Skill UI

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.

Collects comprehensive, sanitized debug evidence for Deepgram support and troubleshooting. This bundle gathers detailed system environment information (Node.js, Python), performs live API connectivity tests, analyzes audio file metadata using ffprobe, and logs request/response data. Essential for preparing detailed support tickets and reproducing bugs related to Deepgram integration.

This guide provides minimal working examples for Deepgram speech-to-text transcription. It demonstrates how to transcribe audio from a URL or a local file using both TypeScript and Python. Users can learn basic API patterns, implement advanced features like diarization (speaker identification), and compare various model options (e.g., nova-3, nova-2). Ideal for initial integration and testing.

This guide establishes a robust local development environment for Deepgram integrations. It covers setting up test fixtures using sample audio, mocking API responses for isolated unit testing, configuring watch-mode development servers, and running comprehensive unit and integration tests using Vitest. Use this setup when initiating a Deepgram feature or requiring a reliable, fast development cycle.

This skill provides a comprehensive and isolated setup for managing Deepgram API integrations across different deployment stages (Development, Staging, Production). It includes typed configuration management, a client factory pattern, and Docker Compose profiles, ensuring that each environment uses specific credentials, models, and feature settings (like diarization or PII redaction) without conflict. Ideal for robust, multi-stage application deployment.

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.