Skill UI

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.

A comprehensive expert system for analyzing software codebases to identify, quantify, and prioritize technical debt across various dimensions (code, architecture, testing, infrastructure). It provides detailed metrics, impact assessments (development velocity, quality loss), and actionable, prioritized remediation roadmaps with clear ROI projections.

A comprehensive troubleshooting guide for diagnosing and fixing common operational and configuration issues with the CodeRabbit PR review bot. Use this guide when reviews fail to post, scope is incorrect, comments are excessive, or configurations are ignored. It covers steps for checking GitHub permissions, YAML syntax, and advanced bot interaction commands.

This skill guides users on fine-tuning CodeRabbit's review quality. It teaches the AI agent team preferences through PR feedback, integrates custom code guidelines (e.g., security policies), allows customization of the review tone, and implements path filters to drastically reduce false positives and noise. This ensures that all code feedback is highly relevant, actionable, and aligns with specific team standards.

A comprehensive guide to optimizing costs associated with AI code review tools like CodeRabbit. Learn strategies for right-sizing seat allocation, scoping services to high-value repositories, and implementing granular file filters to minimize unnecessary AI processing and billing overhead. Essential for managing SaaS spend in a DevOps environment.

This guide provides a step-by-step walkthrough for setting up automated AI code reviews using CodeRabbit. It covers creating the necessary `.coderabbit.yaml` configuration file, defining path-specific instructions, triggering reviews via Pull Requests (PRs), and interacting with the feedback using PR comments or the local CLI. Essential for integrating quality gates into CI/CD workflows.

This pattern demonstrates how to automate workflows around CodeRabbit reviews. By utilizing GitHub API, TypeScript, and Bash scripting, users can programmatically fetch review results, extract detailed line-level comments, trigger specific CodeRabbit actions (like 'summary' or 'resolve'), and implement CI/CD gates to enforce quality standards before merging.

This skill provides professional, AI-powered code review, integrated with Codex AI. It is designed to ensure high code quality before commits, automatically generate comprehensive CHANGELOGs, and assist in managing large-scale refactoring projects efficiently.

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.

This skill provides a structured, git-aware mechanism to revert changes associated with specific logical work units (tracks, phases, or tasks). It performs comprehensive pre-flight checks, identifies relevant commits through structured logging, displays a detailed execution plan, and executes the revert safely in reverse chronological order. Ideal for undoing complex, multi-step development changes while maintaining project integrity.

This skill is designed to configure a Rails project workspace to function correctly within Conductor, a platform for parallel coding agents. It sets up the necessary scaffolding, including `conductor.json`, executable setup scripts, and server scripts. Additionally, it updates critical Rails configuration files (such as Sidekiq, Cable, and development environment settings) to ensure proper environment variable handling, Redis isolation, and port management for reliable parallel agent execution.

Automates configuration reference generation within technical documentation, offering guidance, best practices, and production-ready code while validating outputs against standards to support API docs, user guides, architecture docs, and automation workflows.