Skill UI

A comprehensive, phased guide detailing how to migrate your code review process to CodeRabbit. It covers the entire journey, from assessing current tools (like SonarCloud, DeepSource, or manual reviews) and mapping existing rules, through to running a parallel pilot phase and finally achieving full adoption. Ideal for engineering managers or DevOps teams planning a major CI/CD tool transition.

Automates configuring and aggregating infrastructure metrics across compute, storage, network, containers, load balancers, and databases with Prometheus, Datadog, or CloudWatch for dashboards and capacity tracking.

Handles Confluence page search, creation, updates, space listings, and comments through Atlassian MCP tools; enforces Markdown bodies, natural-language Rovo Search, and Cloud ID/URL detection before any operation.

A comprehensive guide detailing systematic strategies and patterns for optimizing cloud spending across major platforms including AWS, Azure, and GCP. It covers best practices for rightsizing resources, leveraging reserved instances, implementing lifecycle policies, and setting up robust cost governance, ensuring cost efficiency without sacrificing performance or reliability.

Specializing in building high-performance, cloud-native applications using C# and the latest .NET 8+ framework. This skill covers end-to-end development, including building robust RESTful APIs (Minimal/Controller), implementing data access with Entity Framework Core, structuring code using CQRS/MediatR, and scaffolding Blazor components. Focuses on modern best practices like async/await, DTO mapping, and proper error handling.

This skill provides a comprehensive pipeline for deploying Customer.io integrations to major production cloud platforms, including Google Cloud Run, Vercel, AWS Lambda, and Kubernetes. It details best practices for secure deployment using dedicated secrets managers, integrating robust health check endpoints, and implementing blue-green strategies, ensuring reliable and scalable CI/CD workflows.

Expert in designing, building, and optimizing scalable data pipelines, modern data warehouses, and real-time streaming architectures. Mastery covers the complete modern data stack, including Spark, dbt, Airflow, and cloud services (AWS, Azure, GCP). Focuses on ELT/ETL, data lakehouse implementation (Delta Lake, Iceberg), data governance, and ensuring high data quality and operational reliability.

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

This skill provides expert guidance for modern, multi-cloud database administration. It covers comprehensive knowledge across AWS, Azure, and GCP platforms, spanning relational, NoSQL, and graph databases. Key areas include Infrastructure as Code (Terraform, Flyway), achieving high availability, disaster recovery, advanced performance optimization, and ensuring strict regulatory compliance (HIPAA, GDPR). It is essential for managing complex, highly reliable cloud data architectures.

This skill simulates a cloud cost optimization expert specializing in reducing infrastructure expenses across AWS, Azure, and GCP. It analyzes complex cloud spending patterns, identifies waste, overprovisioning, and potential savings opportunities. It provides actionable recommendations, implements cost controls, and proposes cost-effective, reliable architectures, ensuring cost efficiency without sacrificing performance or reliability.

This guide provides a comprehensive framework for minimizing cloud expenditure on Databricks. It covers identifying cost drivers via system billing tables, implementing cluster policies, utilizing spot instances for batch processing, right-sizing SQL warehouses, and automating the shutdown of idle development clusters. Use it when managing cloud spending or optimizing data platform usage.

Provides comprehensive instructions for deploying Deepgram transcription services into various production environments. This guide covers building production-ready Docker images, configuring Docker Compose, setting up robust Kubernetes manifests (including HPA and secrets), and implementing serverless handlers for platforms like AWS Lambda. Essential for scaling Deepgram services reliably and securely in the cloud.