Skill UI

A comprehensive guide to setting up a fast, isolated local development loop for data engineering projects using Databricks Connect v2, Asset Bundles, and standard IDEs (like VS Code). This workflow allows developers to run PySpark code locally for debugging and unit testing, while managing resource deployment and ensuring consistency across development, staging, and production environments.

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.

This specialized skill acts as an expert debugger, designed for deep root cause analysis of software errors, test failures, and unexpected behavior. It guides the user through systematic debugging processes, including capturing stack traces, isolating failure points, implementing minimal fixes, and providing comprehensive reports detailing the root cause, evidence, specific code patches, and prevention recommendations. Ideal for complex bug resolution and quality assurance.

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.

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.

A comprehensive checklist and guide for deploying Deepgram integrations into a production environment. It covers critical areas including robust authentication practices, system resilience design (e.g., backoff strategies, circuit breakers), performance optimization (singleton clients, audio preprocessing), security measures (PII redaction), and mandatory monitoring setup (Prometheus metrics and alert rules). Essential for any go-live phase.

Provides production-ready coding patterns for integrating Deepgram using TypeScript and Python. This guide covers essential best practices, including implementing singleton client management, performing text-to-speech synthesis with Aura, and setting up advanced audio intelligence pipelines (e.g., summarization, topic detection, sentiment analysis). Use this when establishing robust Deepgram integration standards, refactoring existing SDK usage, or migrating to newer SDK versions.

Automates dependency vulnerability checks within the Security Fundamentals skill set, guiding users through best practices, secure coding patterns, and remediation-ready output validation.

This skill teaches the critical principles and decision-making processes for safe production releases. It covers platform selection (e.g., Kubernetes, Vercel), mandatory pre-deployment verification (Code Quality, Environment), structured workflows (Prepare, Backup, Deploy, Verify), and advanced strategies like Blue-Green and Canary deployments. Focuses on thinking through risk mitigation, not just executing scripts.

This meta-skill coordinates the entire design lifecycle, ensuring that ideas are systematically refined through mandatory stages: brainstorming, risk assessment, and multi-agent review. It acts as a process gatekeeper, enforcing adherence to structured steps and mitigating risks before allowing implementation, thus preventing premature or flawed execution.

Detects memory usage issues by scanning code for common leak patterns, such as lingering listeners, closures holding references, or runaway caches, and provides recommendations to fix them before performance degrades.