Skill UI

This comprehensive checklist guides the deployment of Databricks data pipelines and jobs to a production environment. It covers critical stages including pre-deployment security hardening (Service Principals, Unity Catalog), validating infrastructure (cluster types, autoscaling), ensuring code quality (unit/integration tests, Delta Lake best practices), executing controlled deployment, and performing post-deployment health checks and monitoring setup. Ideal for CI/CD pipelines and go-live procedures.

This skill guides the definition of relationships and integration contracts between distinct bounded contexts within a complex system using Domain-Driven Design (DDD) patterns. It is crucial for preventing domain leakage, planning anti-corruption layers (ACLs), and clarifying ownership boundaries when multiple services interact, ensuring a robust and scalable system architecture.

This skill guides the architectural definition of complex business domains using Domain-Driven Design (DDD). It helps in defining core subdomains, separating services by bounded contexts, and establishing a unified ubiquitous language. It is essential for decomposing monoliths, structuring microservices, and aligning team ownership with clear domain boundaries.

Guides diagnosing Buttercup CRS Kubernetes issues across the crs namespace by triaging pod states, analyzing logs, checking resource pressure, monitoring Redis, and inspecting queues for cascading failures in services such as redis, build-bot, scheduler, and UI.

This guide demonstrates how to implement robust, real-time, low-latency streaming transcription using Deepgram's WebSocket API. It covers advanced audio handling, including microphone input capture (via Sox), speaker diarization, utterance boundary detection, and implementing automatic reconnection logic. Essential for building professional voice AI applications, live captioning services, or voice interfaces.

Provides comprehensive instructions for deploying Deepgram transcription services into various production environments. This guide covers building production-ready Docker images, configuring Docker Compose, setting up robust Kubernetes manifests (including HPA and secrets), and implementing serverless handlers for platforms like AWS Lambda. Essential for scaling Deepgram services reliably and securely in the cloud.

A comprehensive runbook for standardizing incident response to Deepgram production issues. It guides users through automated triage (checking status, API connectivity, and model performance), severity classification (SEV1-SEV4), and implementing mitigation strategies, including building fallback services and replaying failed requests to ensure business continuity during outages or degradation.

This guide offers a comprehensive deep dive into migrating existing voice transcription workflows from multiple major providers, including AWS Transcribe, Google Cloud STT, OpenAI Whisper, and others, to Deepgram. It details feature mapping, best practices, and implements an adapter pattern, enabling seamless, reliable, and validated service switching.

This utility provides comprehensive patterns for reliably integrating with the Deepgram API. It implements concurrency-aware rate limiting (using p-limit), exponential backoff with jitter for retries, and a circuit breaker pattern. Use this when building high-volume, mission-critical applications that must handle API quotas, 429 rate limit errors, and temporary service disruptions gracefully.

Guides REST and GraphQL API creation with endpoint planning, versioning, auth, rate limits, error handling, pagination, and OpenAPI docs to keep designs consistent when building or reviewing services.

This skill acts as an expert guide for setting up comprehensive debugging environments, implementing distributed tracing solutions, and establishing robust diagnostic workflows. It is ideal for diagnosing complex, multi-service issues in development or production environments by standardizing logging, tracking correlation IDs, and ensuring end-to-end observability.

This skill provides comprehensive guidance on implementing distributed tracing using industry standards like OpenTelemetry and tools such as Jaeger/Tempo. It enables developers to monitor the end-to-end lifecycle of a request across multiple microservices. Use this skill to debug latency issues, visualize complex service dependencies, pinpoint performance bottlenecks, and accurately trace error propagation in highly distributed systems.