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
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1---2name: jeremylongshore-claude-code-plugins-plus-skills-adk-engineer3description: ADK Engineer4---5# ADK Engineer67Engineer production-ready Agent Development Kit (ADK) agents and multi-agent systems: clean structure, testability, safe tool usage, and deployment automation.89## Overview1011Use 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).1213## Prerequisites1415- A target runtime (Python/Java/Go) consistent with the project’s pinned versions16- ADK installed (and any required model/provider SDKs configured)17- A test runner available in the repo (unit tests at minimum)18- If deploying: access to a Google Cloud project and permissions for the chosen deployment target1920## Instructions21221. Clarify requirements: agent goals, tool surface, latency/cost constraints, and deployment target.232. Propose architecture: single agent vs multi-agent, orchestration pattern, state strategy (Memory Bank / external store).243. Scaffold structure: agent entrypoint(s), tool modules, config, and tests.254. Implement incrementally:26 - add one tool at a time with input validation and structured outputs27 - add regression tests for each tool and critical prompt flows285. Add operational guardrails: retries/backoff, timeouts, logging, and safe error messages.296. Validate locally (tests + smoke prompts) and provide a deployment plan (when requested).3031## Output3233- A concrete architecture plan and file layout34- Agent and tool implementations (or patches) with tests35- A validation checklist (commands to run, expected outputs, and failure triage)36- Optional: deployment instructions and post-deploy health checks3738## Error Handling3940- Build/test failures: isolate the failing module, minimize the repro, fix, and add a regression test.41- Tool/runtime errors: enforce structured error responses and safe retries where appropriate.42- Deployment failures: provide the exact failing command, logs to inspect, and least-privilege IAM fixes.4344## Examples4546**Example: Productionizing an existing ADK agent**47- Request: “Refactor this agent into a clean module structure and add tests before we deploy.”48- Result: reorganized `src/` layout, tool boundaries, a test suite, and a deployment checklist.4950**Example: Multi-agent workflow**51- Request: “Build a validator + deployer + monitor agent team with a sequential orchestrator.”52- Result: orchestrator skeleton, per-agent responsibilities, and smoke tests for each step.5354## Resources5556- Full detailed playbook (kept for reference): `${CLAUDE_SKILL_DIR}/references/SKILL.full.md`57- Repo standards (source of truth):58 - `000-docs/6767-a-SPEC-DR-STND-claude-code-plugins-standard.md`59 - `000-docs/6767-b-SPEC-DR-STND-claude-skills-standard.md`60- ADK / Agent Engine docs: https://cloud.google.com/vertex-ai/docs/agent-engine6162---63> Converted and distributed by [TomeVault](https://tomevault.io/claim/jeremylongshore) — claim your Tome and manage your conversions.64<!-- tomevault:4.0:skill_md:2026-04-11 -->