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Found 3 Skills
Builds, configures, debugs, and optimizes AWS observability using CloudWatch (Logs Insights, Metrics, Alarms, Dashboards, EMF), X-Ray, CloudTrail, and ADOT. Covers Log Insights query syntax (fields, filter, stats, parse, pattern, join, subqueries), alarm configuration (metric, composite, anomaly detection, missing data treatment), dashboard design, custom metrics (PutMetricData, EMF, metric filters), X-Ray tracing (ADOT, sampling rules, annotations vs metadata), ADOT collector config, and CloudTrail auditing. Use when the user mentions CloudWatch, Log Insights, alarms, INSUFFICIENT_DATA, dashboards, custom metrics, EMF, X-Ray, traces, sampling, CloudTrail, who deleted, ADOT, OpenTelemetry, observability, monitoring, synthetics, canaries, or troubleshooting alarm behavior. Do NOT use for application logging setup, container log drivers, or security threat detection.
Sets up CloudWatch Application Observability (also called Omni) for the first time. Covers creating an Omni Space or Domain; configuring and listing Omni access grants (who has access and at what level) and access profiles bounding what async alerts, integrations, or agents can do; instrumenting an application or AI agent with the ADOT SDK so traces reach CloudWatch Omni (Python/Node/Java/.NET on EC2/ECS/EKS/Lambda) — CloudWatch Omni only; for Application Signals (auto-instrumentation, monitored service, ServiceEvents, reporting telemetry) use aws-observability — including no-image-rebuild and .NET CoreCLR profiler env vars; ingesting Azure telemetry via the CloudWatch agent on an Azure VM or AKS cluster; connecting Slack to a Space (and whether GitHub can be connected); and registering a custom MCP tool server over HTTP or stdio and choosing its auth (API key, bearer, or OAuth2). For using a Space already set up — querying, dashboards, Omni alerts, or defining Omni resources as code — use aws-observability.
AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.