prometheus
Prometheus and Grafana Cloud Metrics overview including PromQL query language, Metrics Drilldown, alerting, recording rules, and integration patterns. Use when working with Prometheus, writing PromQL queries, configuring alerting, or discussing metrics architecture and best practices.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/grafana/skills --skill prometheus- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads prometheus automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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"})
Common Patterns
# Error rate percentage
sum(rate(http_requests_total{status=~"5.."}[5m]))
/ sum(rate(http_requests_total[5m])) * 100
# Saturation (CPU usage %)
100 - (avg by(instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)
# Memory usage
node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes
# Predict disk full (linear extrapolation)
predict_linear(node_filesystem_free_bytes[6h], 24*3600) < 0
Alerting Rules
Prometheus Alerting Rule
groups:
- name: api_alerts
rules:
- alert: HighErrorRate
expr: |
sum(rate(http_requests_total{status=~"5.."}[5m]))
/ sum(rate(http_requests_total[5m])) > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: "High 5xx error rate ({{ $value | humanizePercentage }})"
Alertmanager Routing
# alertmanager.yml
route:
receiver: default
group_by: [alertname, job]
group_wait: 30s
group_interval: 5m
routes:
- match:
severity: critical
receiver: pagerduty
- match:
severity: warning
receiver: slack
receivers:
- name: pagerduty
pagerduty_configs:
- service_key: "<key>"
- name: slack
slack_configs:
- channel: "#alerts"
api_url: "<webhook_url>"
- name: default
email_configs:
- to: "oncall@example.com"
Validate Alerting Configuration
promtool check rules rules.yml
amtool check-config alertmanager.yml
amtool config routes test --config.file=alertmanager.yml severity=critical
Recording Rules
Pre-compute expensive PromQL for dashboard performance:
groups:
- name: api_rules
interval: 1m
rules:
- record: job:http_requests:rate5m
expr: sum by (job) (rate(http_requests_total[5m]))
- record: job:http_request_duration_seconds:p99
expr: histogram_quantile(0.99, sum by (job, le) (rate(http_request_duration_seconds_bucket[5m])))
Deploy and Verify Recording Rules
# 1. Validate rule syntax
promtool check rules rules/recording.yml
# 2. Reload Prometheus (after adding to rule_files in prometheus.yml)
curl -X POST http://localhost:9090/-/reload
# 3. Verify rules are active
curl -s http://localhost:9090/api/v1/rules | jq '.data.groups[].rules[] | {name, health}'
Metrics Drilldown (Grafana 12+)
Queryless Prometheus exploration — browse metrics without writing PromQL. Navigate to
Explore > Metrics Drilldown or use <grafana-url>/a/grafana-metricsdrilldown-app.
Provides metric search with label breakdown, smart segmentation for anomaly detection,
auto-visualization, and telemetry pivoting from metrics to related logs and traces.
Resources
FAQ
What does the prometheus skill do?
Prometheus and Grafana Cloud Metrics overview including PromQL query language, Metrics Drilldown, alerting, recording rules, and integration patterns. Use when working with Prometheus, writing PromQL queries, configuring alerting, or discussing metrics architecture and best practices.
How do I install prometheus?
Run npx skills add https://github.com/grafana/skills --skill prometheus in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/prometheus/ (personal) or .claude/skills/prometheus/ (project).
Which AI agents can use prometheus?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is prometheus safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains prometheus?
It is published by grafana in grafana/skills. It has 5,264 recorded installs.
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