Skill UI

Guides GCP users through automating Cloud Scheduler job creation with best practices, ready-to-run code, and validation, ideal for implementing cron-driven workloads across compute, storage, BigQuery, Vertex AI, and Cloud Run.

Automated Cloud SQL setup guidance that activates within GCP Skills and delivers step-by-step configuration, production-ready code, and validation against best practices for compute, storage, BigQuery, Vertex AI, and other GCP services.

Auto-activating AWS skill that guides users through CloudFormation template creation, covering compute, storage, networking, and serverless best practices with validation-ready output.

Automates AWS CloudFront distribution setup with step-by-step guidance, production-ready configurations, and validation against best practices for compute, storage, networking, and serverless workloads.

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

Automates CloudWatch alarm setup by providing best-practice guidance, sample configurations, and validation for AWS compute, storage, networking, and serverless workloads.

A specialist tool designed to generate, format, and validate comprehensive technical documentation. It supports multiple standards, including Google/NumPy docstrings (for Python), JSDoc (for TypeScript), and full OpenAPI/Swagger specifications. Key features include automated detection of code languages and frameworks, robust validation of code examples (using tools like doctest and OpenAPI linters), and generating structured user guides or site configurations.

Automates code injection detection guidance as part of Security Fundamentals, offering step-by-step advice, best-practice patterns, and production-ready snippets for authentication, input validation, and secure coding scenarios.

Automates code of conduct documentation by guiding through generator patterns, setup instructions, and validation checks tailored for technical documentation workflows.

An expert AI specialist that integrates advanced static analysis (e.g., CodeQL, SonarQube) with modern DevOps practices. It performs multi-layered code reviews, checking for security vulnerabilities, performance bottlenecks, architectural flaws, and adherence to best practices across various languages. Ideal for CI/CD pipelines and pull request validation.

This skill executes CodeRabbit's core workflow, providing automated, in-depth code reviews directly on pull requests. It analyzes code diffs based on custom rules (e.g., API validation, DB security), generates detailed walkthrough summaries, and posts line-level feedback. Ideal for setting up robust CI/CD quality gates and maintaining high code standards throughout the development lifecycle.

This comprehensive checklist guides users through the process of elevating CodeRabbit from an optional AI review tool to a mandatory merge gate in a production environment. It covers configuration auditing, technical validation (YAML syntax, review coverage), team onboarding, and setting up branch protection rules to ensure a smooth transition to required status.