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Adk deployment specialist

Skill jeremylongshore/claude-code-plugins-plus-skills/plugins/ai-ml/jeremy-adk-orchestrator/skills/adk-deployment-specialist

425 plugins, 2,810 skills, 200 agents for Claude Code. Open-source marketplace at tonsofskills.com with the ccpi CLI package manager.

Install
npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill adk-deployment-specialist

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What its author says it does

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'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/CD''.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.7 KB, 450 tokens by cl100k_base, as published. Nobody here has run it

Adk Deployment Specialist

Overview

Expert 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.

Prerequisites

  • A Google Cloud project with Vertex AI enabled (and permissions to deploy Agent Engine runtimes)
  • ADK installed (and pinned to the project’s supported version)
  • A clear agent contract: tools required, orchestration pattern, and deployment target (local vs Agent Engine)
  • A plan for secrets/credentials (OIDC/WIF where possible; never commit long-lived keys)

Instructions

  1. Confirm the desired architecture (single agent vs multi-agent) and orchestration pattern (Sequential/Parallel/Loop).
  2. Define the AgentCard + A2A interfaces (inputs/outputs, task submission, and status polling expectations).
  3. Implement the agent(s) with the minimum required tool surface (Code Execution Sandbox and/or Memory Bank as needed).
  4. Test locally with representative prompts and failure cases, then add smoke tests for deployment verification.
  5. Deploy to Vertex AI Agent Engine and validate the generated endpoints (/.well-known/agent-card, task send/status APIs).
  6. Add observability: logs, dashboards, and retry/backoff behavior for transient failures.

Output

  • Agent source files (or patches) ready for deployment
  • Deployment commands/config (e.g., vertexai.Client.agent_engines.create() invocation + required parameters)
  • A verification checklist for Agent Engine endpoints (AgentCard + task APIs) and security posture

Error Handling

See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error handling.

Examples

See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed examples.

Resources

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