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SKILL.md
npx skills add google/skills --skill google-cloud-solution-agentic-ai-bidirectional-streaming
Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat applications or workloads without real-time streaming requirements.
npx skills add google/skills --skill google-cloud-solution-agentic-ai-bidirectional-streaming
This skill guides agents through the workflow to design and implement a tailored multi-product solution in the cloud for a live, bidirectional multimodal streaming workload, use case, or requirement.
The solution design and implementation workflow consists of the following phases:
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:
Step 2: Identify components: Based on the requirements analysis, identify the components of the workload and their relationships. Also identify any cross-cloud 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. The technical decomposition must break down the solution into logical components.
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.
Step 1: Retrieve relevant Google Cloud documentation:
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 and agentic design pattern, identify the appropriate Google Cloud products and features, based on the guidelines in references/product-mapping.md.
Step 3: Create architecture diagram: Generate an architecture diagram in Mermaid format: https://github.com/mermaid-js/mermaid.
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.
Step 1: Retrieve relevant implementation resources:
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:
Step 3: Generate Infrastructure as Code (IaC): Generate code, like 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. Update deployment instructions in
solution-architecture-guide.md, based on the template in
assets/output-template.md.
Step 5: Request review: Present the generated deployment instructions to the user for feedback and confirmation.
Step 6: Iterate: If the user requests changes, then generate an updated implementation plan and repeat steps 2-5 until the user approves the implementation plan.
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:
terraform plan to preview
changes. Step 3: Generate verification scripts: Draft lightweight scripts or
command-line instructions, such as using curl or gcloud, 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
solution-architecture-guide.md, based on the template in
assets/output-template.md.
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.