---

name: monitoring-guidelines description: Monitoring guidelines for applications and infrastructure including metrics collection, alerting strategies, and SLO-based monitoring metadata: maintainer: Mindrally source: https://github.com/Mindrally/skills

Monitoring Guidelines

Apply these monitoring principles to ensure system reliability, performance visibility, and proactive issue detection.

Core Monitoring Principles

• Monitor the four golden signals: latency, traffic, errors, and saturation
• Implement monitoring as code for reproducibility
• Design monitoring around user experience and business impact
• Use SLOs (Service Level Objectives) to guide alerting decisions
• Balance comprehensive coverage with actionable insights

Key Metrics to Monitor

Application Metrics

• Request rate (requests per second)
• Error rate (percentage of failed requests)
• Response time (p50, p90, p95, p99 latencies)
• Active connections and concurrent users
• Queue depths and processing times

Infrastructure Metrics

• CPU utilization and load average
• Memory usage and available memory
• Disk I/O and available storage
• Network throughput and error rates
• Container and pod health (for Kubernetes)

Business Metrics

• Transaction volumes and values
• User signups and conversions
• Feature usage and adoption rates
• Revenue-impacting events
• Customer satisfaction indicators

Alerting Strategy

Alert Design Principles

• Alert on symptoms, not causes
• Make alerts actionable with clear remediation steps
• Set appropriate severity levels (critical, warning, info)
• Avoid alert fatigue through proper threshold tuning
• Include runbook links in alert notifications

SLO-Based Alerting

• Define SLOs for critical user journeys
• Calculate error budgets and burn rates
• Alert when error budget consumption is high
• Use multi-window, multi-burn-rate alerts
• Review and adjust SLOs quarterly

Alert Configuration

• Set meaningful thresholds based on baseline data
• Use hysteresis to prevent flapping alerts
• Implement alert dependencies to reduce noise
• Route alerts to appropriate teams
• Configure escalation policies

Dashboard Design

Effective Dashboards

• Create overview dashboards for service health
• Build detailed dashboards for debugging
• Use consistent layouts and naming conventions
• Include time range selectors and drill-down capabilities
• Display SLO status prominently

Dashboard Content

• Show current state and recent trends
• Include comparison to baseline or previous periods
• Display deployment markers for correlation
• Add annotations for significant events
• Include links to related dashboards and logs

Monitoring Tools Integration

Data Collection

• Use agents or sidecars for metric collection
• Implement service discovery for dynamic environments
• Configure appropriate scrape intervals
• Use push vs pull based on use case
• Ensure metric cardinality is manageable

Data Storage and Retention

• Set retention periods based on use case
• Implement downsampling for long-term storage
• Use appropriate storage backends for scale
• Plan for disaster recovery of monitoring data
• Monitor your monitoring infrastructure

Health Checks and Probes

• Implement liveness probes for crash detection
• Use readiness probes for traffic management
• Create deep health checks that verify dependencies
• Expose health endpoints in a standard format
• Monitor health check latency as a metric

Incident Response

• Use monitoring data to detect incidents early
• Correlate metrics, logs, and traces during investigation
• Document findings and update monitoring post-incident
• Track MTTR (Mean Time to Recovery) metrics
• Conduct regular monitoring reviews and improvements

Capacity Planning

• Track resource utilization trends
• Set alerts for approaching capacity limits
• Use forecasting for proactive scaling
• Document capacity requirements and headroom
• Review capacity quarterly