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): ${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: https://cloud.google.com/vertex-ai/docs/agent-engine