Scaffolds and engineers robust, testable, and deployable agent systems using the Agent Development Kit.

Install

mkdir -p .claude/skills/adk-engineer && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1170" && unzip -o skill.zip -d .claude/skills/adk-engineer && rm skill.zip

Installs to .claude/skills/adk-engineer

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Execute software engineer specializing in creating production-ready
67 charsno explicit “when” trigger
Advanced

Key capabilities

  • Propose ADK agent architecture
  • Scaffold agent entrypoints and tool modules
  • Implement tools with input validation
  • Add regression tests for tools and prompt flows
  • Add operational guardrails like retries and logging
  • Provide a deployment plan and validation checklist

How it works

The skill clarifies requirements, proposes architecture, scaffolds the agent structure, incrementally implements tools with validation and tests, adds operational guardrails, and validates locally before providing a deployment plan.

Inputs & outputs

You give it
Requirements for an ADK agent, including goals, tools, constraints, and deployment target
You get back
Architecture plan, agent/tool implementations, tests, validation checklist, and optional deployment instructions

When to use adk-engineer

  • Build ADK agents
  • Create agent code structure
  • Engineer multi-agent systems
  • Set up agent regression tests

About this skill

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

  1. Clarify requirements: agent goals, tool surface, latency/cost constraints, and deployment target.
  2. Propose architecture: single agent vs multi-agent, orchestration pattern, state strategy (Memory Bank / external store).
  3. Scaffold structure: agent entrypoint(s), tool modules, config, and tests.
  4. Implement incrementally:
    • add one tool at a time with input validation and structured outputs
    • add regression tests for each tool and critical prompt flows
  5. Add operational guardrails: retries/backoff, timeouts, logging, and safe error messages.
  6. 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): ${CLAUDE_SKILL_DIR}/references/SKILL.full.md
  • Repo standards (source of truth):
    • 000-docs/6767-a-SPEC-DR-STND-claude-code-plugins-standard.md
    • 000-docs/6767-b-SPEC-DR-STND-claude-skills-standard.md
  • ADK / Agent Engine docs:

Prerequisites

A target runtime (Python/Java/Go) consistent with the project’s pinned versionsADK 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

How it compares

This skill provides a structured, best-practices approach to building production-ready ADK agents, contrasting with ad-hoc development that may lack maintainability, testability, and deployment considerations.

Compared to similar skills

adk-engineer side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
adk-engineer (this skill)326dReviewAdvanced
copilot-sdk74moReviewIntermediate
llm-application-dev34moReviewIntermediate
agent-implementer-sparc-coder16moReviewIntermediate

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