Skill UI

Provides the Azure Resource Manager SDK for .NET to manage Application Insights resources. It enables comprehensive observability, allowing developers to programmatically create, configure, and manage performance monitoring components, API keys, and availability tests (WebTests) within Azure cloud environments.

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.

A comprehensive Python SDK for interacting with Azure Monitor services. It enables developers to execute complex Kusto Query Language (KQL) queries against Log Analytics workspaces and retrieve various resource metrics (CPU usage, network traffic, etc.). Ideal for building robust monitoring dashboards, automated alerting systems, and custom data analysis pipelines in cloud environments.

An expert resource for developing highly reusable, production-grade cloud infrastructure using AWS Cloud Development Kit (CDK) in languages like TypeScript, Python, or Java. This guide provides established patterns for common services (e.g., Serverless APIs, ECS, Data Pipelines), focusing on best practices such as implementing least privilege, separating stateful resources, and ensuring monitoring via CloudWatch.

Guides GCP cloud monitoring alert tasks by detailing steps, configs, production-ready code, and validation whenever alert-related requests arise for compute, storage, BigQuery, Vertex AI, or native services.

Automate Cloudinary media workflows—folder creation, upload preset configuration, asset lookup, transformation generation, usage monitoring, and webhook setup—via natural-language commands routed through the Rube MCP server.

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

Sets up production-grade Genkit infrastructure via Terraform, covering Firebase Functions, Cloud Run, GKE, AI monitoring, and CI/CD wiring so deployments run securely at scale.

This comprehensive checklist verifies the readiness of Guidewire Cloud for production deployment. It covers critical steps including Gosu compilation validation, GUnit test execution, API role configuration, integration gateway endpoint testing, batch process scheduling verification, and monitoring alert setup. It guides the promotion of configurations through dev, staging, and production environments via GCC.

This comprehensive guide provides step-by-step instructions for integrating Langfuse LLM observability into various cloud and serverless environments. It covers best practices for deployment on platforms such as Vercel, AWS Lambda, Google Cloud Run, and self-hosted Docker, ensuring robust tracing and monitoring of entire AI workflows.

Orchestrate PhantomBuster cloud agents through the Composio MCP integration, automating lead generation, web scraping, usage monitoring, container tracking, CSV exports, and hCaptcha solving in a single workflow.