aio-monitoring-observability

Monitoring & Observability

SCRIPTS="${CLAUDE_PLUGIN_ROOT}/skills/aio-monitoring-observability/scripts"

Environment

• python3: !python3 --version 2>/dev/null || echo "NOT INSTALLED"

Overview

This skill provides comprehensive guidance for monitoring and observability workflows including metrics design, log aggregation, distributed tracing, alerting strategies, SLO/SLA management, and tool selection.

When to use this skill:

• Setting up monitoring for new services
• Designing alerts and dashboards
• Troubleshooting performance issues
• Implementing SLO tracking and error budgets
• Choosing between monitoring tools
• Integrating OpenTelemetry instrumentation
• Analyzing metrics, logs, and traces
• Optimizing Datadog costs and finding waste
• Migrating from Datadog to open-source stack

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Core Workflow: Observability Implementation

Use this decision tree to determine your starting point:

Are you setting up monitoring from scratch?
├─ YES → Start with "1. Design Metrics Strategy"
└─ NO → Do you have an existing issue?
    ├─ YES → Go to "9. Troubleshooting & Analysis"
    └─ NO → Are you improving existing monitoring?
        ├─ Alerts → Go to "3. Alert Design"
        ├─ Dashboards → Go to "4. Dashboard & Visualization"
        ├─ SLOs → Go to "5. SLO & Error Budgets"
        ├─ Tool selection → Read references/tool_comparison.md
        └─ Using Datadog? High costs? → Go to "7. Datadog Cost Optimization & Migration"

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1. Design Metrics Strategy

Start with The Four Golden Signals

Every service should monitor:

1. Latency: Response time (p50, p95, p99)
2. Traffic: Requests per second
3. Errors: Failure rate
4. Saturation: Resource utilization

For request-driven services, use the RED Method:

• Rate: Requests/sec
• Errors: Error rate
• Duration: Response time

For infrastructure resources, use the USE Method:

• Utilization: % time busy
• Saturation**: Queue depth
• Errors**: Error count