[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F6D8DPQMYH5MG0THSTVX8P":3,"skill-same:01M6F6D8DPQMYH5MG0THSTVX8P":89},{"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":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":66,"commitSha":67,"skillDirectory":68,"forksCount":69,"repositoryHtmlUrl":70,"repositoryHomepageUrl":71,"ownerHtmlUrl":72,"ownerBlogUrl":6,"ownerDescription":73,"updatedAt":74,"install":75,"resources":88},"01M6F6D8DPQMYH5MG0THSTVX8P","adk-deployment-specialist",null,"Deploy and orchestrate Vertex AI ADK agents using A2A protocol. Manages AgentCard discovery, task submission, Code Execution Sandbox, and Memory Bank. Use when asked to \"deploy ADK agent\" or \"orchestrate agents\". Trigger with phrases like 'deploy', 'infrastructure', or 'CI\u002FCD'.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fjeremylongshore\u002Fclaude-code-plugins-plus-skills\u002Fadk-deployment-specialist\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"adk","deployment","adk deployment specialist","CI\u002FCD","openclaw","code execution","trigger","io","project","edit","write","read","Bash","discovery","ci-cd","ci","orchestrate","code","infrastructure","memory","2026-11-10 10:12:30",false,"---\nname: adk-deployment-specialist\ndescription: |\n  Deploy and orchestrate Vertex AI ADK agents using A2A protocol. Manages AgentCard discovery, task submission, Code Execution Sandbox, and Memory Bank. Use when asked to \"deploy ADK agent\" or \"orchestrate agents\". Trigger with phrases like 'deploy', 'infrastructure', or 'CI\u002FCD'.\nallowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)\nversion: 2.1.0\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\nlicense: MIT\ncompatible-with: claude-code, codex, openclaw\neffort: high\nargument-hint: \"\u003Cagent-name or project-id>\"\ntags: [ai, deployment, ci-cd]\n\n---\n# Adk Deployment Specialist\n\n## Overview\n\nExpert in building and deploying production multi-agent systems using Google's Agent Development Kit (ADK). Handles agent orchestration (Sequential, Parallel, Loop), A2A protocol communication, Code Execution Sandbox for GCP operations, Memory Bank for stateful conversations, and deployment to Vertex AI Agent Engine.\n\n## Prerequisites\n\n- A Google Cloud project with Vertex AI enabled (and permissions to deploy Agent Engine runtimes)\n- ADK installed (and pinned to the project’s supported version)\n- A clear agent contract: tools required, orchestration pattern, and deployment target (local vs Agent Engine)\n- A plan for secrets\u002Fcredentials (OIDC\u002FWIF where possible; never commit long-lived keys)\n\n## Instructions\n\n1. Confirm the desired architecture (single agent vs multi-agent) and orchestration pattern (Sequential\u002FParallel\u002FLoop).\n2. Define the AgentCard + A2A interfaces (inputs\u002Foutputs, task submission, and status polling expectations).\n3. Implement the agent(s) with the minimum required tool surface (Code Execution Sandbox and\u002For Memory Bank as needed).\n4. Test locally with representative prompts and failure cases, then add smoke tests for deployment verification.\n5. Deploy to Vertex AI Agent Engine and validate the generated endpoints (`\u002F.well-known\u002Fagent-card`, task send\u002Fstatus APIs).\n6. Add observability: logs, dashboards, and retry\u002Fbackoff behavior for transient failures.\n\n## Output\n\n- Agent source files (or patches) ready for deployment\n- Deployment commands\u002Fconfig (e.g., `vertexai.Client.agent_engines.create()` invocation + required parameters)\n- A verification checklist for Agent Engine endpoints (AgentCard + task APIs) and security posture\n\n## Error Handling\n\nSee `${CLAUDE_SKILL_DIR}\u002Freferences\u002Ferrors.md` for comprehensive error handling.\n\n## Examples\n\nSee `${CLAUDE_SKILL_DIR}\u002Freferences\u002Fexamples.md` for detailed examples.\n\n## Resources\n\n- ADK docs: https:\u002F\u002Fcloud.google.com\u002Fvertex-ai\u002Fdocs\u002Fagent-engine\n- Workload Identity (CI\u002FCD): https:\u002F\u002Fcloud.google.com\u002Fiam\u002Fdocs\u002Fworkload-identity-federation\n- A2A \u002F AgentCard patterns: see `000-docs\u002F6767-a-SPEC-DR-STND-claude-code-plugins-standard.md`\n","# Adk Deployment Specialist\n\n## Overview\n\nExpert in building and deploying production multi-agent systems using Google's Agent Development Kit (ADK). Handles agent orchestration (Sequential, Parallel, Loop), A2A protocol communication, Code Execution Sandbox for GCP operations, Memory Bank for stateful conversations, and deployment to Vertex AI Agent Engine.\n\n## Prerequisites\n\n- A Google Cloud project with Vertex AI enabled (and permissions to deploy Agent Engine runtimes)\n- ADK installed (and pinned to the project’s supported version)\n- A clear agent contract: tools required, orchestration pattern, and deployment target (local vs Agent Engine)\n- A plan for secrets\u002Fcredentials (OIDC\u002FWIF where possible; never commit long-lived keys)\n\n## Instructions\n\n1. Confirm the desired architecture (single agent vs multi-agent) and orchestration pattern (Sequential\u002FParallel\u002FLoop).\n2. Define the AgentCard + A2A interfaces (inputs\u002Foutputs, task submission, and status polling expectations).\n3. Implement the agent(s) with the minimum required tool surface (Code Execution Sandbox and\u002For Memory Bank as needed).\n4. Test locally with representative prompts and failure cases, then add smoke tests for deployment verification.\n5. Deploy to Vertex AI Agent Engine and validate the generated endpoints (`\u002F.well-known\u002Fagent-card`, task send\u002Fstatus APIs).\n6. Add observability: logs, dashboards, and retry\u002Fbackoff behavior for transient failures.\n\n## Output\n\n- Agent source files (or patches) ready for deployment\n- Deployment commands\u002Fconfig (e.g., `vertexai.Client.agent_engines.create()` invocation + required parameters)\n- A verification checklist for Agent Engine endpoints (AgentCard + task APIs) and security posture\n\n## Error Handling\n\nSee `${CLAUDE_SKILL_DIR}\u002Freferences\u002Ferrors.md` for comprehensive error handling.\n\n## Examples\n\nSee `${CLAUDE_SKILL_DIR}\u002Freferences\u002Fexamples.md` for detailed examples.\n\n## Resources\n\n- ADK docs: https:\u002F\u002Fcloud.google.com\u002Fvertex-ai\u002Fdocs\u002Fagent-engine\n- Workload Identity (CI\u002FCD): https:\u002F\u002Fcloud.google.com\u002Fiam\u002Fdocs\u002Fworkload-identity-federation\n- A2A \u002F AgentCard patterns: see `000-docs\u002F6767-a-SPEC-DR-STND-claude-code-plugins-standard.md`\n",{"name":5,"tags":57,"author":59,"effort":60,"license":15,"version":61,"description":62,"allowed-tools":63,"argument-hint":64,"compatible-with":65},[58,33,46],"ai","Jeremy Longshore \u003Cjeremy@intentsolutions.io>","high","2.1.0","Deploy and orchestrate Vertex AI ADK agents using A2A protocol. 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