Agent skill · software engineering · jeremylongshore
deploying-monitoring-stacks
Monitor use when deploying monitoring stacks including Prometheus, Grafana,
Why this skill is useful
Generates production-ready configurations and deployment manifests for monitoring stacks that the AI wouldn't reliably create on its own.
What it needs
Requires docker, helm, kubectl installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Deploying Monitoring Stacks Overview Deploy production monitoring stacks (Prometheus + Grafana, Datadog, or Victoria Metrics) with metric collection, custom dashboards, and alerting rules. Configure exporters, scrape targets, recording rules, and notification channels for comprehensive infrastructure and application observability. Prerequisites Target infrastructure identified: Kubernetes cluster, Docker hosts, or bare-metal servers Metric endpoints accessible from the monitoring platform (application /metrics, node exporters) Storage backend capacity planned for time-series data (Prometheus TSDB, Thanos, or Cortex for long-term) Alert notification channels defined: Slack webhook, PagerDuty integration key, or email SMTP Helm 3+ for Kubernetes deployments using kube-prometheus-stack or similar charts Instructions 1. Select the monitoring platform: Prometheus + Grafana for open-source self-hosted, Datadog for managed SaaS, Victoria Metrics for high-cardinality workloads 2. Deploy the monitoring stack: helm install kube-prometheus-stack prometheus-community/kube-prometheus-stack or Docker Compose for non-Kubernetes 3. Install exporters on monitored systems: node-exporter for host metrics, kube-state-metrics for Kubernetes object states, application-specific exporters 4. Configure scrape targets in prometheus.yml: define job names, scrape intervals, and relabeling rules for service discovery 5. Create recording rules for frequently queried aggregations to reduce dashboard query load 6. Define alerting rules with meaningful thresholds: high CPU (>80% for 5m), high memory (>90%), error rate (>1%), latency P99 (>500ms) 7. Configure Alertmanager with routing, grouping, and notification channels (Slack, PagerDuty, email) 8. Build Grafana dashboards: RED metrics (Rate, Errors, Duration) for services, USE metrics (Utilization, Saturation, Errors) for resources 9. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/deploying-monitoring-stacks--c4f4ad