Skill UI

Automate customer engagement workflows including broadcast triggers, message analytics, segment management, and newsletter tracking through Customer.io via Composio

A comprehensive guide for diagnosing and fixing common integration errors with Customer.io. It covers critical topics such as API status codes (400, 401, 429), authentication failures, incorrect timestamp formatting, and best practices for event tracking and transactional message delivery. Use this resource when troubleshooting campaign failures or API calls failing.

This comprehensive guide demonstrates the essential workflow for integrating with Customer.io using its SDK. It covers the core functions required for user lifecycle management: identifying and updating user profiles, tracking custom events (both authenticated and anonymous), and sending transactional emails. Ideal for developers performing quick customer journey mapping or initial platform setup.

This comprehensive guide details how to install and configure the Customer.io SDK for programmatic integration in Node.js projects. It covers the critical setup of authentication using two distinct API keys: the Track API Key (for user identification and event tracking) and the App API Key (for sending transactional messages and broadcasts). Essential for developers integrating Customer.io's core customer data and marketing features.

This comprehensive guide identifies and explains the most common anti-patterns and mistakes when integrating with Customer.io. It covers crucial development topics, including proper API key usage, Unix timestamp formatting, ensuring correct event tracking order (Identify before Track), and optimizing API calls for performance. Use this as a definitive checklist for code review and developer onboarding.

This skill provides advanced engineering patterns to dramatically improve the performance and reliability of Customer.io API integrations. It implements critical optimizations such as HTTP connection pooling, LRU deduplication caching, parallel batch processing, and fire-and-forget async tracking. Use this when integrating Customer.io into high-throughput, mission-critical systems to reduce latency from seconds to milliseconds.

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 protocol that provides LLM agents with persistent structural memory of a codebase. It maps the project as a directed graph, tracking not only dependencies (what imports what) but also the 'reasons' (why it connects). This allows agents to understand context, perform impact analysis, and navigate large repositories without loading the entire source code into the context window.

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

Creates step-by-step decomposition plans and migration roadmaps to guide the transition from monolithic applications to distributed architectures, prioritizing risk, value, and dependencies while tracking progress through pattern-based phases.

Implements a comprehensive observability stack for Deepgram integrations. It utilizes Prometheus metrics for tracking request rates, latency, and costs, OpenTelemetry for distributed tracing, Pino for structured logging, and AlertManager rules for proactive alerting. Essential for production monitoring, dashboard setup, and performance analysis of Deepgram API usage.