Skill UI

A comprehensive guide for setting up and maintaining Cursor IDE for enterprise and production use. It covers essential best practices including security hardening, defining project-wide coding rules, optimizing codebase indexing, ensuring data privacy compliance, and standardizing team development workflows. Essential for adopting AI coding tools in a professional environment.

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.

Guides ML training teams through data augmentation pipelines, offering best practices, code/config generation, validation, and experiment-tracking support so pipelines stay production-ready.

A comprehensive, end-to-end workflow for building product features guided by rigorous data insights. This skill guides users through the entire lifecycle, encompassing exploratory data analysis, forming measurable business hypotheses, designing statistical A/B tests, architecting data pipelines, and implementing robust backend/frontend instrumentation. It ensures that every feature iteration is validated by data and measured for impact.

Offers automated guidance for implementing data lineage trackers across ETL and streaming pipelines, including best practices on transformations, orchestration, and validation for production environments.

Automated guidance for data normalization tool tasks in ML training, offering best practices, production-ready code/configuration generation, and output validation.

Automated Data Quality Checker offers guidance, best practices, and production-ready code for validating pipelines whenever you work with ETL, streaming, or data transformation scenarios.