AWS Observability
OfficialFreeOptimize AWS observability with comprehensive tooling.
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What AWS Observability does
The AWS Observability skill provides developers and DevOps engineers with a suite of tools for enhancing and managing observability within AWS environments. It focuses on integrating services with Application Signals using the AWS Distro for OpenTelemetry (ADOT) auto-instrumentation SDKs. This skill is particularly beneficial for those working with AWS services such as EC2, ECS, EKS, and Lambda, and supports multiple programming languages including Python, Node.js, Java, and .NET. By leveraging this skill, users can efficiently enable and onboard services to Application Signals, ensuring that metrics, logs, and traces are properly captured and utilized.
This skill is designed to work seamlessly with the AWS MCP server, allowing users to execute CLI commands, query CloudWatch, and validate configurations directly. It encompasses a wide range of observability aspects, including setting up CloudWatch alarms, creating dashboards, and configuring X-Ray tracing. Additionally, it provides guidance on writing Log Insights queries, managing custom metrics, and conducting operational audits with CloudTrail. The included reference files offer detailed instructions for various observability tasks, ensuring that users can find the information they need quickly and effectively.
For developers needing to debug and optimize their applications, the AWS Observability skill includes features for dynamic instrumentation, allowing for live debugging of running services without redeployment. This capability is crucial for identifying and resolving issues in real-time, making it an essential tool for maintaining high availability and performance in production environments. Overall, this skill serves as a comprehensive resource for anyone looking to enhance their AWS observability practices and ensure their applications are performing optimally.
When to use it
Use this skill when you need to onboard AWS services to Application Signals or configure observability features like alarms and dashboards.
When not to use it
This skill is not suitable for application logging or security threat detection tasks.
What you can build with it
Onboarding a New Service to Application Signals
Quickly enable a new service for Application Signals using the auto-instrumentation features provided by ADOT.
Setting Up CloudWatch Alarms
Configure metric, composite, and anomaly detection alarms to monitor application performance effectively.
Debugging a Running Application
Utilize dynamic instrumentation to capture live data and debug issues in a running service without redeploying.
How to install AWS Observability
View source1. Install with the skills CLI
npx skills add aws/agent-toolkit-for-aws/aws-observability --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by awsAWS Observability
Overview
Domain expertise for AWS observability across metrics, logs, and traces, covering the full lifecycle: enabling/onboarding a service to Application Signals using ADOT (AWS Distro for OpenTelemetry) auto-instrumentation SDKs and ServiceEvents — making the service show up in Application Signals — on EC2, ECS, EKS, and Lambda in Python, Node.js, Java, and .NET.
Works best with the AWS MCP server — enables running CLI commands, querying CloudWatch, and validating configurations directly. All guidance also works with standard AWS CLI access.
Note: Reference files contain specific runtime versions, quota values, and feature matrices that may change. When precision matters (e.g., deploying to production, choosing a runtime, or checking a quota), confirm values against current AWS documentation rather than relying solely on the values in these files.
Routing
| User need | Action |
|---|---|
| Enabling/onboarding a service to Application Signals (auto-instrumentation) | Read application-signals-onboarding.md |
| Propagating ServiceEvents git/deployment metadata through CI/CD | Read application-signals-cicd-metadata.md |
| Per-platform/per-language enablement steps | Read the matching references/appsignals-guides/<platform>-<language>.md (e.g. eks-python.md) |
| Writing Log Insights queries | Read log-insights.md |
| Configuring alarms (metric, composite, anomaly) | Read alarms.md |
| Publishing custom metrics or using EMF | Read metrics.md |
| Setting up X-Ray tracing or ADOT | Read tracing.md |
| Building dashboards | Read dashboards.md |
| Debugging observability issues | Read troubleshooting.md — starts with the 5 most common fixes |
| Debugging canary failures | Read synthetics.md — see Common failures table |
| CloudTrail operational auditing | Read cloudtrail.md |
| Setting up Lambda monitoring with CDK | Use alarm-template.ts as a starting point |
| Creating synthetic canaries | Read synthetics.md |
| Configuring ADOT collector | Use otel-config.yaml as a starting point |
| Debugging a running service with breakpoints/snapshots — Dynamic Instrumentation (modifies live services and capture live data) | Read dynamic-instrumentation.md in full before acting. Confirm with the user before any create/delete, and narrate before significant actions: observation → hypothesis → proposed action → expected result. Diagnosing running-service root cause from source/code inspection. Source inspection alone identifies hypotheses, not confirmed root causes. Keep suspected causes tentative until runtime evidence confirms them. |
| Spans multiple areas | Read the most specific reference first, then consult others as needed |
Files
| File | Content |
|---|---|
| application-signals-onboarding.md | Enable Application Signals auto-instrumentation: EKS add-on, CloudWatch Agent IAM, OTLP endpoints, ServiceEvents env vars, Dynamic Instrumentation — two-tier scope by platform/language |
| application-signals-cicd-metadata.md | ServiceEvents git & deployment metadata propagation through CI/CD (the 5 OTEL_AWS_SERVICE_EVENTS_* vars) |
references/appsignals-guides/ (e.g. eks-python.md) | 16 per-platform × per-language enablement guides (EC2/ECS/EKS/Lambda × Python/Node.js/Java/.NET) |
| alarms.md | Metric, composite, anomaly detection alarms — configuration, constraints, recommended defaults |
| log-insights.md | Complete query syntax, commands, functions, known issues, reusable query library |
| metrics.md | Custom metrics, EMF spec, metric filters, high-resolution, retention |
| tracing.md | X-Ray → ADOT migration, sampling rules, annotations vs metadata, collector config |
| dashboards.md | Widget types, cross-account/region, dynamic labels, sharing |
| troubleshooting.md | Error → cause → fix for all observability services |
| cloudtrail.md | Operational auditing, event types, S3+Athena queries |
| synthetics.md | Canary runtime/blueprint constraints, VPC networking, common failures |
| alarm-template.ts | Best-practice CDK Lambda monitoring (alarms + dashboard) |
| otel-config.yaml | ADOT collector config for X-Ray traces + CloudWatch EMF metrics |
| dynamic-instrumentation.md | Dynamic Instrumentation debugging loop — breakpoints/probes on live code, snapshot capture + correlation analysis, create/delete gating, snapshot PII handling. Runs via scripts/di_instrumentation.py + scripts/di_snapshots.py. |
Frequently asked questions about AWS Observability
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