Skill UI

This SDK provides comprehensive Python access to manage Microsoft Fabric capacities and resources within Azure. Developers can programmatically perform full lifecycle operations, including creating, reading, updating, deleting, suspending, and resuming Fabric capacities. It is essential for automating resource provisioning, monitoring capacity status, and optimizing cloud costs within the Azure ecosystem.

This SDK provides comprehensive Python access to manage Microsoft Fabric capacities and resources within Azure. Developers can programmatically perform full lifecycle operations, including creating, reading, updating, deleting, suspending, and resuming Fabric capacities. It is essential for automating resource provisioning, monitoring capacity status, and optimizing cloud costs within the Azure ecosystem.

This skill provides comprehensive scripts and workflows to automatically identify and remove unused AWS resources across multiple services (EBS, S3, EC2, EIPs). By automating the cleanup of orphaned volumes, old snapshots, and stale data, users can eliminate cloud waste, significantly optimizing their AWS expenditure and ensuring a cost-efficient infrastructure.

Cloud Architect Blueprint provides guidance for designing resilient AWS, Azure, and GCP topologies, planning secure migrations, tightening zero-trust controls, optimizing costs, and orchestrating disaster recovery across multi-region deployments.

This skill provides comprehensive guidance for optimizing costs within Guidewire Cloud environments. It covers crucial areas such as rightsizing cloud compute instances, optimizing batch process scheduling to off-peak hours, reducing API call expenses through effective caching, and implementing bulk endpoints instead of individual calls. It is essential for cloud architects, DevOps engineers, and development teams managing large-scale insurance SaaS platforms.

This skill provides a comprehensive decision framework and set of patterns for architects designing cloud-agnostic applications across major providers like AWS, Azure, and GCP. It details service comparisons (Compute, Storage, DB), multi-cloud patterns (e.g., Best-of-Breed, DR), cost optimization strategies, and structured migration plans, ensuring robust and resilient enterprise deployments.

This comprehensive guide provides a detailed framework for optimizing spending across Replit services. Learn how to conduct a thorough billing audit, right-size deployment resources (Autoscale vs. Reserved VM), manage data egress costs, optimize team seat licenses, and clean up idle resources. Essential for controlling cloud spending and ensuring efficient resource allocation in any development workflow.

SkyPilot orchestrates ML training, batch jobs, and serving across 20+ clouds with automatic cost-aware region selection, spot-instance recovery, managed clusters, and GPU-aware distributed training, letting you optimize spend and avoid vendor lock-in.

This guide provides comprehensive, actionable methods to audit and significantly reduce Supabase operational costs. It covers advanced database tuning (vacuuming, index cleanup, bloat management), efficient storage lifecycle management, connection pooling strategies, and optimal plan selection. Use this when analyzing high billing reports, identifying resource waste, or implementing robust cost-saving measures in a cloud environment.

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

This guide demonstrates how to implement robust Role-Based Access Control (RBAC) and financial governance for cloud GPU environments. It allows organizations to allocate resources, set team-specific budgets, whitelist approved GPUs, and track spending via detailed audit logs. Ideal for multi-team research and engineering environments requiring strict cost accountability and access separation.

This guide establishes a fast, reproducible local development workflow for GPU workloads using Vast.ai. It covers key practices such as testing Docker images locally (in CPU mode), mocking external APIs like Vast.ai, and implementing robust unit and integration tests. This is essential for minimizing cloud GPU costs and accelerating the iteration cycle for complex AI/ML projects.