Skill UI

This guide provides a comprehensive method for setting up Snowflake observability. It teaches how to leverage built-in ACCOUNT_USAGE views to monitor critical metrics such as query performance (latency, error rates), credit consumption, and storage growth. Furthermore, it demonstrates how to implement automated alerts for operational issues (e.g., high failure rates, stuck warehouses) and export metrics to external monitoring systems like Grafana or Prometheus.

Implement a comprehensive observability stack for Webflow Data API v2 integrations. This skill provides structured code using Prometheus metrics, OpenTelemetry distributed tracing, and structured logging (pino) to monitor API call frequency, latency, error rates, and rate limit usage. Ideal for developers building robust, production-grade Webflow-connected SaaS applications that require deep performance insights and reliable alerting.

This skill exports cost-tracking telemetry data into formats suitable for external observability platforms like Prometheus, Grafana, and Datadog. It allows cost data, previously confined to the AgentDB namespace, to be visualized and monitored in real-time dashboards via Prometheus textfile collectors or structured JSON webhooks.

Set up effective alerting rules, configure on-call rotations, and manage incident response workflows for production systems. Covers Prometheus alerting, Alertmanager routing, PagerDuty integration, and alert design best practices.

This skill enables the creation of comprehensive monitoring solutions for database migrations. It covers Change Data Capture (CDC) pipelines, real-time data synchronization, anomaly detection, and automated alerting. You will learn to build observability infrastructure using tools like Prometheus, Grafana, Kafka, and Debezium to track migration metrics, document processing, and replication lag in enterprise environments.

Learn to operate GPU-backed Kubernetes clusters for AI inference and training workloads. Covers NVIDIA GPU Operator installation, device plugin configuration, MIG partitioning for resource sharing, and time-slicing for non-MIG GPUs. Includes GPU-aware autoscaling, health monitoring with DCGM and Prometheus, and troubleshooting scheduling or driver issues. Essential for production AI infrastructure management.

Provision, configure, and monitor NVIDIA GPU servers for AI inference and training workloads, including driver/CUDA installation, Docker GPU support, MIG partitioning, and health monitoring.

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.

This skill enables building production-ready monitoring, logging, and tracing systems for enterprise applications. It covers implementing observability strategies, managing SLIs and SLOs, and handling incident response workflows. Expertise includes distributed tracing, log management, alerting, and reliability engineering practices using tools like Prometheus, Grafana, and OpenTelemetry to ensure system stability and performance.

Implement vendor-neutral observability using OpenTelemetry standards for traces, metrics, and logs. Configure collectors, set up Kubernetes deployments, and instrument Python services. Supports Prometheus, Jaeger, and Loki backends. Essential for unified monitoring in microservices and infrastructure environments.

Set up Prometheus monitoring, configure metric scraping and service discovery, create recording and alert rules, and deploy via Helm or Docker Compose for infrastructure and application observability.

Configure Prometheus and Grafana for comprehensive metrics collection, visualization, and alerting. This skill covers Docker and Kubernetes deployment, PromQL query writing for analysis, and setting up alerting rules for system performance monitoring. Ideal for DevOps engineers managing infrastructure observability.