n\n```python\nfrom google.adk import Agent\nfrom google.adk.apps import App\nfrom google.adk.apps.app import EventsCompactionConfig\nfrom google.adk.plugins.save_files_as_artifacts_plugin import SaveFilesAsArtifactsPlugin\n\napp = App(\n    name=\"my_app\",\n    root_agent=Agent(name=\"my_agent\", model=\"gemini-2.5-flash\", ...),\n    plugins=[SaveFilesAsArtifactsPlugin()],\n    events_compaction_config=EventsCompactionConfig(compaction_interval=2),\n)\n```\n\nUse `App` when needing plugins, event compaction, or custom lifecycle management.\n\n## CLI Tools\n\n| Command | Purpose |\n|---------|---------|\n| `adk web \u003Cagents_dir>` | Dev UI (recommended for development) |\n| `adk run \u003Cagent_dir>` | Interactive CLI testing |\n| `adk api_server \u003Cagents_dir>` | FastAPI production server |\n| `adk eval \u003Cagent> \u003Cevalset.json>` | Run evaluation suite |\n\n## Agent Types\n\n| Type | Use Case |\n|------|----------|\n| `Agent` \u002F `LlmAgent` | Dynamic routing, tool use, reasoning |\n| `SequentialAgent` | Fixed-order pipeline |\n| `ParallelAgent` | Concurrent execution |\n| `LoopAgent` | Iterative processing |\n| `RemoteA2aAgent` | Remote agent via A2A protocol |\n\n## Key APIs\n\n| Feature | API |\n|---------|-----|\n| State | `tool_context.state[key] = value` |\n| Artifacts | `tool_context.save_artifact(name, part)` |\n| Callbacks | `before_agent_callback`, `after_model_callback`, etc. |\n| MCP Tools | `MCPToolset(connection_params=StdioConnectionParams(...))` |\n| Sub-agents | `Agent(..., sub_agents=[agent1, agent2])` |\n| Human-in-loop | `LongRunningFunctionTool(func=my_func)` |\n| Plugins | `App(..., plugins=[MyPlugin()])` |\n\n## Model Support\n\nLatest: `gemini-2.5-flash` (default), `gemini-2.5-pro`, `gemini-2.0-flash` (sunsets Mar 2026)\nPreview: `gemini-3-flash-preview`, `gemini-3-pro-preview`\nAlso: Anthropic Claude, Ollama, LiteLLM, vLLM, Model Garden\n\n## Best Practices\n\n1. **Code-first** — define agents in Python for version control and testing\n2. **Agent convention** — always use `root_agent` or `app` variable in `agent.py`\n3. **Modular agents** — specialize per domain, compose via `sub_agents`\n4. **Workflow selection** — workflow agents for predictable, LlmAgent for dynamic\n5. **State** — `ToolContext.state` for ephemeral, `MemoryService` for long-term\n6. **Safety** — callbacks for guardrails, tool confirmation for sensitive ops\n7. **Evaluate** — test with `adk eval` + evalset JSON before deployment\n\n## References\n\nDetailed guides (load as needed):\n\n- `references\u002Fagent-types-and-architecture.md` — Agent types, workflows, custom agents\n- `references\u002Ftools-and-mcp-integration.md` — Custom tools, MCP, tool filtering\n- `references\u002Fmulti-agent-and-a2a-protocol.md` — Sub-agents, A2A, coordinator patterns\n- `references\u002Fsessions-state-memory-artifacts.md` — State, artifacts, sessions, memory\n- `references\u002Fcallbacks-plugins-observability.md` — Lifecycle hooks, plugins, tracing\n- `references\u002Fevaluation-testing-cli.md` — adk eval, CLI, evalset format\n- `references\u002Fdeployment-cloud-run-vertex-gke.md` — Cloud Run, Vertex AI, GKE\n\n## External Resources\n\n- GitHub: https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-python\n- Docs: https:\u002F\u002Fgoogle.github.io\u002Fadk-docs\u002F\n- Samples: https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-python\u002Ftree\u002Fmain\u002Fcontributing\u002Fsamples\n- llms.txt: https:\u002F\u002Fraw.githubusercontent.com\u002Fgoogle\u002Fadk-python\u002Frefs\u002Fheads\u002Fmain\u002Fllms.txt\n","# Google ADK Python Skill\n\nExpert 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.\n\n## When to Activate\n\n- Build single or multi-agent systems with tool integration\n- Implement A2A protocol for remote agent communication\n- Integrate MCP servers as agent tools\n- Use workflow agents (sequential, parallel, loop) for pipelines\n- Manage sessions, state, memory, and artifacts\n- Add callbacks, plugins, or observability hooks\n- Deploy to Cloud Run, Vertex AI Agent Engine, or GKE\n- Evaluate agents with `adk eval` framework\n\n## Agent Structure Convention (Required)\n\n```\nmy_agent\u002F\n├── __init__.py   # MUST: from . import agent\n└── agent.py      # MUST: root_agent = Agent(...) OR app = App(...)\n```\n\n## Quick Start\n\n```bash\npip install google-adk          # stable (weekly releases)\nuv sync --all-extras            # dev setup (uv required, Python 3.10+, 3.11+ recommended)\n```\n\n```python\nfrom google.adk import Agent\n\nroot_agent = Agent(\n    name=\"assistant\",\n    model=\"gemini-2.5-flash\",\n    instruction=\"You are a helpful assistant.\",\n    description=\"General assistant agent.\",\n    tools=[get_weather],\n)\n```\n\n## App Pattern (Production)\n\n```python\nfrom google.adk import Agent\nfrom google.adk.apps import App\nfrom google.adk.apps.app import EventsCompactionConfig\nfrom google.adk.plugins.save_files_as_artifacts_plugin import SaveFilesAsArtifactsPlugin\n\napp = App(\n    name=\"my_app\",\n    root_agent=Agent(name=\"my_agent\", model=\"gemini-2.5-flash\", ...),\n    plugins=[SaveFilesAsArtifactsPlugin()],\n    events_compaction_config=EventsCompactionConfig(compaction_interval=2),\n)\n```\n\nUse `App` when needing plugins, event compaction, or custom lifecycle management.\n\n## CLI Tools\n\n| Command | Purpose |\n|---------|---------|\n| `adk web \u003Cagents_dir>` | Dev UI (recommended for development) |\n| `adk run \u003Cagent_dir>` | Interactive CLI testing |\n| `adk api_server \u003Cagents_dir>` | FastAPI production server |\n| `adk eval \u003Cagent> \u003Cevalset.json>` | Run evaluation suite |\n\n## Agent Types\n\n| Type | Use Case |\n|------|----------|\n| `Agent` \u002F `LlmAgent` | Dynamic routing, tool use, reasoning |\n| `SequentialAgent` | Fixed-order pipeline |\n| `ParallelAgent` | Concurrent execution |\n| `LoopAgent` | Iterative processing |\n| `RemoteA2aAgent` | Remote agent via A2A protocol |\n\n## Key APIs\n\n| Feature | API |\n|---------|-----|\n| State | `tool_context.state[key] = value` |\n| Artifacts | `tool_context.save_artifact(name, part)` |\n| Callbacks | `before_agent_callback`, `after_model_callback`, etc. |\n| MCP Tools | `MCPToolset(connection_params=StdioConnectionParams(...))` |\n| Sub-agents | `Agent(..., sub_agents=[agent1, agent2])` |\n| Human-in-loop | `LongRunningFunctionTool(func=my_func)` |\n| Plugins | `App(..., plugins=[MyPlugin()])` |\n\n## Model Support\n\nLatest: `gemini-2.5-flash` (default), `gemini-2.5-pro`, `gemini-2.0-flash` (sunsets Mar 2026)\nPreview: `gemini-3-flash-preview`, `gemini-3-pro-preview`\nAlso: Anthropic Claude, Ollama, LiteLLM, vLLM, Model Garden\n\n## Best Practices\n\n1. **Code-first** — define agents in Python for version control and testing\n2. **Agent convention** — always use `root_agent` or `app` variable in `agent.py`\n3. **Modular agents** — specialize per domain, compose via `sub_agents`\n4. **Workflow selection** — workflow agents for predictable, LlmAgent for dynamic\n5. **State** — `ToolContext.state` for ephemeral, `MemoryService` for long-term\n6. **Safety** — callbacks for guardrails, tool confirmation for sensitive ops\n7. **Evaluate** — test with `adk eval` + evalset JSON before deployment\n\n## References\n\nDetailed guides (load as needed):\n\n- `references\u002Fagent-types-and-architecture.md` — Agent types, workflows, custom agents\n- `references\u002Ftools-and-mcp-integration.md` — Custom tools, MCP, tool filtering\n- `references\u002Fmulti-agent-and-a2a-protocol.md` — Sub-agents, A2A, coordinator patterns\n- `references\u002Fsessions-state-memory-artifacts.md` — State, artifacts, sessions, memory\n- `references\u002Fcallbacks-plugins-observability.md` — Lifecycle hooks, plugins, tracing\n- `references\u002Fevaluation-testing-cli.md` — adk eval, CLI, evalset format\n- `references\u002Fdeployment-cloud-run-vertex-gke.md` — Cloud Run, Vertex AI, GKE\n\n## External Resources\n\n- GitHub: https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-python\n- Docs: https:\u002F\u002Fgoogle.github.io\u002Fadk-docs\u002F\n- Samples: https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-python\u002Ftree\u002Fmain\u002Fcontributing\u002Fsamples\n- llms.txt: 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Use for React apps, server rendering, build optimization, caching strategies, shared dependencies.","MIT","https:\u002F\u002Fgithub.com\u002Fyosnap\u002Fdevdock\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fweb-frameworks\u002FSKILL.md",[],[114,141,142,143,144,145,146,147,148,149,125,150,151,152,153,154,34,155,156,157],"web frameworks","frameworks","ck:web frameworks","react","ssr","framework","server","turborepo","Optimization","router","next","caching","strategies","shared","app","apps","rendering",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":137,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":172,"publishedAt":44},"01M6GTCPB9F326DAZ9RRXEB9J9","ck:ui-styling","Style UIs with shadcn\u002Fui components (Radix UI + Tailwind CSS). Use for accessible components, themes, dark mode, responsive layouts, design systems, color customization.","https:\u002F\u002Fgithub.com\u002Fyosnap\u002Fdevdock\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fui-styling\u002FSKILL.md",[164,167],{"categoryNo":165,"categoryName":166,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":168,"categoryName":169,"taxonomyType":170,"taxonomyTypeDesc":171},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[173,174,175,176,177,178,179,180,181,182,183,184,125,185,186,187,188,189,190,191],"ui styling","ui","styling","ck:ui styling","design","css","components","component","design systems","shadcn","Tailwind","radix ui","responsive","layout","dark mode","ui components","themes","color","mode",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":137,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":201,"publishedAt":44},"01M6GTCF8WB87S84M4EQZVCRDG","ck:threejs","Build 3D web apps with Three.js (WebGL\u002FWebGPU). 556 searchable examples, 60 API classes, 20 use cases. Actions: create 3D scene, load model, add animation, implement physics, build VR\u002FXR. Topics: GLTF loader, PBR materials, particle effects, shadows, post-processing, compute shaders, TSL. Integrations: WebGPU, physics engines, spatial audio.","https:\u002F\u002Fgithub.com\u002Fyosnap\u002Fdevdock\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fthreejs\u002FSKILL.md",[198],{"categoryNo":199,"categoryName":200,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[202,194,203,204,205,206,207,208,209,210,211,212,213,214,34,156,215,216,217,218],"threejs","webgpu","create","shaders","actions","api","Animation","3D","Audio","model","integrations","three","physics","add","processing","post-processing","vr",{"skillNo":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":137,"licenseFileUrl":6,"sourceHtmlUrl":223,"categories":224,"keywords":225,"publishedAt":44},"01M6GTBBA0E5D1CVZZTFG4HPV3","ck:sequential-thinking","Apply step-by-step analysis for complex problems with revision capability. Use for multi-step reasoning, hypothesis verification, adaptive planning, problem decomposition, course correction.","https:\u002F\u002Fgithub.com\u002Fyosnap\u002Fdevdock\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fsequential-thinking\u002FSKILL.md",[],[226,227,228,229,230,231,232,233,234,125,235,236,33,31,237,238],"sequential thinking","thinking","ck:sequential thinking","planning","reasoning","verification","hypothesis","analysis","analyze","decomposition","problem","course","revision",{"skillNo":240,"name":241,"nameZh":6,"description":242,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":137,"licenseFileUrl":6,"sourceHtmlUrl":243,"categories":244,"keywords":248,"publishedAt":44},"01M6GTB26Y169PZFEX59Q9KM7F","ck:research","Research technical solutions, analyze architectures, gather requirements thoroughly. Use for technology evaluation, best practices research, solution design, scalability\u002Fsecurity\u002Fmaintainability analysis.","https:\u002F\u002Fgithub.com\u002Fyosnap\u002Fdevdock\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fresearch\u002FSKILL.md",[245],{"categoryNo":246,"categoryName":247,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[249,241,177,117,250,36,233,234,251,125,252,253,254,255,256],"research","best practices","technical","scalability","technology","requirements","maintainability","architectures"]