Skill UI

This skill provides implementation patterns and code examples for creating an incident runbook using Retell AI. It is specifically designed for automating complex workflows, handling emergency notifications, and managing interactive phone calls within a voice agent or telephony system environment.

This skill provides implementation patterns for setting up and managing a multi-environment connection with Retell AI. It is essential for developers building advanced AI voice agents, integrating complex phone call automation, or utilizing robust telephony workflows. It guides users through SDK usage and handles common API error scenarios.

This guide provides a comprehensive production checklist and SDK pattern for integrating Retell AI into voice agents and telephony systems. It covers the necessary steps, error handling, and implementation patterns for deploying reliable, AI-powered phone call automation and voice interactions in a production environment.

Provides a comprehensive guide and framework for executing end-to-end (E2E) tests. It simulates real user interactions across the entire application stack, covering browser UI, API calls, and database operations. The guide details best practices like implementing the Page Object Model (POM), configuring multi-browser environments (Playwright, Cypress, Selenium), and establishing robust error handling mechanisms to ensure high application stability and quality.

Automates environment prep, service orchestration, and cleanup for executing integration test suites, covering database setup, migrations, and optional coverage reports.

Guides authorized practitioners through Nmap, Masscan, Nessus, OpenVAS, Burp Suite, and OWASP ZAP for network discovery, vulnerability assessment, web application testing, wireless security, and compliance reporting across diverse environments.

A comprehensive fullstack development toolkit designed to accelerate project setup and maintain code health. It provides project scaffolding for popular stacks (Next.js, FastAPI, MERN, Django), generating complete boilerplate code, Docker configurations, and environment templates. Furthermore, the Code Quality Analyzer audits existing codebases for critical security vulnerabilities, complexity metrics, and dependency issues, ensuring high standards throughout the development lifecycle. Ideal for new project initiation and routine code auditing.

Guides production ML engineering with patterns for deploying models, building MLOps pipelines, monitoring drift, integrating LLMs and RAG systems, and enforcing feature-store, retraining, and cost controls in live environments.

A comprehensive guide to integrating Sentry into continuous integration and continuous deployment (CI/CD) pipelines (e.g., GitHub Actions, GitLab CI, CircleCI). This skill automates complex tasks such as creating releases, uploading source maps for readable stack traces, associating commits, and tracking deployment environments, ensuring rapid and accurate debugging of production errors.

This guide provides comprehensive steps to diagnose and fix the most frequent Sentry SDK integration issues. It covers critical topics such as DSN validation, missing event capture, source map failures, rate limiting, and proper flushing patterns in Node.js, Python, and serverless environments. Use this when your error monitoring system is not working as expected.

A comprehensive collection of diagnostic scripts designed for troubleshooting Sentry integration failures. This bundle collects SDK versions, runtime configuration details (e.g., DSN, environment), and performs network connectivity checks to the Sentry ingest endpoint. Use it when events are not appearing in Sentry, or when systematically diagnosing complex setup issues across Node.js and Python environments.

This skill provides a comprehensive walkthrough for setting up and testing Sentry error monitoring using both Node.js and Python SDKs. It demonstrates how to capture various event types, including simple messages and full exceptions, and crucially, how to enrich these events with user context, searchable tags, and detailed breadcrumbs. This is essential for verifying robust error logging and monitoring in a production environment.