Aws Observability

OrganizationPopular

aws

aws-observability

Builds, configures, debugs, and optimizes AWS observability with CloudWatch (Log Insights, Metrics, Alarms, Dashboards, EMF), X-Ray, CloudTrail, and ADOT (AWS Distro for OpenTelemetry), AND enables/onboards services to Application Signals using ADOT auto-instrumentation SDKs. Covers Log Insights queries, alarms (metric, composite, anomaly), dashboards, custom metrics/EMF, X-Ray tracing and sampling, ADOT collector config, CloudTrail auditing, and end-to-end Application Signals enablement via ADOT SDKs (CloudWatch Observability EKS add-on, CloudWatch Agent IAM, OTLP endpoints, ServiceEvents, Dynamic Instrumentation), breakpoint and snapshot in Dynamic Instrumentation, live data capture in running service, debug without redeploying. Applies to CloudWatch, alarms, dashboards, EMF, X-Ray, traces, CloudTrail, ADOT, monitoring, synthetics/canaries, OR enabling/onboarding/instrumenting a service for Application Signals. Not for app logging or security threat detection.

Overview

Publisher	aws
Repository	`agent-toolkit-for-aws`
Skill name	`aws-observability`
Stars	2.7K
Forks	311
Bundled files	55
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
55 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by aws on GitHub. Read the source before you install it.

Installation

Install the Aws Observability AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-core/skills/aws-observability

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git /tmp/agent-toolkit-for-aws
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit-for-aws/plugins/aws-core/skills/aws-observability .claude/skills/aws-observability

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Observability in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Observability on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Observability is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AWS 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`.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aws Observability AI skill do?

Why use Aws Observability on TypingMind?

Because you install it once and use it with any model. Aws Observability is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Observability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-core/skills/aws-observability. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aws Observability?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Observability?

As many as you like. As long as a model supports skills, you can use Aws Observability with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Observability AI skill free?

Yes. It is published on GitHub by aws under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Implement

Implement a piece of work based on a spec or set of tickets.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

AWS Observability

Overview

Routing

Files

Bundled files

Frequently asked questions

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