Query monitoring metrics, check alerts, and verify target health via the Prometheus HTTP API. API and PromQL syntax are referenced through Context7 MCP; only environment-specific rules are documented here.
Setup
Configure your Prometheus endpoint before using this skill:
| Variable | Description | Required |
|---|---|---|
PROMETHEUS_URL |
Your Prometheus server URL (e.g. http://prometheus.internal:9090) |
Yes |
Common metric prefixes to monitor:
node_*— Node Exporter (host metrics: CPU, memory, disk, network)kube_*— kube-state-metrics (K8s object state: deployments, pods, nodes)container_*— cAdvisor (container resource usage)apiserver_*— K8s API Server metricskubelet_*— Kubelet metricsprometheus_*— Prometheus self-monitoring
If you have additional exporters (Kafka, Redis, custom applications), add their metric prefixes here:
| Prefix | Source | Description |
|---|---|---|
kafka_* |
Kafka Exporter | Broker and consumer group metrics |
fluentbit_* |
Fluent Bit | Log pipeline metrics |
| (add your own) |
Authentication: Configure as needed for your environment (none, basic auth, or bearer token).
API endpoints and PromQL syntax can be found in the official Prometheus documentation.
Rules
Query Considerations
- Confirm whether your Prometheus uses HTTP or HTTPS and configure
PROMETHEUS_URLaccordingly stepshould not be smaller than the scrape interval (typically 15s-60s) to avoid invalid interpolation- High-cardinality labels (user_id, request_id) must not be used in
rate()/sum by()aggregations - On macOS, use
date -v-1H +%sinstead of the Linuxdate -d '1 hour ago' +%s
Job Label Convention
Job labels are the key to locating services. Common naming patterns:
| Pattern | Example | Description |
|---|---|---|
{env}-{region}-{service} |
prod-gateway |
Service by environment and region |
kubernetes-{resource} |
kubernetes-pods |
Standard K8s metrics |
{component}-exporter |
kafka-exporter |
Dedicated exporters |
Configure your own job naming convention here to help the agent locate services correctly.
Kafka Consumer Lag Monitoring
If you run Kafka with a Kafka Exporter, this is a common pattern:
# Aggregate consumer lag by consumergroup and topic
sum by (consumergroup, topic) (kafka_consumergroup_lag)
Normal lag range depends on your workload. Sustained growth indicates consumer processing capacity issues.
Common Workflows
- Node resource investigation:
node_cpu_seconds_total->node_memory_MemAvailable_bytes->node_filesystem_avail_bytes-> locate high-load nodes - Kafka health check:
kafka_brokers(broker count) ->kafka_consumergroup_lag(consumer lag) ->kafka_topic_partition_under_replicated_partition(under-replicated partitions) - Container investigation:
container_cpu_usage_seconds_total->container_memory_working_set_bytes-> aggregate by pod/namespace - K8s cluster health:
kube_node_status_condition->kube_pod_status_phase->kube_deployment_status_replicas_unavailable
Examples
Bad
# High-cardinality label aggregation -- will cause Prometheus OOM
curl "$PROMETHEUS_URL/api/v1/query?query=sum by(pod)(rate(container_cpu_usage_seconds_total[5m]))"
# pod label cardinality is too high (hundreds of pods); aggregate by namespace or deployment instead
Good
# Check Kafka consumer lag
curl -s "$PROMETHEUS_URL/api/v1/query?query=sum%20by%20(consumergroup,topic)(kafka_consumergroup_lag)" | jq '.data.result[] | {group: .metric.consumergroup, topic: .metric.topic, lag: .value[1]}'
# Check node CPU usage top 10
curl -s "$PROMETHEUS_URL/api/v1/query?query=topk(10,100*(1-rate(node_cpu_seconds_total{mode=\"idle\"}[5m])))" | jq '.data.result[] | {node: .metric.instance, cpu_pct: .value[1]}'
# Disk space prediction (will it be full in 24h)
curl -s "$PROMETHEUS_URL/api/v1/query?query=predict_linear(node_filesystem_avail_bytes{mountpoint=\"/\"}[24h],86400)" | jq '.data.result[] | {instance: .metric.instance, predicted_bytes: .value[1]}'