ADK software engineer for creating production-ready agents (placeholder - to be implemented)

1 Skills

MIT License

Free Pricing

Installation

Open Claude Code and run this command:

/plugin install jeremy-adk-software-engineer@claude-code-plugins-plus

Use --global to install for all projects, or --project for current project only.

What It Does

This plugin provides an auto-activating skill that acts as a senior ADK software engineer. It helps you build maintainable, testable, and deployable ADK agents following Google Cloud best practices. Whether you are creating a single-agent tool-calling application or a multi-agent orchestration pipeline, this plugin delivers structured code, comprehensive tests, and deployment-ready configurations.

Features

Agent Architecture Design: Single-agent and multi-agent system patterns (Sequential, Parallel, Loop)
Clean Code Implementation: Modular project structure with proper separation of concerns
Comprehensive Testing: Unit, integration, and end-to-end test scaffolding with pytest
Deployment Automation: CI/CD pipelines for Agent Engine, Cloud Run, and GKE
Tool Engineering: Typed tool interfaces with FunctionTool, input validation, and structured outputs
Orchestration Patterns: SequentialAgent, ParallelAgent, and custom workflow coordination
Production Guardrails: Retries with backoff, timeouts, structured logging, and safe error messages
Multi-Language Support: Python (primary), Java, and Go

Skills (1)

adk-engineer SKILL.md View full skill →

Execute software engineer specializing in creating production-ready ADK agents with best practices, code structure, testing, and deployment automation.

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ADK Engineer

Engineer production-ready Agent Development Kit (ADK) agents and multi-agent systems: clean structure, testability, safe tool usage, and deployment automation.

Overview

Use this skill to design and implement ADK agent code that is maintainable and shippable: clear module boundaries, structured tool interfaces, regression tests, and a deployment checklist (local or Agent Engine).

Prerequisites

A target runtime (Python/Java/Go) consistent with the project’s pinned versions
ADK installed (and any required model/provider SDKs configured)
A test runner available in the repo (unit tests at minimum)
If deploying: access to a Google Cloud project and permissions for the chosen deployment target

Instructions

Clarify requirements: agent goals, tool surface, latency/cost constraints, and deployment target.
Propose architecture: single agent vs multi-agent, orchestration pattern, state strategy (Memory Bank / external store).
Scaffold structure: agent entrypoint(s), tool modules, config, and tests.
Implement incrementally:

add one tool at a time with input validation and structured outputs
add regression tests for each tool and critical prompt flows

Add operational guardrails: retries/backoff, timeouts, logging, and safe error messages.
Validate locally (tests + smoke prompts) and provide a deployment plan (when requested).

Output

A concrete architecture plan and file layout
Agent and tool implementations (or patches) with tests
A validation checklist (commands to run, expected outputs, and failure triage)
Optional: deployment instructions and post-deploy health checks

Error Handling

Build/test failures: isolate the failing module, minimize the repro, fix, and add a regression test.
Tool/runtime errors: enforce structured error responses and safe retries where appropriate.
Deployment failures: provide the exact failing command, logs to inspect, and least-privilege IAM fixes.

Examples

Example: Productionizing an existing ADK agent

Request: “Refactor this agent into a clean module structure and add tests before we deploy.”
Result: reorganized src/ layout, tool boundaries, a test suite, and a deployment checklist.

Example: Multi-agent workflow

Request: “Build a validator + deployer + monitor agent team with a sequential orchestrator.”
Result: orchestrator skeleton, per-agent responsibilities, and smoke tests for each step.

Resources

Full detailed playbook (kept for reference): ${CLAUDESKILLDIR}/references/SKILL.full.md
Repo standards (source of truth):
000-docs



      
      
          How It Works
          
pip install google-adk google-cloud-aiplatform[agent_engines]

Then ask Claude: "Build an ADK agent that [your use case]"
        

      
      
          Use Cases
          
Greenfield Agent Development: Scaffold a new ADK project from scratch with best practices
Production Hardening: Add tests, error handling, and monitoring to an existing agent
Multi-Agent Systems: Design and implement orchestrated agent teams
CI/CD Pipelines: Generate GitHub Actions workflows for automated testing and deployment
Code Review Agents: Build agents that lint, review, and analyze code quality

        

      
      

      
      
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          Tags
          
            adksoftware-engineeragent-developmentvertex-ai