skilld
All skills
google avatar

/google-cloud-solution-agentic-ai-data-science-workflow

@becc4b8
by googlegoogle/skills21k stars
1,698

Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.

Ask your Agent

Use this Skill: https://skilld.dev/gh/google/skills/google-cloud-solution-agentic-ai-data-science-workflow

This session only. Nothing lands on disk.

referencesproduct-mapping.md

≈796 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Product Mapping Guidance

For each component in the confirmed technical decomposition and agentic design pattern, identify the appropriate Google Cloud products and features, based on the following guidance. Use https://docs.cloud.google.com/architecture/choose-agentic-ai-architecture-components.md.txt to ground the product mapping and guidance that you generate.

  • Frontend interface:
    • Recommended primary product: Cloud Run
    • Alternative product 1: Google Kubernetes Engine (GKE)
      • Pros: Full control over container runtimes, custom ingress/routing, and built-in VPC integration for strict private internal network access and governance.
      • Cons: High operational management complexity, manual cluster lifecycle overhead, and higher base infrastructure costs.
  • Runtime for your agent:
    • Recommended primary product: Cloud Run
    • Alternative product 1: Gemini Enterprise Agent Runtime
      • Pros: Fully managed Python runtime, built-in memory storage, and secure code execution sandbox.
      • Cons: Limited to Python, doesn't support hosting custom MCP servers, and less control over container environment.
    • Alternative product 2: Google Kubernetes Engine (GKE)
      • Pros: Maximum infrastructure control, stateful pods, and custom scaling.
      • Cons: High operational complexity and overhead.
  • Database & Data Warehouse:
    • Recommended primary product: Google Cloud Databases. Use the recommendations listed to help the user choose the appropriate database option.
  • Database connectivity:
    • Recommended primary product: MCP Toolbox for Databases or Google Cloud MCP servers.
    • Alternative product 1: Custom MCP servers
      • Pros: Full control over tool schemas, custom data transformations, and custom authentication logic.
      • Cons: Requires that you build, host, and maintain custom container infrastructure and connection pooling.
    • Alternative product 2: ADK built-in tools
      • Pros: Direct framework integration with zero additional infrastructure or MCP protocol overhead.
      • Cons: Limited to supported built-in tool types and lacks centralized MCP connection pooling across agent runtimes.
  • Model runtime:
    • Recommended primary product: Gemini Enterprise Agent Platform
    • Alternative product 1: Cloud Run
      • Pros: Serverless hosting for containerized open/custom models.
      • Cons: Can't serve Google Gemini models and requires manual instance scaling overhead.
    • Alternative product 2: Google Kubernetes Engine (GKE)
      • Pros: Maximum control over inference server on compute nodes and is cheap for predictable high volume.
      • Cons: Can't run Google Gemini models and has high cluster management overhead.
  • Model selection:
    • Recommended primary product: Gemini Flash
    • Alternative product 1: Gemini Pro
      • Pros: Highest capability for reasoning, complex instructions, context tracking, and multi-agent coordination.
      • Cons: Higher request cost and latency, which makes it less suitable for real-time conversational requirements.

Source: SKILL.md on GitHub

Third-party checks

No alerts9d3 checks · Risk SAFE
  • Gen Agent Trust Hub9d

    This skill provides a structured framework for designing and implementing agentic AI workflows on Google Cloud. It includes security considerations such as the generation of infrastructure-as-code and validation scripts based on user requirements. These features are fundamental to the skill's utility and are supported by references to official documentation and security best practices.

  • Socket9d

    No alerts

  • Snyk9d

    Risk: LOW · No issues

Provenance

Signed by skilld at becc4b8. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 2 weeks ago

Capability

metadata
{
  "version": "1.0.0",
  "category": "MultiProductSolutions"
}

README badge

README badge for google/skills/google-cloud-solution-agentic-ai-data-science-workflow