Skill UI

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 application of Domain-Driven Design (DDD) tactical patterns, including defining aggregates, modeling value objects, managing repositories, and utilizing domain events. It helps developers translate complex domain rules into clean, robust, and behavior-rich code structures, ensuring invariants are explicitly enforced.

Expert in event sourcing, CQRS, and event-driven architecture patterns. Masters event store design, projection building, saga orchestration, and eventual consistency patterns. Use for systems requiring audit trails, complex domain modeling, or temporal queries.

Learn to build and deploy event-driven serverless applications using Google Cloud Functions Gen1 and Gen2. This skill covers configuring triggers for Pub/Sub, Cloud Storage, and Eventarc, as well as managing deployments via gcloud CLI. It includes examples for Python and Node.js runtimes, handling HTTP requests, scheduled tasks, and data processing pipelines without maintaining servers. Ideal for implementing serverless workloads on GCP.

This skill enables building production-ready serverless applications on Google Cloud Platform using Cloud Run. It covers containerized services, event-driven functions, cold start optimization, and Pub/Sub integration. Includes guidance on memory management, concurrency settings, and scaling bottlenecks to ensure efficient deployment and cost control.

Master Inngest for building serverless-first background jobs and event-driven workflows without managing queues or workers. Learn durable execution, automatic retries, concurrency control, and idempotency patterns. Ideal for Next.js, Express, and serverless platforms like Vercel and Cloudflare Workers.

This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

Analyzes options chain data by separating real calls and puts from lottery-like speculative contracts to produce more reliable put/call sentiment, anomaly detection, and expiry breakdowns for watchlists, sectors, and event-driven names.