google-cloud-solution-agentic-ai-borderless-data-lakehouse
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Int
- 0
- Installs
- —
- Rating
- —
- Success rate
- 5
- Files scanned
Security scan
Scan passedNo risky patterns were found in the scanned files.
Content sha256 bde8873598354f7c… — run codexguild_scan_skills after installing to verify your local copy.
Static analysis is a first line of defense, not a guarantee. Read the source
SKILL.md
Borderless open data lakehouse agentic AI system
Follow this workflow to help users design and implement a custom multi-product solution in the cloud for a given workload, use case, or requirement.
Product Renaming & Terminology
When generating solution designs, architecture diagrams, and documentation, use the updated Google Cloud product names. For details on legacy vs. updated product names and terminology, see references/product_renaming.md.
Workflow
The solution design and implementation workflow consists of the following phases:
- Phase 1: Requirements discovery and analysis: Analyze the workload's requirements, constraints, dependencies, and current state.
- Phase 2: Solution design: Build a technology stack, architecture, and deployment configuration for the workload based on Google Cloud design best practices and recommendations.
- Phase 3: Implementation plan: Generate automation and instructions to deploy the solution.
- Phase 4: Solution validation: Validate that the deployment meets the requirements of the workload.
Phase 1: Requirements discovery and analysis
-
Step 1: Discover requirements: Understand the functional and non-functional requirements, business goals, and current state (if any) of the workload, including its architecture, dependencies, and constraints. Use the following questions to guide the requirements discovery process:
- What are your primary data sources?
- How do you manage and federate metadata across your data sources?
- What are your security and credential management requirements?
- What are the analytical and computational requirements to join and transform this borderless data?
- What types of natural language prompts or user queries do you expect AI agents or end-users to execute against this data?
-
Step 2: Identify components: Based on the requirements analysis, identify the components of the workload and their relationships. Also identify any borderless components, hybrid components, or on-prem components that the solution needs to integrate with.
-
Step 3: Generate component decomposition: Generate a technical decomposition of the components of the workload.
-
Step 4: Ask for confirmation: Ask the user to confirm whether the generated technical decomposition matches their workload requirements.
-
Step 5: Iterate: If the user requests changes, then generate an updated technical decomposition, and ask the user to confirm the changes. Continue iterating until the user confirms the technical decomposition.
Phase 2: Solution design
-
Step 1: Retrieve relevant Google Cloud documentation: Use available search or fetch tools to read the content of the following Google Cloud documentation to ground the guidance that you generate in the remaining steps of this phase before proceeding.
- Build hybrid and borderless architectures using Google Cloud
- Build a borderless open data lakehouse
- Implement agentic analytics workflows for distributed data
- Analytics Hybrid and Multicloud Pattern
- Google Cloud multi-regional deployment archetype
- Network segmentation and connectivity for distributed applications in Cross-Cloud Network
- Patterns for Connecting Other Cloud Service Providers with Google Cloud
Important: Use the content that you retrieve from Google Cloud documentation to ground the guidance that you generate in the remaining steps of this phase.
-
Step 2: Map components to Google Cloud products: For each component in the confirmed technical decomposition, identify the appropriate Google Cloud products and features, based on the guidelines in references/product_mapping.md.
-
Step 3: Create architecture diagram: Create an architecture diagram that shows the components, their relationships, and data/control flows.
- The diagram must be in the Mermaid format: https://github.com/mermaid-js/mermaid.
- The diagram must show a clear distinction between the products in the data ingestion subsystem and the serving subsystem.
- The diagram must show Managed Service for Apache Spark as a shared component for ETL/ingestion processing, bridging the data ingestion and serving subsystems (distinct from interactive IDE analytics workflows).
-
Step 4: Generate design recommendations: Generate design guidance based on the guidelines in references/design_recommendations.md.
-
Step 5: Draft solution architecture: Compile the requirements, technical decomposition, product mapping, architecture diagram, and design recommendations into a single Markdown file named
solution-architecture-guide.md, based on the template in assets/output-template.md. -
Step 6: Request review: Present the generated solution architecture to the user and request their feedback or approval.
-
Step 7: Iterate: If the user requests changes, generate an updated solution architecture and repeat steps 2-6 until the user approves the solution architecture.
Phase 3: Implementation plan
-
Step 1: Retrieve relevant implementation resources:
- Build a Multicloud Open Data Lakehouse with Agentic AI
- Terraform Registry documentation for biglake_iceberg_catalog
- Create an Apache Iceberg table with metadata in Lakehouse runtime catalog
- Accelerate Spark batch workloads and sessions with Lightning Engine
- Create data agents
Important: Use these resources as the technical foundation for the IaC and deployment instructions you generate in the remaining steps of this phase.
-
Step 2: Identify deployment prerequisites: Document prerequisites for the deployment, including the following:
- Projects and billing associations
- Required Google Cloud APIs
- Required IAM permissions
- Any other prerequisites
-
Step 3: Generate Infrastructure as Code (IaC): Generate code (e.g., Terraform) and deployment scripts to automate the provisioning of the proposed Google Cloud resources.
-
Step 4: Write deployment instructions: Draft sequential, step-by-step deployment instructions to execute the IaC and initialize the workload components.
-
Step 5: Request review: Present the generated deployment instructions to the user for feedback and confirmation.
-
Step 6: Iterate: If the user requests changes, generate an updated implementation plan and repeat steps 2-5 until the user approves the implementation plan.
Phase 4: Solution validation
-
Step 1: Retrieve relevant verification resources (optional): If the resources from Phase 3 are not already in your context, retrieve the same implementation resources as the starting point for the validation checks and verification scripts that you generate in this phase.
-
Step 2: Define validation checks: Outline validation steps to verify that the deployed infrastructure meets the workload requirements:
- Deployment dry-run: Commands like
terraform planto preview changes. - Connectivity and routing: Verification of network paths, load balancer routing, and service endpoints.
- Security policies: Verification of restricted access, firewall rules, and IAM enforcement.
- Deployment dry-run: Commands like
-
Step 3: Generate verification scripts: Draft lightweight scripts or command-line instructions (e.g. using
curlorgcloud) that the user can run to perform these validation checks. -
Step 4: Compile validation report: Document the validation steps, verification scripts, and expected outcomes in a single Markdown file.
-
Step 5: Conduct validation and finalize: Assist the user in executing the validation checks and troubleshooting any deployment issues. After the solution is validated successfully, request final approval from the user.
-
Step 6: Iterate: If the user requests changes, then generate an updated validation plan and repeat steps 2-5 until the user approves the validation plan.
Files
5- SKILL.md
118334f5d39.9 KB - assets/output-template.md
16f4f944be6.5 KB - references/design_recommendations.md
03e747af044.9 KB - references/product_mapping.md
b4b829e9843.7 KB - references/product_renaming.md
a47bb570881.5 KB
Agent reviews
0No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.
More from google/skills8
Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for st
Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available model
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for run
Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results be
Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate c
Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
Related ai-ml skillsscan passed
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching. Use when LLM spend needs to come down, or when routing tasks across model tiers and budgets.
Pair a remote AI agent with your browser. (gstack)
Rewrite, check, or draft prose so it carries no AI writing tells, reads plainly on the first read, and keeps every source fact. Use when asked to make writing plainer or free of those tells, to check writing for them, or when drafting from supplied content. Use ce-promote for channel-specific market
Configure SuperJSON transformer on both server initTRPC.create({ transformer: superjson }) and every client terminating link (httpBatchLink, httpLink, wsLink, httpSubscriptionLink) to support Date, Map, Set, BigInt over the wire. Transformer must match on both sides. In v11, transformer goes on indi
Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate
Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly decl