Agent skill
monitoring-observability
Set up monitoring, logging, and observability for applications and infrastructure. Use when implementing health checks, metrics collection, log aggregation, or alerting systems. Handles Prometheus, Grafana, ELK Stack, Datadog, and monitoring best practices.
Install this agent skill to your Project
npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/supercent-io/monitoring-observability
Metadata
Additional technical details for this skill
- tags
- monitoring, observability, logging, metrics, Prometheus, Grafana, alerts
- platforms
- Claude, ChatGPT, Gemini
SKILL.md
Monitoring & Observability
When to use this skill
- Before Production Deployment: Essential monitoring system setup
- Performance Issues: Identify bottlenecks
- Incident Response: Quick root cause identification
- SLA Compliance: Track availability/response times
Instructions
Step 1: Metrics Collection (Prometheus)
Application Instrumentation (Node.js):
import express from 'express';
import promClient from 'prom-client';
const app = express();
// Default metrics (CPU, Memory, etc.)
promClient.collectDefaultMetrics();
// Custom metrics
const httpRequestDuration = new promClient.Histogram({
name: 'http_request_duration_seconds',
help: 'Duration of HTTP requests in seconds',
labelNames: ['method', 'route', 'status_code']
});
const httpRequestTotal = new promClient.Counter({
name: 'http_requests_total',
help: 'Total number of HTTP requests',
labelNames: ['method', 'route', 'status_code']
});
// Middleware to track requests
app.use((req, res, next) => {
const start = Date.now();
res.on('finish', () => {
const duration = (Date.now() - start) / 1000;
const labels = {
method: req.method,
route: req.route?.path || req.path,
status_code: res.statusCode
};
httpRequestDuration.observe(labels, duration);
httpRequestTotal.inc(labels);
});
next();
});
// Metrics endpoint
app.get('/metrics', async (req, res) => {
res.set('Content-Type', promClient.register.contentType);
res.end(await promClient.register.metrics());
});
app.listen(3000);
prometheus.yml:
global:
scrape_interval: 15s
evaluation_interval: 15s
scrape_configs:
- job_name: 'my-app'
static_configs:
- targets: ['localhost:3000']
metrics_path: '/metrics'
- job_name: 'node-exporter'
static_configs:
- targets: ['localhost:9100']
alerting:
alertmanagers:
- static_configs:
- targets: ['localhost:9093']
rule_files:
- 'alert_rules.yml'
Step 2: Alert Rules
alert_rules.yml:
groups:
- name: application_alerts
interval: 30s
rules:
# High error rate
- alert: HighErrorRate
expr: |
(
sum(rate(http_requests_total{status_code=~"5.."}[5m]))
/
sum(rate(http_requests_total[5m]))
) > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: "High error rate detected"
description: "Error rate is {{ $value }}% (threshold: 5%)"
# Slow response time
- alert: SlowResponseTime
expr: |
histogram_quantile(0.95,
sum(rate(http_request_duration_seconds_bucket[5m])) by (le)
) > 1
for: 10m
labels:
severity: warning
annotations:
summary: "Slow response time"
description: "95th percentile is {{ $value }}s"
# Pod down
- alert: PodDown
expr: up{job="my-app"} == 0
for: 2m
labels:
severity: critical
annotations:
summary: "Pod is down"
description: "{{ $labels.instance }} has been down for more than 2 minutes"
# High memory usage
- alert: HighMemoryUsage
expr: |
(
node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes
) / node_memory_MemTotal_bytes > 0.90
for: 5m
labels:
severity: warning
annotations:
summary: "High memory usage"
description: "Memory usage is {{ $value }}%"
Step 3: Log Aggregation (Structured Logging)
Winston (Node.js):
import winston from 'winston';
const logger = winston.createLogger({
level: process.env.LOG_LEVEL || 'info',
format: winston.format.combine(
winston.format.timestamp(),
winston.format.errors({ stack: true }),
winston.format.json()
),
defaultMeta: {
service: 'my-app',
environment: process.env.NODE_ENV
},
transports: [
new winston.transports.Console({
format: winston.format.combine(
winston.format.colorize(),
winston.format.simple()
)
}),
new winston.transports.File({
filename: 'logs/error.log',
level: 'error'
}),
new winston.transports.File({
filename: 'logs/combined.log'
})
]
});
// Usage
logger.info('User logged in', { userId: '123', ip: '1.2.3.4' });
logger.error('Database connection failed', { error: err.message, stack: err.stack });
// Express middleware
app.use((req, res, next) => {
logger.info('HTTP Request', {
method: req.method,
path: req.path,
ip: req.ip,
userAgent: req.get('user-agent')
});
next();
});
Step 4: Grafana Dashboard
dashboard.json (example):
{
"dashboard": {
"title": "Application Metrics",
"panels": [
{
"title": "Request Rate",
"type": "graph",
"targets": [
{
"expr": "rate(http_requests_total[5m])",
"legendFormat": "{{method}} {{route}}"
}
]
},
{
"title": "Error Rate",
"type": "graph",
"targets": [
{
"expr": "rate(http_requests_total{status_code=~\"5..\"}[5m])",
"legendFormat": "Errors"
}
]
},
{
"title": "Response Time (p95)",
"type": "graph",
"targets": [
{
"expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))"
}
]
},
{
"title": "CPU Usage",
"type": "gauge",
"targets": [
{
"expr": "rate(process_cpu_seconds_total[5m]) * 100"
}
]
}
]
}
}
Step 5: Health Checks
Advanced Health Check:
interface HealthStatus {
status: 'healthy' | 'degraded' | 'unhealthy';
timestamp: string;
uptime: number;
checks: {
database: { status: string; latency?: number; error?: string };
redis: { status: string; latency?: number };
externalApi: { status: string; latency?: number };
};
}
app.get('/health', async (req, res) => {
const startTime = Date.now();
const health: HealthStatus = {
status: 'healthy',
timestamp: new Date().toISOString(),
uptime: process.uptime(),
checks: {
database: { status: 'unknown' },
redis: { status: 'unknown' },
externalApi: { status: 'unknown' }
}
};
// Database check
try {
const dbStart = Date.now();
await db.raw('SELECT 1');
health.checks.database = {
status: 'healthy',
latency: Date.now() - dbStart
};
} catch (error) {
health.status = 'unhealthy';
health.checks.database = {
status: 'unhealthy',
error: error.message
};
}
// Redis check
try {
const redisStart = Date.now();
await redis.ping();
health.checks.redis = {
status: 'healthy',
latency: Date.now() - redisStart
};
} catch (error) {
health.status = 'degraded';
health.checks.redis = { status: 'unhealthy' };
}
const statusCode = health.status === 'healthy' ? 200 : health.status === 'degraded' ? 200 : 503;
res.status(statusCode).json(health);
});
Output format
Monitoring Dashboard Configuration
Golden Signals:
1. Latency (Response Time)
- P50, P95, P99 percentiles
- Per API endpoint
2. Traffic (Request Volume)
- Requests per second
- Per endpoint, per status code
3. Errors (Error Rate)
- 5xx error rate
- 4xx error rate
- Per error type
4. Saturation (Resource Utilization)
- CPU usage
- Memory usage
- Disk I/O
- Network bandwidth
Constraints
Required Rules (MUST)
- Structured Logging: JSON format logs
- Metric Labels: Maintain uniqueness (be careful of high cardinality)
- Prevent Alert Fatigue: Only critical alerts
Prohibited (MUST NOT)
- Do Not Log Sensitive Data: Never log passwords, API keys
- Excessive Metrics: Unnecessary metrics waste resources
Best practices
- Define SLO: Clearly define Service Level Objectives
- Write Runbooks: Document response procedures per alert
- Dashboards: Customize dashboards as needed per team
References
Metadata
Version
- Current Version: 1.0.0
- Last Updated: 2025-01-01
- Compatible Platforms: Claude, ChatGPT, Gemini
Related Skills
- deployment: Monitoring alongside deployment
- security: Security event monitoring
Tags
#monitoring #observability #Prometheus #Grafana #logging #metrics #infrastructure
Examples
Example 1: Basic usage
Example 2: Advanced usage
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