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Found 487 Skills
Create serverless functions on Azure with triggers, bindings, authentication, and monitoring. Use for event-driven computing without managing infrastructure.
Comprehensive PostgreSQL database engineering skill covering indexing strategies, query optimization, performance tuning, partitioning, replication, backup and recovery, high availability, and production database management. Master advanced PostgreSQL features including MVCC, VACUUM operations, connection pooling, monitoring, and scalability patterns.
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring.
Expert Celery distributed task queue engineer specializing in async task processing, workflow orchestration, broker configuration (Redis/RabbitMQ), Celery Beat scheduling, and production monitoring. Deep expertise in task patterns (chains, groups, chords), retries, rate limiting, Flower monitoring, and security best practices. Use when designing distributed task systems, implementing background job processing, building workflow orchestration, or optimizing task queue performance.
AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.
Expert in setting up Sentry error tracking and Google Analytics for NestJS and Next.js applications. Use this skill when users need monitoring, error tracking, or analytics configuration.
Decompose complex tasks, design dependency graphs, and coordinate multi-agent work with proper task descriptions and workload balancing. Use this skill when breaking down work for agent teams, managing task dependencies, or monitoring team progress.
AWS RDS (Relational Database Service) management using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.
AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
Configure Spring Boot Actuator for production-grade monitoring, health probes, secured management endpoints, and Micrometer metrics across JVM services.
Health check endpoints for liveness, readiness, dependency monitoring. Use for Kubernetes, load balancers, auto-scaling, or encountering probe failures, startup delays, dependency checks, timeout configuration errors.
Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.