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Found 84 Skills
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.
Set up Prometheus monitoring for applications with custom metrics, scraping configurations, and service discovery. Use when implementing time-series metrics collection, monitoring applications, or building observability infrastructure.
Application monitoring and observability setup for Python/React projects. Use when configuring logging, metrics collection, health checks, alerting rules, or dashboard creation. Covers structured logging with structlog, Prometheus metrics for FastAPI, health check endpoints, alert threshold design, Grafana dashboard patterns, error tracking with Sentry, and uptime monitoring. Does NOT cover incident response procedures (use incident-response) or deployment (use deployment-pipeline).
Grafana Beyla eBPF auto-instrumentation for application observability without code changes. Covers supported languages/runtimes, requirements, installation, configuration (discovery, eBPF settings, OTLP traces export, Prometheus metrics export), Kubernetes deployment, and integration with Grafana Cloud. Use when setting up zero-code instrumentation, configuring eBPF probes, deploying Beyla to Kubernetes, connecting to Tempo/Prometheus, or troubleshooting instrumentation issues.
Observability and SRE expert. Use when setting up monitoring, logging, tracing, defining SLOs, or managing incidents. Covers Prometheus, Grafana, OpenTelemetry, and incident response best practices.
Reduce Grafana Cloud Metrics costs by managing cardinality with Adaptive Metrics aggregation rules. Use when the user asks to reduce metrics costs, manage cardinality, create aggregation rules, apply label dropping, analyse unused metrics, understand Active Series, or optimise Prometheus storage. Triggers on phrases like "adaptive metrics", "reduce cardinality", "aggregation rules", "metrics cost", "too many series", "Active Series", "label dropping", "unused metrics", "cardinality reduction", or "metrics spend".
Reviews Prometheus instrumentation in Go code for proper metric types, labels, and patterns. Use when reviewing code with prometheus/client_golang metrics.
Author monitoring resources: PrometheusRules, ServiceMonitors, PodMonitors, AlertmanagerConfig, Silence CRs, and canary-checker health checks. Use when: (1) Creating or modifying alert rules (PrometheusRule), (2) Adding scrape targets (ServiceMonitor/PodMonitor), (3) Configuring Alertmanager routing or silences, (4) Writing canary-checker health checks, (5) Creating recording rules, (6) Adding monitoring for a new application or platform component. Triggers: "create alert", "add alerting", "PrometheusRule", "ServiceMonitor", "PodMonitor", "AlertmanagerConfig", "silence alert", "canary check", "recording rule", "add monitoring", "scrape target", "alert rule", "prometheus rule", "health check canary"
Comprehensive observability and monitoring skill covering Prometheus, Grafana, metrics collection, alerting, exporters, PromQL, and production monitoring patterns for distributed systems and cloud-native applications
Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging
Grafana Professional Services tool for identifying which Prometheus metrics drive high Data Points per Minute (DPM). Analyzes metric-level DPM with per-label breakdown to help optimize Grafana Cloud costs. Use when the user asks about DPM analysis, high-cardinality metrics, metric cost optimization, finding noisy metrics, or running dpm-finder against a Grafana Cloud Prometheus endpoint.