prometheus-configuration
Skill2 starsChanged 8 months agoMITView on GitHub
Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.
5 other projects use a version of this, 5 of them edited.
Adopted by
Most recently taken up by
- ControllableModel/SWE-Skills-Benchedited, first seen Nov 8, 2026
- xfstudio/skillsedited, first seen Oct 13, 2026
- Samueladewole/claude-skillsedited, first seen Oct 13, 2026
- ai4brands-design/claude-skillsedited, first seen Mar 22, 2026
- bcastelino/agent-skills-kitedited, first seen Feb 28, 2026
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 5
- xfstudio/skills★ 991% same
- ControllableModel/SWE-Skills-Bench★ 897% same
- ai4brands-design/claude-skills★ 291% same
- bcastelino/agent-skills-kit★ 291% same
- Samueladewole/claude-skills★ 097% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository239 other files it gives its agents.
- claude-code-docs/agents.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- claude-code-docs/llms.txtllms.txt
- agent-native-architectureSkill
- claude-apiSkill
- embedding-strategiesSkill
- fal-ai-mediaSkill
- gemini-imagegenSkill
- hybrid-search-implementationSkill
- langchain-architectureSkill
- llm-evaluationSkill
- ml-pipeline-workflowSkill
- prompt-engineering-patternsSkill
- rag-implementationSkill
- similarity-search-patternsSkill
- vector-index-tuningSkill
- api-design-principlesSkill
- api-designSkill
- architecture-patternsSkill
- cqrs-implementationSkill
- event-store-designSkill
- fastapi-templatesSkill
- microservices-patternsSkill
- openapi-spec-generationSkill
- projection-patternsSkill
- saga-orchestrationSkill
- api-conventionsSkill
- andrew-kane-gem-writerSkill
- angular-migrationSkill
- auth-implementation-patternsSkill
- backend-patternsSkill
- bazel-build-optimizationSkill
- coding-standardsSkill
- database-migrationSkill
- debugging-strategiesSkill
- dependency-upgradeSkill
- dhh-rails-styleSkill
- dotnet-backend-patternsSkill
- dspy-rubySkill
- error-handling-patternsSkill
- monorepo-managementSkill
- nodejs-backend-patternsSkill
- nx-workspace-patternsSkill
- react-modernizationSkill
- temporal-python-testingSkill
- turborepo-cachingSkill
- workflow-orchestration-patternsSkill
- defi-protocol-templatesSkill
- nft-standardsSkill
- solidity-securitySkill
- web3-testingSkill
- backtesting-frameworksSkill
- billing-automationSkill
- competitive-landscapeSkill
- data-storytellingSkill
- employment-contract-templatesSkill
- investor-materialsSkill
- investor-outreachSkill
- kpi-dashboard-designSkill
- market-sizing-analysisSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
1 rule. 1 is common, 0 are distinctive, and 0 are very specific to this project.
1
Common
Most of the 5 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 227
Create pre-computed metrics for frequently queried expressions:
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Prometheus Configuration
Complete guide to Prometheus setup, metric collection, scrape configuration, and recording rules.
Purpose
Configure Prometheus for comprehensive metric collection, alerting, and monitoring of infrastructure and applications.
When to Use
- Set up Prometheus monitoring
- Configure metric scraping
- Create recording rules
- Design alert rules
- Implement service discovery
Prometheus Architecture
┌──────────────┐
│ Applications │ ← Instrumented with client libraries
└──────┬───────┘
│ /metrics endpoint
↓
┌──────────────┐
│ Prometheus │ ← Scrapes metrics periodically
│ Server │
└──────┬───────┘
│
├─→ AlertManager (alerts)
├─→ Grafana (visualization)
└─→ Long-term storage (Thanos/Cortex)Installation
Kubernetes with Helm
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
helm install prometheus prometheus-community/kube-prometheus-stack \
--namespace monitoring \
--create-namespace \
--set prometheus.prometheusSpec.retention=30d \
--set prometheus.prometheusSpec.storageVolumeSize=50GiDocker Compose
version: "3.8"
services:
prometheus:
image: prom/prometheus:latest
ports:
- "9090:9090"
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
- prometheus-data:/prometheus
command:
- "--config.file=/etc/prometheus/prometheus.yml"
- "--storage.tsdb.path=/prometheus"
- "--storage.tsdb.retention.time=30d"
volumes:
prometheus-data:Configuration File
prometheus.yml:
global:
scrape_interval: 15s
evaluation_interval: 15s
external_labels:
cluster: "production"
region: "us-west-2"
# Alertmanager configuration
alerting:
alertmanagers:
- static_configs:
- targets:
- alertmanager:9093
# Load rules files
rule_files:
- /etc/prometheus/rules/*.yml
# Scrape configurations
scrape_configs:
# Prometheus itself
- job_name: "prometheus"
static_configs:
- targets: ["localhost:9090"]
# Node exporters
- job_name: "node-exporter"
static_configs:
- targets:
- "node1:9100"
- "node2:9100"
- "node3:9100"
relabel_configs:
- source_labels: [__address__]
target_label: instance
regex: "([^:]+)(:[0-9]+)?"
replacement: "${1}"
# Kubernetes pods with annotations
- job_name: "kubernetes-pods"
kubernetes_sd_configs:
- role: pod
relabel_configs:
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels:
[__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
action: replace
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: $1:$2
target_label: __address__
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: namespace
- source_labels: [__meta_kubernetes_pod_name]
action: replace
target_label: pod
# Application metrics
- job_name: "my-app"
static_configs:
- targets:
- "app1.example.com:9090"
- "app2.example.com:9090"
metrics_path: "/metrics"
scheme: "https"
tls_config:
ca_file: /etc/prometheus/ca.crt
cert_file: /etc/prometheus/client.crt
key_file: /etc/prometheus/client.keyReference: See assets/prometheus.yml.template
Scrape Configurations
Static Targets
scrape_configs:
- job_name: "static-targets"
static_configs:
- targets: ["host1:9100", "host2:9100"]
labels:
env: "production"
region: "us-west-2"File-based Service Discovery
scrape_configs:
- job_name: "file-sd"
file_sd_configs:
- files:
- /etc/prometheus/targets/*.json
- /etc/prometheus/targets/*.yml
refresh_interval: 5mtargets/production.json:
[
{
"targets": ["app1:9090", "app2:9090"],
"labels": {
"env": "production",
"service": "api"
}
}
]Kubernetes Service Discovery
scrape_configs:
- job_name: "kubernetes-services"
kubernetes_sd_configs:
- role: service
relabel_configs:
- source_labels:
[__meta_kubernetes_service_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels:
[__meta_kubernetes_service_annotation_prometheus_io_scheme]
action: replace
target_label: __scheme__
regex: (https?)
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)Reference: See references/scrape-configs.md
Recording Rules
Create pre-computed metrics for frequently queried expressions:
# /etc/prometheus/rules/recording_rules.yml
groups:
- name: api_metrics
interval: 15s
rules:
# HTTP request rate per service
- record: job:http_requests:rate5m
expr: sum by (job) (rate(http_requests_total[5m]))
# Error rate percentage
- record: job:http_requests_errors:rate5m
expr: sum by (job) (rate(http_requests_total{status=~"5.."}[5m]))
- record: job:http_requests_error_rate:percentage
expr: |
(job:http_requests_errors:rate5m / job:http_requests:rate5m) * 100
# P95 latency
- record: job:http_request_duration:p95
expr: |
histogram_quantile(0.95,
sum by (job, le) (rate(http_request_duration_seconds_bucket[5m]))
)
- name: resource_metrics
interval: 30s
rules:
# CPU utilization percentage
- record: instance:node_cpu:utilization
expr: |
100 - (avg by (instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)
# Memory utilization percentage
- record: instance:node_memory:utilization
expr: |
100 - ((node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100)
# Disk usage percentage
- record: instance:node_disk:utilization
expr: |
100 - ((node_filesystem_avail_bytes / node_filesystem_size_bytes) * 100)Reference: See references/recording-rules.md
Alert Rules
# /etc/prometheus/rules/alert_rules.yml
groups:
- name: availability
interval: 30s
rules:
- alert: ServiceDown
expr: up{job="my-app"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Service {{ $labels.instance }} is down"
description: "{{ $labels.job }} has been down for more than 1 minute"
- alert: HighErrorRate
expr: job:http_requests_error_rate:percentage > 5
for: 5m
labels:
severity: warning
annotations:
summary: "High error rate for {{ $labels.job }}"
description: "Error rate is {{ $value }}% (threshold: 5%)"
- alert: HighLatency
expr: job:http_request_duration:p95 > 1
for: 5m
labels:
severity: warning
annotations:
summary: "High latency for {{ $labels.job }}"
description: "P95 latency is {{ $value }}s (threshold: 1s)"
- name: resources
interval: 1m
rules:
- alert: HighCPUUsage
expr: instance:node_cpu:utilization > 80
for: 5m
labels:
severity: warning
annotations:
summary: "High CPU usage on {{ $labels.instance }}"
description: "CPU usage is {{ $value }}%"
- alert: HighMemoryUsage
expr: instance:node_memory:utilization > 85
for: 5m
labels:
severity: warning
annotations:
summary: "High memory usage on {{ $labels.instance }}"
description: "Memory usage is {{ $value }}%"
- alert: DiskSpaceLow
expr: instance:node_disk:utilization > 90
for: 5m
labels:
severity: critical
annotations:
summary: "Low disk space on {{ $labels.instance }}"
description: "Disk usage is {{ $value }}%"Validation
# Validate configuration
promtool check config prometheus.yml
# Validate rules
promtool check rules /etc/prometheus/rules/*.yml
# Test query
promtool query instant http://localhost:9090 'up'Reference: See scripts/validate-prometheus.sh
Best Practices
- Use consistent naming for metrics (prefix_name_unit)
- Set appropriate scrape intervals (15-60s typical)
- Use recording rules for expensive queries
- Implement high availability (multiple Prometheus instances)
- Configure retention based on storage capacity
- Use relabeling for metric cleanup
- Monitor Prometheus itself
- Implement federation for large deployments
- Use Thanos/Cortex for long-term storage
- Document custom metrics
Troubleshooting
Check scrape targets:
curl http://localhost:9090/api/v1/targetsCheck configuration:
curl http://localhost:9090/api/v1/status/configTest query:
curl 'http://localhost:9090/api/v1/query?query=up'Related Skills
grafana-dashboards- For visualizationslo-implementation- For SLO monitoringdistributed-tracing- For request tracing
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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