Prometheus Patterns Agent Skill

by vibeeval

PromQL queries, alerting rules, recording rules, Grafana dashboard JSON, SLO

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

521

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/vibeeval/vibecosystem --skill prometheus-patterns

The instruction itself

9 sections, as written by the author

Prometheus Patterns

PromQL Essentials

Rate and Error Calculations

# Request rate (per second, 5m window)
rate(http_requests_total[5m])

# Error rate percentage
sum(rate(http_requests_total{status=~"5.."}[5m]))
/ sum(rate(http_requests_total[5m])) * 100

# P99 latency from histogram
histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))

# P50 latency by endpoint
histogram_quantile(0.50,
  sum(rate(http_request_duration_seconds_bucket[5m])) by (le, handler)
)

# Saturation: CPU usage per pod
sum(rate(container_cpu_usage_seconds_total[5m])) by (pod)
/ sum(kube_pod_container_resource_limits{resource="cpu"}) by (pod) * 100

SLO: Error Budget

# SLO: 99.9% availability over 30 days
# Error budget = 0.1% = 43.2 minutes/month

# Current burn rate (how fast consuming budget)
1 - (
  sum(rate(http_requests_total{status!~"5.."}[1h]))
  / sum(rate(http_requests_total[1h]))
) / (1 - 0.999)

# Remaining error budget (percentage)
1 - (
  sum(increase(http_requests_total{status=~"5.."}[30d]))
  / (sum(increase(http_requests_total[30d])) * 0.001)
)

Recording Rules

groups:
  - name: sli_rules
    interval: 30s
    rules:
      - record: job:http_request_rate:5m
        expr: sum(rate(http_requests_total[5m])) by (job)

      - record: job:http_error_rate:5m
        expr: |
          sum(rate(http_requests_total{status=~"5.."}[5m])) by (job)
          / sum(rate(http_requests_total[5m])) by (job)

      - record: job:http_latency_p99:5m
        expr: |
          histogram_quantile(0.99,
            sum(rate(http_request_duration_seconds_bucket[5m])) by (le, job)
          )

Alerting Rules

groups:
  - name: slo_alerts
    rules:
      - alert: HighErrorRate
        expr: job:http_error_rate:5m > 0.01
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Error rate above 1% for {{ $labels.job }}"
          runbook: "https://wiki.internal/runbooks/high-error-rate"

      - alert: HighLatency
        expr: job:http_latency_p99:5m > 0.5
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "P99 latency above 500ms for {{ $labels.job }}"

      - alert: ErrorBudgetBurn
        expr: |
          (
            sum(rate(http_requests_total{status=~"5.."}[1h]))
            / sum(rate(http_requests_total[1h]))
          ) > 14.4 * 0.001
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Burning error budget 14.4x faster than allowed"

Instrumentation (Go)

import "github.com/prometheus/client_golang/prometheus"

var (
    httpRequests = prometheus.NewCounterVec(
        prometheus.CounterOpts{
            Name: "http_requests_total",
            Help: "Total HTTP requests",
        },
        []string{"method", "handler", "status"},
    )
    httpDuration = prometheus.NewHistogramVec(
        prometheus.HistogramOpts{
            Name:    "http_request_duration_seconds",
            Help:    "HTTP request duration",
            Buckets: []float64{0.01, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5},
        },
        []string{"method", "handler"},
    )
)

Checklist

[ ] RED metrics (Rate, Errors, Duration) for every service
[ ] Recording rules for expensive queries
[ ] Alerts have runbook links
[ ] Error budget alerts with multi-window burn rate
[ ] Histogram buckets match expected latency distribution
[ ] Labels have low cardinality (no user IDs, request IDs)
[ ] Grafana dashboards use recording rules, not raw queries
[ ] Alert severity matches response SLA

Anti-Patterns

High cardinality labels (user_id, trace_id) causing metric explosion
Using avg() for latency instead of histograms/quantiles
Missing for clause in alerts causing alert storms
Recording rules with too short intervals wasting resources
Alerting on symptoms without linking to causes
Not setting meaningful histogram bucket boundaries

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How to use it

Copy the folder

Take vibeeval/prometheus-patterns from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.