Skill UI

Provision production-grade Vertex AI ADK Agent Engine infrastructure with Terraform, covering Agent Engine runtime, code execution sandbox, Memory Bank, VPC Service Controls, and secure IAM setup for multi-agent deployments.

Argocd App Deployer is an auto-activating assistant for advanced DevOps operators working with Argo CD deployments. Mention “argocd app deployer” or request deployment best practices to get step-by-step recommendations, production-ready configs, validation, and guidance spanning Kubernetes, Terraform, Helm, monitoring, and IaC patterns.

Argocd App Deployer is an auto-activating assistant for advanced DevOps operators working with Argo CD deployments. Mention “argocd app deployer” or request deployment best practices to get step-by-step recommendations, production-ready configs, validation, and guidance spanning Kubernetes, Terraform, Helm, monitoring, and IaC patterns.

Activation-aware weight quantization that compresses 7B-70B models to 4-bit with minimal accuracy loss, delivering ~3x inference speedups and memory reductions for constrained GPU deployments while integrating with vLLM and Marlin kernels.

Provides canonical AWS serverless patterns covering Lambda handlers, DynamoDB single-table modeling, CDK infrastructure, and S3 event processing along with anti-patterns and a checklist for production-ready deployments.

This skill provides comprehensive guidance and established patterns for developing applications on Amazon Web Services (AWS). It focuses specifically on implementing robust infrastructure automation and adhering to best-practice cloud architecture patterns, ensuring scalable, secure, and resilient cloud deployments. Use this when building enterprise-grade systems requiring modern cloud development methodologies.

This skill provides comprehensive guidance and established patterns for developing applications on Amazon Web Services (AWS). It focuses specifically on implementing robust infrastructure automation and adhering to best-practice cloud architecture patterns, ensuring scalable, secure, and resilient cloud deployments. Use this when building enterprise-grade systems requiring modern cloud development methodologies.

This is a high-level SDK for .NET that provides comprehensive client access to Azure AI Foundry services. It enables developers to manage complex AI workflows, including creating and managing conversational agents, handling data connections, uploading datasets, managing model deployments, and setting up search indexes within an Azure AI project structure. Ideal for integrating advanced AI capabilities into enterprise applications.

This is a high-level Software Development Kit (SDK) designed for managing the full lifecycle of AI projects within Azure AI Foundry. Built for Java, it provides robust client interfaces for core AI operations, including managing connections to Azure resources, uploading and managing datasets, creating and updating search indexes, running model evaluations, and handling deployments. It streamlines complex AI workflow orchestration.

This is a high-level Software Development Kit (SDK) designed for managing the full lifecycle of AI projects within Azure AI Foundry. Built for Java, it provides robust client interfaces for core AI operations, including managing connections to Azure resources, uploading and managing datasets, creating and updating search indexes, running model evaluations, and handling deployments. It streamlines complex AI workflow orchestration.

The Azure AI Projects SDK enables developers to build and manage sophisticated, end-to-end AI applications directly on Microsoft Foundry. It provides comprehensive client operations for managing agents, datasets, model deployments, and indexes. Key features include integrating diverse tools (like code interpreters and file search) and handling complex conversational flows, making it ideal for building production-grade AI workflows.

The Azure AI Projects SDK enables developers to build and manage sophisticated, end-to-end AI applications directly on Microsoft Foundry. It provides comprehensive client operations for managing agents, datasets, model deployments, and indexes. Key features include integrating diverse tools (like code interpreters and file search) and handling complex conversational flows, making it ideal for building production-grade AI workflows.