Skill UI

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.

Provides a comprehensive workflow for developing against Customer.io in local or staging environments. It implements environment isolation, dry-run clients, and service mocks to ensure development work does not affect production data, making unit and integration testing safe and reliable.

This skill provides a comprehensive solution for implementing observability for Customer.io integrations. It covers setting up Prometheus metrics (latency, request counts, bounces), structured JSON logging with PII redaction, and OpenTelemetry tracing. Use it when building robust, production-grade monitoring dashboards and alerting systems for Customer.io APIs.

A comprehensive, step-by-step checklist and audit script designed for validating Customer.io integrations before going live. It covers credential verification, email deliverability setup (SPF, DKIM, DMARC), API connectivity smoke tests, campaign readiness, and setting up operational monitoring alerts for a smooth, reliable production launch.

This guide provides technical patterns for building fault-tolerant integrations with Customer.io. Learn to implement critical resilience techniques, including Circuit Breakers to prevent cascading failures, Retry with Jitter for handling transient errors, and Fallback Queues (using Redis) to ensure data continuity during service outages. Essential for robust, production-grade applications.

This guide details production-ready patterns for integrating the Customer.io SDK (customerio-node). It covers best practices including building type-safe clients, implementing robust retry logic with exponential backoff for network stability, performing efficient event batching for high-volume data streams, and managing singleton lifecycles. Use these patterns to build scalable, reliable, and maintainable user behavior tracking systems.

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.

A comprehensive guide covering best practices for database migrations. It addresses schema changes, data migrations, and rollbacks for major databases (PostgreSQL, MySQL) and modern ORMs (Prisma, Drizzle, etc.). Learn how to implement safe, reversible, and zero-downtime schema updates for production environments.

Specializes in generating production-grade SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, and SQL Server. Provides comprehensive plans covering data integrity, detailed rollback procedures, performance optimization, and validation checks for critical schema changes.

This comprehensive guide outlines the end-to-end process of building production-grade ETL data pipelines using Databricks and Delta Lake. It implements the robust Medallion Architecture (Bronze, Silver, Gold) for scalable data management. Techniques covered include Auto Loader for incremental streaming ingestion, MERGE INTO for upsert semantics, data cleansing, and advanced aggregation into analytics-ready layers. Ideal for establishing reliable data pipelines in a data lakehouse environment.

A comprehensive guide to setting up a fast, isolated local development loop for data engineering projects using Databricks Connect v2, Asset Bundles, and standard IDEs (like VS Code). This workflow allows developers to run PySpark code locally for debugging and unit testing, while managing resource deployment and ensuring consistency across development, staging, and production environments.

This skill guides the setup and management of Databricks resources across development, staging, and production environments. It covers best practices for using isolated workspaces, managing environment-specific secrets, implementing Asset Bundles, and integrating with CI/CD pipelines to ensure consistent and reliable data deployments.