Agent skill

production-monitoring

Use when the Production Engineer is assessing production health, setting up monitoring, defining health checks, evaluating SLA compliance, or responding to incidents. Activates when discussing production readiness, system health, alerting, or incident response.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/production-monitoring

SKILL.md

Production Monitoring Expertise

When This Applies

Apply this guidance when:

  • Assessing whether a release is production-ready
  • Setting up or reviewing health checks
  • Evaluating system stability after a deployment
  • Defining monitoring and alerting requirements
  • Responding to production incidents

Health Check Design

Application Health Endpoint

Every service should expose a health endpoint:

GET /health
Response:
{
  "status": "healthy" | "degraded" | "unhealthy",
  "version": "1.2.3",
  "timestamp": "2026-02-28T10:00:00Z",
  "checks": {
    "database": "healthy",
    "cache": "healthy",
    "external_api": "degraded"
  }
}

Health Check Levels

Level What to Check Frequency
Liveness Process is running Every 10s
Readiness Can handle requests Every 30s
Deep health All dependencies ok Every 60s

Key Metrics to Monitor

The Four Golden Signals

  1. Latency — Response time distribution (p50, p95, p99)
  2. Traffic — Request rate (requests/second)
  3. Errors — Error rate (5xx responses / total)
  4. Saturation — Resource utilization (CPU, memory, disk, connections)

Service-Level Indicators (SLIs)

SLI Target Measurement
Availability 99.9% Successful responses / total
Latency p95 < 200ms Response time percentiles
Error rate < 0.1% Error responses / total
Throughput > N req/s Requests per second

Post-Deployment Verification

After merging to main and deploying:

Immediate (0-5 minutes)

  • Health endpoint returns healthy
  • No error spike in logs
  • Response times are within normal range
  • Key user flows work (manual smoke test)

Short-term (5-30 minutes)

  • Error rate is stable or improving
  • No memory leaks (memory usage stable)
  • No connection pool exhaustion
  • No increase in support tickets or alerts

Medium-term (30 min - 2 hours)

  • Performance metrics are within historical norms
  • Background jobs are processing normally
  • No data integrity issues reported
  • All scheduled tasks ran successfully

Alerting Guidelines

Alert Severity

Severity Condition Response
Critical Service down, data loss risk Page on-call, immediate action
Warning Degraded performance, elevated errors Investigate within 30 min
Info Anomaly detected, approaching threshold Review during business hours

Alert Design Rules

  1. Actionable — Every alert should have a clear response action
  2. No noise — If an alert fires > 5 times/day without action needed, fix or remove it
  3. Contextual — Include what's wrong, what threshold was breached, and where to look
  4. Escalation — If not acknowledged within N minutes, escalate to next level

Incident Documentation

After any production incident, document in reports/INCIDENT_<YYYYMMDD>_<NNN>.md:

markdown
# Incident Report — <date>

## Summary
<One-line description of what happened>

## Timeline
- HH:MM — Issue detected
- HH:MM — Investigation started
- HH:MM — Root cause identified
- HH:MM — Fix applied
- HH:MM — Verified resolved

## Impact
- Duration: X minutes
- Users affected: N
- Data impact: None / Describe

## Root Cause
<What caused the issue>

## Resolution
<How it was fixed>

## Prevention
<What changes will prevent recurrence>

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