Prometheus
A comprehensive skill catalog for AI agents
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Prometheus monitoring and alerting with PromQL. Use for metrics collection.
SKILL.md
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Prometheus
Prometheus is the cloud-native standard for metric collection. Prometheus 3.0 (2025) features a modern UI, Native Histograms, and direct OpenTelemetry (OTLP) ingestion.
When to Use
- Kubernetes: Standard monitoring stack (Prometheus Operator).
- White-box Monitoring: Measuring internal state (heap usage, request count) via endpoints.
- Alerting: Alertmanager handles de-duplication and routing to Slack/PagerDuty.
Quick Start
# prometheus.yml
global:
scrape_interval: 15s
scrape_configs:
- job_name: "node"
static_configs:
- targets: ["localhost:9100"]
Core Concepts
Time Series Format
Metrics are identified by name and label pairs.
http_requests_total{method="POST", handler="/api"}
PromQL
Powerful query language.
rate(http_requests_total[5m])
Pull Model
Prometheus scrapes targets. Apps do not push to Prometheus (usually).
Best Practices (2025)
Do:
- Use High-Cardinality wisely: Native Histograms in v3.0 help, but keep labels bounded.
- Use Service Monitors: In K8s, use the Operator's
ServiceMonitorCRD instead of manual config. - Use OTLP: Ingest OTel metrics directly if you are transitioning standards.
Don't:
- Don't use for logs: It is for metrics only. Use Loki for logs.