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Cor-IncorporatedCor-Incorporated/claude-code-skills/skills/adk-engineer/SKILL.md

adk-engineer

Skill5 starsChanged 2 months agoMITView on GitHub

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.

1 other project uses a version of this.

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The file

We split this skill into the rules it gives an agent

7 rules. About 0 are common, 3 are distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

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

    line 13
  2. Very specific:

    Test locally: Run python agent.py or adk ui.

    line 46
  3. Distinctive:

    Read agent code and identify patterns used.

    line 59
  4. Very specific:

    Check implementation against references/adk-patterns.md.

    line 60
  5. Very specific:

    If validate_agent.py reports issues, fix all CRITICAL/HIGH items before deployment.

    line 103
  6. Distinctive:

    If pattern selection is ambiguous, default to Coordinator for routing tasks, Sequential for pipelines.

    line 104
  7. Distinctive:

    If deployment fails, check project permissions and staging bucket access first.

    line 105

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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.

<important if="choosing an ADK agent pattern or designing a multi-agent system architecture">

Pattern Selection

Task TypePatternTemplate
Linear data processingSequential Pipelineassets/templates/workflow/
Intent-based routingCoordinator/Dispatcherassets/templates/multi-agent/
Independent concurrent tasksParallel Fan-Out/Gatherassets/templates/workflow/
Nested agent structuresHierarchical Decompositionassets/templates/multi-agent/
Validation loopsGenerator-Criticassets/templates/workflow/
Quality improvementIterative Refinementassets/templates/workflow/
Critical decisionsHuman-in-the-Loopassets/templates/single-agent/
</important>

Implementation Workflow

<important if="creating a new ADK agent from a template">

New Agent

  1. Choose pattern from table above based on task analysis.
  2. Copy template:
    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:
    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 Review

  1. 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

# 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.

</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

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