Skill UI

This skill provides a comprehensive framework for mastering microservices architecture patterns. It covers critical aspects of building robust, distributed systems, including defining service boundaries (Domain-Driven Design), managing inter-service communication, handling distributed data transactions, and implementing various resilience patterns (e.g., circuit breaker, circuit breaker). It is essential for large-scale system decomposition.

Delivers end-to-end monitoring setups—structured logging, Prometheus/Grafana dashboards, alerting rules, distributed tracing, and load/profile testing—so teams can observe services, debug production issues, and plan capacity.

Expert guidance on designing highly scalable, distributed NoSQL databases, specifically covering Apache Cassandra and Amazon DynamoDB. This skill emphasizes the 'Query-First' modeling approach, teaching best practices like partitioning, denormalization, single-table design, and avoiding common anti-patterns (e.g., hot partitions, joins) to ensure optimal read/write performance at massive scale.

Expert skill in designing and implementing comprehensive observability stacks for enterprise microservices. Covers advanced monitoring (Prometheus, Grafana), distributed tracing (Jaeger, OpenTelemetry), centralized logging (ELK, Splunk), and defining SLIs/SLOs. Ideal for ensuring high availability, mastering incident response, and performing reliability engineering on large-scale, distributed applications.

This skill acts as an expert guide for implementing comprehensive monitoring and observability solutions. It covers setting up the three pillars of observability—metrics, distributed tracing, and log aggregation—along with defining monitoring architecture, creating actionable dashboards, and establishing effective alerting runbooks. Use it to achieve full visibility into system health, reduce MTTR, and ensure proactive issue detection.

OSS-Fuzz gives open-source teams free distributed infrastructure for continuous fuzz testing, letting you build Docker images, compile sanitizing fuzzers, run harnesses, and review coverage/bugs through the helper.py CLI instead of hosting your own servers.

This skill simulates an expert performance engineer capable of diagnosing complex bottlenecks across the entire technology stack. It covers modern observability techniques (OpenTelemetry, APM), advanced profiling (CPU, Memory, I/O), and sophisticated load testing strategies (k6, Chaos Engineering). Use this when optimizing scalability, reducing latency, improving throughput, or architecting highly reliable, performant distributed systems in cloud environments.

Guides adding PyTorch FSDP2’s fully_shard integration to training loops, covering correct distributed init, bottom-up sharding, mixed precision/offload policies, DTensor-aware optimizers, and distributed checkpointing so oversized models or DeviceMesh sharding goals work reliably.

High-level PyTorch Lightning streamlines training loops with automatic distributed support (DDP/FSDP/DeepSpeed), callbacks, checkpointing, logging, and device management so you can scale from a laptop to multi-node clusters without boilerplate.

This skill provides a comprehensive framework for implementing robust API rate limiting. It supports advanced algorithms like sliding windows, token buckets, and fixed counters, backed by Redis for distributed state. Use it to protect endpoints from excessive requests, enforce tiered quotas (Free, Pro, Enterprise), and prevent abuse like brute-force attacks by setting per-user, per-endpoint, or per-API-key limits.

Provides reusable Redis patterns for caching, rate limiting, pub/sub, streams, Lua scripts, and distributed locking to build scalable, resilient data services.

Execute controlled chaos experiments to proactively test system resilience, fault tolerance, and recovery capabilities. This toolkit simulates real-world failures, such as network latency, service crashes, resource exhaustion, and dependency outages, ensuring that microservices degrade gracefully and recover automatically. It is essential for modern distributed systems validation.