Skill UI

This comprehensive guide demonstrates how to integrate external applications with the Clay platform using webhooks. Users learn to send raw data (such as company domains or lead information) via API calls in multiple popular languages (cURL, Python, Node.js). Clay automatically processes this data through configured enrichment columns and returns enriched, structured results. This is essential for building robust data pipelines and testing webhook setups.

This guide details the complete setup process for integrating with the Clay data enrichment platform. It covers obtaining the Enterprise API key, configuring webhook endpoints for data ingestion, and connecting third-party data providers (like Apollo or Clearbit). Use this when onboarding, setting up automated data pipelines, or maximizing credit savings through programmatic integration.

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.

This comprehensive guide details how to configure robust CI/CD pipelines for applications secured by Clerk authentication. It covers setting up GitHub Actions workflows, utilizing Playwright for end-to-end (E2E) testing, managing test user states, and scripting user seeding for reliable automated testing environments. Essential for developers integrating Clerk into their build and deployment process.

A comprehensive guide for designing production-grade lead enrichment pipelines in Clay. Covers three integration patterns (Webhook, API), detailed table schema design, advanced data waterfall logic, implementing complex ICP scoring formulas, and setting up robust CRM synchronization flows for go-to-market data operations.

This skill simulates a documentation expert, capable of generating comprehensive, maintainable technical documentation directly from source code. It analyzes codebases to produce various artifacts, including API documentation, system architecture diagrams, comprehensive user guides, and technical reference manuals. It is essential for standardizing documentation and building automated documentation pipelines in large, complex projects.

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.

This comprehensive guide details how to implement robust webhook endpoints for CodeRabbit, an AI-powered code review tool. It covers essential security practices, including signature validation (HMAC-SHA256), and provides structured methods for handling various event types (e.g., PR reviews, check run completions, comments). Use this skill when integrating CodeRabbit into CI/CD pipelines to automate notifications, track metrics, and enforce quality gates.

A comprehensive pattern for building robust and cost-efficient LLM application pipelines. This guide details combining model routing based on task complexity, immutable cost tracking, limited retry logic, and prompt caching to manage API expenditures while maintaining high quality. Essential for production systems handling variable-complexity, high-volume tasks.

This skill provides a comprehensive pipeline for deploying Customer.io integrations to major production cloud platforms, including Google Cloud Run, Vercel, AWS Lambda, and Kubernetes. It details best practices for secure deployment using dedicated secrets managers, integrating robust health check endpoints, and implementing blue-green strategies, ensuring reliable and scalable CI/CD workflows.

A comprehensive reference architecture guide for implementing enterprise-grade integrations with Customer.io. It details best practices for building robust, scalable, and reliable systems, focusing on event-driven patterns, message queuing (e.g., Redis/Kafka), service layer separation, and idempotency. Ideal for designing complex data pipelines and ensuring high availability in mission-critical applications.

This comprehensive workflow guides the entire database lifecycle, from initial architecture design (covering both SQL and NoSQL) to advanced implementation, optimization, and maintenance. It covers crucial stages like schema design, implementing migrations, optimizing query performance, developing robust data pipelines (using tools like Airflow and dbt), and managing data quality and operations. Ideal for engineers building complex, production-grade data systems.