Skill UI

A comprehensive guide to building resilient and fault-tolerant data enrichment pipelines. Learn to implement critical patterns like credit budget circuit breakers, Dead Letter Queues (DLQs) for failed records, and health monitoring to ensure stability and cost control when integrating with external services like Clay.

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.

Systematic code maturity assessment using Trail of Bits' 9-category framework, analyzing arithmetic safety, access controls, documentation, MEV risks, testing, and more to deliver evidence-backed scorecards and actionable recommendations.

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 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.

Manages CodeRabbit's enterprise access control, seat assignment, and organization-wide policies. This is crucial for large organizations needing centralized control over who receives AI code reviews, defining global coding standards via YAML, and implementing granular Role-Based Access Control (RBAC) across multiple repositories and teams. Use when scaling AI code quality checks across multiple departments.

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.

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.

This skill provides a comprehensive guide to configuring CodeRabbit for advanced, security-focused code reviews. It teaches how to implement rules for detecting hardcoded secrets, common vulnerabilities (like SQL injection and XSS), and enforcing secure coding standards across APIs, databases, and infrastructure code. Use this when hardening a codebase's security posture or ensuring compliance with industry standards.

Provides universal coding standards and best practices for TypeScript, JavaScript, React, and Node.js projects, covering naming, immutability, async handling, component design, API structure, and validation to keep teams aligned.

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.