adk-engineer

# 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