aio-monitoring-observabilitylisted

# Monitoring & Observability ```bash 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 --- ## 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" ``` --- ## 1. Desig