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Adk Engineer by cor-incorporated — Agent Skill

Name: Adk Engineer
Author: Cor-Incorporated

Adk Engineer

Implement production-ready agents using Google Agent Development Kit (ADK). Use when creating ADK agents, building multi-agent systems, implementing workflow agents, reviewing ADK code, or deploying to Vertex AI / Cloud Run. Triggers: 'ADK agent', 'Google ADK', 'multi-agent system', 'Gemini agent', 'agent workflow', 'adk deploy'. Do NOT use for non-ADK agent frameworks (LangChain, CrewAI, AutoGen) or general Gemini API calls without ADK.

File contents

ADK Engineer

Implement, review, and deploy Google ADK agents following proven patterns and best practices.

Quick Start

Single agent: Read references/adk-patterns.md -> Use assets/templates/single-agent/ -> Validate with references/adk-best-practices.md.

Multi-agent system: Choose pattern (Coordinator, Sequential, Parallel, Loop) -> Use matching template from assets/templates/ -> Consult references/adk-patterns.md.

Pattern Selection

Task Type	Pattern	Template
Linear data processing	Sequential Pipeline	`assets/templates/workflow/`
Intent-based routing	Coordinator/Dispatcher	`assets/templates/multi-agent/`
Independent concurrent tasks	Parallel Fan-Out/Gather	`assets/templates/workflow/`
Nested agent structures	Hierarchical Decomposition	`assets/templates/multi-agent/`
Validation loops	Generator-Critic	`assets/templates/workflow/`
Quality improvement	Iterative Refinement	`assets/templates/workflow/`
Critical decisions	Human-in-the-Loop	`assets/templates/single-agent/`

Implementation Workflow

New Agent

Choose pattern from table above based on task analysis.

Copy template:

cp -r assets/templates/[single-agent|multi-agent|workflow]/ ./my_agent/
# Or: bash scripts/init_adk_project.sh my_agent [template_type]

Implement domain logic: Define custom tools, update instructions, configure output_key values.
Test locally: Run python agent.py or adk ui.

Validate:

python scripts/validate_agent.py agent.py

Deploy per references/adk-deployment.md.

Code Review

Read agent code and identify patterns used.
Check implementation against references/adk-patterns.md.
Review against references/adk-best-practices.md.
Run python scripts/validate_agent.py agent.py.
Provide specific, actionable recommendations with references.

Validation Checklist

root_agent properly defined
Agent descriptions are clear and specific
Descriptive output_key values for state management
Loop agents have max_iterations
Parallel agents use unique output_key values
Tools follow verb-noun naming pattern
Error handling with graceful degradation
Appropriate model selection per agent complexity
REQUIRE_CONFIRMATION for high-stakes operations

Deployment

# Vertex AI Agent Engine (recommended)
adk deploy agent_engine --project=PROJECT_ID --region=us-central1 \
  --staging_bucket=gs://BUCKET/staging --agent-file=agent.py

# Cloud Run
adk deploy cloud_run --project=PROJECT_ID --region=us-central1 \
  --agent-file=agent.py

See references/adk-deployment.md for Docker, CI/CD, monitoring, and security details.

Error Handling

If validate_agent.py reports issues, fix all CRITICAL/HIGH items before deployment.
If pattern selection is ambiguous, default to Coordinator for routing tasks, Sequential for pipelines.
If deployment fails, check project permissions and staging bucket access first.

References

references/adk-patterns.md -- 7 design patterns with code examples
references/adk-best-practices.md -- Agent design, state management, tools, security
references/adk-deployment.md -- Deployment workflows, CI/CD, monitoring, cost optimization
assets/templates/ -- Single-agent, multi-agent, workflow starter templates
Google ADK Docs

---name: adk-engineerdescription: Implement production-ready agents using Google Agent Development Kit (ADK). Use when creating ADK agents, building multi-agent systems, implementing workflow agents, reviewing ADK code, or deploying to Vertex AI / Cloud Run. Triggers: 'ADK agent', 'Google ADK', 'multi-agent system', 'Gemini agent', 'agent workflow', 'adk deploy'. Do NOT use for non-ADK agent frameworks (LangChain, CrewAI, AutoGen) or general Gemini API calls without ADK.---# ADK EngineerImplement, review, and deploy Google ADK agents following proven patterns and best practices.## Quick Start**Single agent:** Read `references/adk-patterns.md` -> Use `assets/templates/single-agent/` -> Validate with `references/adk-best-practices.md`.**Multi-agent system:** Choose pattern (Coordinator, Sequential, Parallel, Loop) -> Use matching template from `assets/templates/` -> Consult `references/adk-patterns.md`.<important if="choosing an ADK agent pattern or designing a multi-agent system architecture">## Pattern Selection| Task Type | Pattern | Template ||-----------|---------|----------|| Linear data processing | Sequential Pipeline | `assets/templates/workflow/` || Intent-based routing | Coordinator/Dispatcher | `assets/templates/multi-agent/` || Independent concurrent tasks | Parallel Fan-Out/Gather | `assets/templates/workflow/` || Nested agent structures | Hierarchical Decomposition | `assets/templates/multi-agent/` || Validation loops | Generator-Critic | `assets/templates/workflow/` || Quality improvement | Iterative Refinement | `assets/templates/workflow/` || Critical decisions | Human-in-the-Loop | `assets/templates/single-agent/` |</important>## Implementation Workflow<important if="creating a new ADK agent from a template">### New Agent1. **Choose pattern** from table above based on task analysis.2. **Copy template:** ```bash cp -r assets/templates/[single-agent|multi-agent|workflow]/ ./my_agent/ # Or: bash scripts/init_adk_project.sh my_agent [template_type] ```3. **Implement domain logic:** Define custom tools, update instructions, configure `output_key` values.4. **Test locally:** Run `python agent.py` or `adk ui`.5. **Validate:** ```bash python scripts/validate_agent.py agent.py ```6. **Deploy** per `references/adk-deployment.md`.</important><important if="reviewing existing ADK agent code for correctness and best practices">### Code Review1. Read agent code and identify patterns used.2. Check implementation against `references/adk-patterns.md`.3. Review against `references/adk-best-practices.md`.4. Run `python scripts/validate_agent.py agent.py`.5. Provide specific, actionable recommendations with references.</important><important if="validating an ADK agent before deployment or during code review">## Validation Checklist- [ ] `root_agent` properly defined- [ ] Agent descriptions are clear and specific- [ ] Descriptive `output_key` values for state management- [ ] Loop agents have `max_iterations`- [ ] Parallel agents use unique `output_key` values- [ ] Tools follow verb-noun naming pattern- [ ] Error handling with graceful degradation- [ ] Appropriate model selection per agent complexity- [ ] `REQUIRE_CONFIRMATION` for high-stakes operations</important><important if="deploying an ADK agent to Vertex AI Agent Engine or Cloud Run">## Deployment```bash# Vertex AI Agent Engine (recommended)adk deploy agent_engine --project=PROJECT_ID --region=us-central1 \ --staging_bucket=gs://BUCKET/staging --agent-file=agent.py# Cloud Runadk deploy cloud_run --project=PROJECT_ID --region=us-central1 \ --agent-file=agent.py```See `references/adk-deployment.md` for Docker, CI/CD, monitoring, and security details.</important>## Error Handling- If `validate_agent.py` reports issues, fix all CRITICAL/HIGH items before deployment.- If pattern selection is ambiguous, default to Coordinator for routing tasks, Sequential for pipelines.- If deployment fails, check project permissions and staging bucket access first.## References- `references/adk-patterns.md` -- 7 design patterns with code examples- `references/adk-best-practices.md` -- Agent design, state management, tools, security- `references/adk-deployment.md` -- Deployment workflows, CI/CD, monitoring, cost optimization- `assets/templates/` -- Single-agent, multi-agent, workflow starter templates- [Google ADK Docs](https://google.github.io/adk-docs/)

Frequently asked questions