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Found 84 Skills
Use when setting up monitoring systems, logging, metrics, tracing, or alerting. Invoke for dashboards, Prometheus/Grafana, load testing, profiling, capacity planning.
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 patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
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"
Diagnostic guide for active Prometheus cardinality problems — slow queries, OOMing Prometheus, high Grafana Cloud Active Series or DPM bills, "too many samples" ingest errors, series churn, or rapid memory growth. Walks through tsdb status endpoints, per-metric and per-label drill-downs, common-culprit galleries, and remediation paths. Use when the user is *currently experiencing* a cardinality fire. For preventing cardinality issues at the source, route to prometheus-label-strategy. For post-ingest aggregation, route to adaptive-metrics. For DPM-specific analysis, route to dpm-finder.
Comprehensive observability and monitoring skill covering Prometheus, Grafana, metrics collection, alerting, exporters, PromQL, and production monitoring patterns for distributed systems and cloud-native applications
Grafana Mimir scalable long-term metrics storage. Covers architecture (distributor/ingester/compactor/querier/ query-frontend/store-gateway/ruler), deployment modes (monolithic/microservices), configuration, Prometheus remote write, PromQL querying, multi-tenancy, compaction, and operations. Use when working with Mimir for metrics storage, scaling Prometheus, configuring Mimir clusters, writing PromQL, or debugging Mimir.