Prometheus

This gives Claude solid knowledge of Prometheus monitoring and PromQL syntax, plus Grafana Cloud Metrics integration. It covers the essentials: instant and range vector selectors, rate calculations, aggregations, and common patterns like error rates and resource saturation queries. You also get guidance on alerting with both Alertmanager and Grafana, recording rules for pre-computation, and the newer Metrics Drilldown feature for queryless exploration. The PromQL quick reference alone is worth having on hand when you're writing queries or debugging dashboards. Most useful when you're setting up monitoring, writing alerts, or need to remember the difference between rate and irate.

skills.sh installs: 5.6K
GitHub stars: 307
last updated: Aug 21, 2026

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Install

$ npx -y skills add grafana/skills --skill prometheus --agent claude-code

Installs into .claude/skills of the current project.

Files

Metrics with Prometheus and Grafana

Docs: https://prometheus.io/docs/ | Grafana Cloud Metrics: https://grafana.com/docs/grafana-cloud/send-data/metrics/

PromQL Quick Reference

Instant Vector Selectors

# By metric name
http_requests_total

# Label filter
http_requests_total{job="api-server"}

# Multiple labels (AND)
http_requests_total{job="api-server", method="GET"}

# Regex
http_requests_total{job=~"api.*", status=~"5.."}

# Negative
http_requests_total{status!="200"}

Range Vectors & Rates

# Per-second rate over 5 minutes
rate(http_requests_total[5m])

# Increase over interval
increase(http_requests_total[1h])

# Instant rate (last two samples)
irate(http_requests_total[5m])

# Offset (5 minutes ago)
rate(http_requests_total[5m] offset 5m)

Aggregations

# Sum by label
sum by (job) (rate(http_requests_total[5m]))

# Average
avg by (instance) (node_cpu_seconds_total)

# Top-K
topk(5, rate(http_requests_total[5m]))

# Histogram quantiles
histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m]))

# Count distinct
count(up{job="api"})