prometheus

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:

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 metrics
• kubelet_* — Kubelet metrics
• prometheus_* — Prometheus self-monitoring

If you have additional exporters (Kafka, Redis, custom applications), add their metric prefixes here:

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_URL accordingly
• step should 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 +%s instead of the Linux date -d '1 hour ago' +%s

Job Label Convention

Job labels are the key to locating services. Common naming patterns:

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]}'