Skill UI

This skill guides the comprehensive, multi-step rollout of CodeRabbit AI code review across an entire organization. It covers setting organization-level defaults, customizing team-specific review rules (e.g., API security, frontend performance), and automating deployment via multi-repo scripts and branch protection enforcement. Use when establishing standardized, high-quality AI code review practices company-wide.

Configure CodeRabbit CLI to integrate AI-powered code reviews directly into your local development loop. This workflow allows developers to run comprehensive, AI-driven reviews against staged or unstaged changes before committing or pushing to a remote repository. It establishes a crucial 'two-layer' review process, providing instant, fast feedback at the local CLI level, complementing the formal PR review process. This tool is essential for maintaining high code quality and consistency during rapid development cycles.

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.

Analyzes namespaces to spot root-level orphaned classes, highlights hierarchy violations, and produces flattening or refactoring plans that keep components at leaf-level directories.

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.

Provides expert-level tooling and workflows for Atlassian Confluence. Capabilities include designing comprehensive documentation architectures, setting up permission hierarchies, creating standardized page templates, and managing content governance. Ideal for restructuring knowledge bases, standardizing documentation practices, or integrating Jira reports into spaces.

Detects operations in cryptographic code that leak secrets via timing side channels, guiding developers through multi-language analysis and CLI flags for warnings, filtering, architecture, and optimization levels.

A comprehensive guide for setting up Single Sign-On (SSO) for Cursor IDE using industry standards like SAML 2.0 and OpenID Connect (OIDC). This guide supports major Identity Providers, including Okta, Microsoft Entra ID, and Google Workspace. It covers configuration steps, attribute mapping, SCIM provisioning, and best practices for enterprise-level authentication and user lifecycle management.

A comprehensive, expert-level skill for designing, analyzing, and optimizing robust database schemas. It covers normalization, index tuning, choosing between SQL/NoSQL, and implementing zero-downtime data migrations. Provides systematic workflows for schema analysis, performance modeling, and advanced SQL pattern generation.

A comprehensive guide for establishing robust, enterprise-grade access control within Databricks. Covers implementing Single Sign-On (SSO) via SCIM, leveraging Unity Catalog for fine-grained Role-Based Access Control (RBAC), defining cluster policies, and enforcing data governance measures like Row-Level Security (RLS) and column masking. Essential for large-scale, secure data platforms.

This guide outlines a production-ready lakehouse architecture using Unity Catalog, Delta Lake, and the Medallion pattern. It provides best practices for organizing Databricks workspaces, implementing three-level governance, defining compute strategies, and adopting CI/CD best practices with Asset Bundles for robust team collaboration.