Adk Deployment Specialist Skill for Claude

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''. '

11k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

2679

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill adk-deployment-specialist

What comes with it

41 296 bytes besides the instruction

references/ARD.md

references/PRD.md

references/errors.md

references/examples.md

references/how-it-works.md

references/workflow-examples.md

scripts/deploy-agent.sh

scripts/test-a2a-protocol.py

What it tells the agent to use

found in the instruction text

Bash runs shell commands — read the instruction before connecting

The instruction itself

8 sections, as written by the author

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

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

ADK docs:
Workload Identity (CI/CD): https://cloud.google.com/iam/docs/workload-identity-federation
A2A / AgentCard patterns: see 000-docs/6767-a-SPEC-DR-STND-claude-code-plugins-standard.md

How to use it

Copy the folder

Take jeremylongshore/adk-deployment-specialist from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.