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Found 53 Skills
Constructs secure, efficient CI/CD pipelines with supply chain security (SLSA), monorepo optimization, caching strategies, and parallelization patterns for GitHub Actions, GitLab CI, and Argo Workflows. Use when setting up automated testing, building, or deployment workflows.
Optimize Guidewire InsuranceSuite performance including query optimization, batch processing, caching, and JVM tuning. Trigger with phrases like "guidewire performance", "slow queries", "optimize policycenter", "batch processing", "query tuning".
Progressive Web Apps - service workers, caching strategies, offline, Workbox
Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance monitoring. Handles end-to-end optimization, real user monitoring, and scalability patterns. Use PROACTIVELY for performance optimization, observability, or scalability challenges.
Expert backend development covering API design, database architecture, microservices, message queues, caching, and system scalability.
Service Worker API implementation guide — registration, lifecycle management, caching strategies, push notifications, and background sync. Use when: (1) creating or modifying service worker files (sw.js), (2) implementing offline-first caching (cache-first, network-first, stale-while-revalidate), (3) setting up push notifications or background sync, (4) debugging service worker registration, scope, or update issues, (5) implementing navigation preload, (6) user mentions 'service worker', 'sw.js', 'offline support', 'cache strategy', 'push notification', 'background sync', 'workbox alternative', or 'PWA caching'.
Comprehensive guide for Redis state management including caching strategies, session management, pub/sub patterns, distributed locks, and data structures
PostgreSQL + Redis database design patterns. Use for data modeling, indexing, caching strategies. Covers JSONB, tiered storage, cache consistency.
Guidelines for developing GraphQL APIs and React applications using Apollo Client for state management, data fetching, and caching
Expert guidance for building production-ready FastAPI applications with modular architecture where each business domain is an independent module with own routes, models, schemas, services, cache, and migrations. Uses UV + pyproject.toml for modern Python dependency management, project name subdirectory for clean workspace organization, structlog (JSON+colored logging), pydantic-settings configuration, auto-discovery module loader, async SQLAlchemy with PostgreSQL, per-module Alembic migrations, Redis/memory cache with module-specific namespaces, central httpx client, OpenTelemetry/Prometheus observability, conversation ID tracking (X-Conversation-ID header+cookie), conditional Keycloak/app-based RBAC authentication, DDD/clean code principles, and automation scripts for rapid module development. Use when user requests FastAPI project setup, modular architecture, independent module development, microservice architecture, async database operations, caching strategies, logging patterns, configuration management, authentication systems, observability implementation, or enterprise Python web services. Supports max 3-4 route nesting depth, cache invalidation patterns, inter-module communication via service layer, and comprehensive error handling workflows.
Expert at diagnosing and fixing performance bottlenecks across the stack. Covers Core Web Vitals, database optimization, caching strategies, bundle optimization, and performance monitoring. Knows when to measure vs optimize. Use when "slow page load, performance optimization, core web vitals, bundle size, lighthouse score, database slow, memory leak, optimize performance, speed up, reduce load time, performance, optimization, core-web-vitals, caching, profiling, bundle-size, database" mentioned.
ML inference latency optimization, model compression, distillation, caching strategies, and edge deployment patterns. Use when optimizing inference performance, reducing model size, or deploying ML at the edge.