Skill UI

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 comprehensive template provides a production-grade reference architecture for CodeRabbit AI code reviews. It is designed to guide teams in establishing rigorous coding standards by defining specific, best-practice instructions for various layers (API, Database, Authentication, Frontend, CI/CD, Infrastructure). Use this as a robust starting point to build a comprehensive, automated, and secure code quality review process.

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.

Orchestrates an exhaustive, multi-dimensional code review process by coordinating specialized agents (e.g., security, performance, quality, architecture). This workflow covers code quality, architectural integrity, security vulnerabilities (OWASP Top 10), performance bottlenecks, and test coverage, providing consolidated, actionable feedback and remediation guidance.

Provides expert guidance on designing, implementing, and optimizing cutting-edge computer vision pipelines. Expertise spans real-time object detection (YOLO26), advanced promptable segmentation (SAM 3), and leveraging Vision Language Models (VLMs) for complex visual reasoning. Ideal for building high-performance systems in robotics, autonomous vehicles, and industrial inspection, including 3D reconstruction and edge deployment optimization.

Create a new track with specification and phased implementation plan

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 provides a comprehensive, actionable overview of the Conductor project's status. It aggregates information from project plans, active tracks, and detailed specifications to display overall progress, identify next critical tasks, and flag potential blockers. Use it when you need a high-level dashboard view of project health and required actions.

This tool serves as an interactive, step-by-step installer for the Everything Claude Code (ECC) framework. It guides users in selecting and installing complex development skills (covering patterns for Django, Spring Boot, Python, Go, etc.) and standardized rulesets (e.g., common best practices, language-specific guides). It ensures the correct setup, validation, and optimization of the development environment for both user and project levels.

Deprecated routing skill that reroutes legacy "content creator" requests to content-production for writing/SEO/brand-voice needs or to content-strategy for planning, ensuring users land with the specialist best suited to their intent.

A sophisticated memory management system designed to recover, reconstruct, and maintain project context across complex, distributed, and multi-agent AI workflows. It specializes in advanced semantic memory rehydration, utilizing semantic vector search, relevance ranking, and conflict resolution to ensure seamless continuity and high fidelity of historical knowledge.

Use when working with context management context save