Skill UI

A comprehensive skill for conducting offensive security assessments across major cloud providers, including Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). It guides users through reconnaissance, authentication testing, resource enumeration, privilege escalation, and persistence techniques, strictly for authorized security engagements.

This skill serves as an expert guide for writing, reviewing, and optimizing AWS CloudFormation templates. It covers foundational best practices, including utilizing parameterized environments, implementing nested stacks, managing stateful resources with deletion policies, and ensuring production-grade infrastructure consistency. Use it when building robust and maintainable Infrastructure as Code (IaC) for AWS.

Expert guide for designing robust, scalable, and resilient backend systems. Covers modern patterns including Microservices, Event Sourcing, CQRS, and advanced API contracts (REST, GraphQL, gRPC). Specializes in defining service boundaries, implementing distributed transactions (Saga), and integrating comprehensive security measures like OAuth 2.0 and mTLS.

This skill covers the end-to-end development of cross-browser extensions (Chrome, Firefox). Expertise includes mastering Manifest V3, structuring complex architecture (service workers, content scripts), managing local/sync storage, building user interfaces, and implementing monetization models. Ideal for building robust, production-ready tools and navigating store publishing policies.

Provides comprehensive guidance for developing robust, cross-platform Command Line Interface (CLI) applications. Covers the entire workflow from initial UX analysis and command design to implementation using popular frameworks (e.g., commander, click, cobra). Key features include advanced argument parsing, handling shell completions, displaying progress bars, and ensuring reliable terminal interactions across Windows, macOS, and Linux.

This skill simulates a documentation expert, capable of generating comprehensive, maintainable technical documentation directly from source code. It analyzes codebases to produce various artifacts, including API documentation, system architecture diagrams, comprehensive user guides, and technical reference manuals. It is essential for standardizing documentation and building automated documentation pipelines in large, complex projects.

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.

This expert skill analyzes codebases to identify, quantify, and prioritize all forms of technical debt—including code complexity, architectural flaws, outdated dependencies, and testing gaps. It calculates the measurable impact of this debt on development velocity, bug rates, and overall business risk. The output provides a comprehensive, actionable, and ROI-driven roadmap for systematic remediation.

This comprehensive checklist provides a systematic framework for conducting thorough code reviews. It covers critical areas including functionality, security vulnerabilities, performance optimization, and overall code maintainability. Use it when reviewing pull requests, conducting code audits, or standardizing team development practices.

Learn how to integrate CodeRabbit as a mandatory Continuous Integration (CI) gate within your GitHub repository. This comprehensive guide demonstrates setting up branch protection rules, creating GitHub Actions workflows, and enforcing CodeRabbit's reviews to ensure high code quality before merging. Includes advanced setup for PR size checks and notification mechanisms.

This guide provides methods to implement robust data handling, secret detection, and compliance patterns within code reviews (e.g., GDPR, CCPA). It teaches users how to configure CodeRabbit to exclude sensitive files (like .env or keys), detect hardcoded secrets, limit the review scope to active source code, and ensure that PII is properly handled during the CI/CD process.

Monitor the effectiveness and adoption of AI code reviews using CodeRabbit. This skill calculates critical metrics including review coverage percentage, time-to-first-review, and comment acceptance rates. It is ideal for building organizational dashboards to maintain high code quality and optimize development workflows.