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/google-cloud-solution-build-deploy-agents

@becc4b8
by googlegoogle/skills21k stars
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Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.

Use this Skill: https://skilld.dev/gh/google/skills/google-cloud-solution-build-deploy-agents

This session only. Nothing lands on disk.

assetsvalidation-template.md

≈541 tokens on demand. Your agent reads this file only when SKILL.md points to it.

<!-- Use this template to compile the solution validation that you generate based on the instructions in `SKILL.md`. -->

1. Validation Plan & Verification Report

This validation plan outlines the dry-run, connectivity, routing, and security checks to verify that the deployed infrastructure meets all functional and non-functional requirements.

1.1. Infrastructure Dry-Run

Verify the deployment plan using preview tools before applying changes.

  • Command:
    terraform plan -out=tfplan
  • Expected Outcome: Preview validates resource hierarchy and shows exactly what resources will be created, updated, or destroyed without configuration errors.

1.2. Network Connectivity & Routing

[Draft workloads-specific connectivity checks. E.g., test load balancer endpoints, serverless VPC path latency, private database access, or DNS resolution rules.]

  • Command:
    curl -iv -H "Host: [Target Domain]" https://[LB-IP-Address]/healthz
  • Expected Outcome: Returns HTTP 200 OK from the correct regional serverless endpoint.

1.3. Security and Access Governance

Verify IAM role mappings, service account isolation, and network perimeter rules.

  • Command:
    gcloud projects get-iam-policy [GCP-Project-ID] \
        --flatten="bindings[].members" \
        --format='table(bindings.role, bindings.members)' \
        --filter="bindings.members:[Service-Account-Email]"
  • Expected Outcome: Confirms that only the specified roles are granted, enforcing the principle of least privilege.

1.4. Data Protection & Content Inspection

Audit content filtering, input sanitization/redaction rules, and safety/PII guardrails.

  • Command:
    # Test request with mock PII to audit redaction filters
    curl -X POST -H "Content-Type: application/json" \
         -d '{"prompt": "[Mock Sensitive Data Code/PII Pattern]"}' \
         https://[Target Domain]/chat
  • Expected Outcome: Request is either anonymized/masked by the security layer (e.g., Model Armor) or blocked with a safety intercept notice.

Source: SKILL.md on GitHub

No alerts9d3 checks · Risk SAFE
  • Gen Agent Trust Hub9d

    This skill provides a structured workflow for designing and deploying AI agents on Google Cloud. It incorporates security best practices, such as least privilege IAM and human-in-the-loop review, and utilizes official Google developer tools. No malicious patterns were detected.

  • Socket9d

    No alerts

  • Snyk9d

    Risk: LOW · No issues

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

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