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