Skill UI

This meta-skill is designed to guide and execute structured developmental workflows, enabling continuous skill improvement and knowledge transfer. It is applicable when a task requires following a defined developmental process. Users must ensure all necessary inputs, permissions, and success criteria are provided, and must treat the output as a guide, supplementing professional validation.

This skill is designed to execute complex, structured workflows and actions based on a defined overview. It facilitates systematic process completion and adherence to scope. Users must ensure the task precisely matches the skill's scope, and remember that the output is not a substitute for manual validation or expert review.

A comprehensive Python SDK for interacting with Azure Queue Storage. This tool enables reliable, asynchronous communication patterns by managing message queuing, task distribution, and data processing workflows. It supports core operations including sending, receiving, peeking, updating, and deleting messages, making it ideal for building resilient, decoupled backend systems on Azure.

A comprehensive guide to essential Bash and Linux command patterns for macOS and Linux systems. Covers fundamental scripting concepts, including command chaining (&&, ||), file operations (find, grep, sed, awk), process management, text processing, environment variable handling, and network utilities (curl). Ideal for automating system tasks and improving command-line efficiency.

Master essential practices for writing robust, secure, and portable Bash scripts suitable for production environments, CI/CD pipelines, and critical system automation. This guide covers strict error handling, safe argument parsing, input validation, process orchestration, and cross-platform compatibility, ensuring scripts are resilient to unexpected inputs and failures.

A comprehensive guide and set of patterns for optimizing build processes using Bazel in large-scale monorepositories. Covers configuring WORKSPACE, managing remote caching/execution, defining custom rules (Python, TypeScript), and best practices for maintaining build performance and consistency across massive codebases.

A comprehensive guide to implementing reliable, scalable, and resilient background job processing in Node.js/TypeScript using BullMQ and Redis. Covers advanced patterns such as delayed jobs, repeatable scheduling, job dependencies (flows), rate limiting, robust error handling, and graceful worker shutdown, ensuring production-grade async execution.

This guide details setting up robust CI/CD pipelines for applications utilizing Clay. It covers automated unit and integration testing for webhook handlers, validating data schema compliance, and ensuring the reliability of data enrichment processes within your build workflow. Ideal for teams deploying webhooks or complex data transformation logic.

This guide provides comprehensive instructions for deploying applications that interact with Clay—such as webhook receivers, enrichment processors, or CRM sync services—to major production infrastructure platforms (Vercel, Google Cloud Run, or Docker). It covers environment configuration, secret management, and setting up secure HTTPS endpoints for robust production deployment.

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 and code implementation for scaling data enrichment pipelines to handle high volumes (10K-100K+ leads monthly). It covers capacity planning, optimizing batch processing using queues (e.g., BullMQ/Redis), managing rate limits, and designing multi-table architectures for robust, large-scale data ingestion.