Skill UI

This guide provides comprehensive strategies for scaling Customer.io integrations to handle high volumes of daily events. It covers implementing k6 load tests for performance benchmarking and outlines architectural patterns—from simple Direct APIs to complex Redis/Kafka queue-backed systems—ensuring reliability and managing API rate limits for massive data throughput.

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.

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.

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.

Implements robust mechanisms for handling API rate limits (e.g., 429 errors) when interacting with services like Customer.io. It utilizes the Token Bucket algorithm for precise throttling and combines it with exponential backoff, jitter, and queue processing (p-queue) to ensure high reliability and prevent API overloads.

This comprehensive guide details essential security best practices for integrating with Customer.io. It covers secure credential storage using secrets managers, sanitizing Personally Identifiable Information (PII) via hashing, verifying webhook signatures (HMAC-SHA256) to ensure data integrity, and establishing procedures for API key rotation to maintain compliance with GDPR/CCPA regulations.

A comprehensive guide for safely upgrading and migrating the Customer.io Node SDK. It details the necessary steps for handling major version changes, such as transitioning from the legacy CustomerIO class to the modern TrackClient and APIClient pattern. Use this skill when refactoring old code, adapting to breaking API changes, or ensuring compatibility with the latest SDK best practices.

A comprehensive guide and implementation pattern for managing rate limits, throttling, and ensuring idempotency when interacting with the Databricks API. This skill demonstrates techniques like exponential backoff with jitter, token-bucket rate limiting, and using idempotency tokens to make bulk and concurrent API calls reliable and efficient, minimizing HTTP 429 errors.

This comprehensive guide sets up a robust CI/CD pipeline for Deepgram integrations using GitHub Actions. It covers unit tests (mocked SDK), full integration tests against the live Deepgram API (including transcription, diarization, and TTS), and smoke tests. Use this when establishing automated testing, ensuring deployment stability, or implementing continuous delivery workflows for Deepgram-powered applications.

A comprehensive guide for diagnosing and fixing common Deepgram API errors. Covers HTTP error codes, WebSocket issues, optimal audio preparation using ffmpeg, and implementing robust retry patterns for reliable Speech-to-Text integration.

A comprehensive workflow for production-grade, pre-recorded Speech-to-Text (STT) transcription using the Deepgram API. This skill supports transcribing audio from URLs or local files, offering advanced features such as speaker diarization, automatic summaries, sentiment analysis, and topic detection. It also includes robust batch processing methods for handling large datasets.

This skill provides comprehensive methods to optimize Deepgram API usage and manage costs. It implements budget-aware transcription pipelines that preemptively check projected spending against set limits, utilizes audio preprocessing techniques (like silence removal via ffmpeg) to minimize billable minutes, and includes API calls to monitor current usage and spending against defined budgets. Ideal for cost-sensitive, production-grade deployments.