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AWS CloudWatch Anomalie-Untersucher

Untersucht CloudWatch-Metrik-Anomalien mithilfe der AWS SDK CloudWatch.getMetricData- und Logs.filterLogEvents-APIs. Korreliert Metrikspitzen mit Log-Mustern und Bereitstellungsereignissen von CodeDeploy.

Was ist AWS CloudWatch Anomalie-Untersucher?

Der AWS CloudWatch Anomalie-Untersucher ist ein KI-Agenten-Skill, der speziell für die Fehlerbehebung und Analyse von CloudWatch-Metrik-Anomalien entwickelt wurde. Er nutzt die CloudWatch.getMetricData-API, um ungewöhnliche Metrikspitzen zu identifizieren, und die Logs.filterLogEvents-API, um zugehörige Log-Einträge zu durchsuchen. Darüber hinaus korreliert er diese Anomalien mit Bereitstellungsereignissen von CodeDeploy, um Kontext zu liefern, ob eine kürzliche Bereitstellung die Ursache sein könnte. Der Skill ist ideal für DevOps-Ingenieure und SREs, die AWS-Umgebungen betreiben, und lässt sich nahtlos in KI-Codierungsassistenten wie Claude Code, Cursor oder Codex integrieren. Die Hauptvorteile sind eine schnellere Root-Cause-Analyse und die Automatisierung von Untersuchungsabläufen, die sonst manuell in der AWS-Konsole durchgeführt werden müssten.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/aws-cloudwatch-anomaly-investigator
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Dokumentation

Was macht AWS CloudWatch Anomalie-Untersucher?

Untersucht CloudWatch-Metrik-Anomalien mithilfe der AWS SDK CloudWatch.getMetricData- und Logs.filterLogEvents-APIs. Korreliert Metrikspitzen mit Log-Mustern und Bereitstellungsereignissen von CodeDeploy.

Installation

Nutzen Sie den upstream-Installations- oder Einrichtungspfad, der Ihrer Umgebung entspricht:

  • Lassen Sie uns ein Projekt einrichten, das von DynamoDB aus dem SDK abhängt und einen einfachen Service-Aufruf tätigt. Die folgenden Schritte verwenden yarn als Beispiel. Diese Schritte setzen voraus, dass Node.js und yarn bereits installiert sind.
  • git clone https://github.com/aws/aws-sdk-js-v3.git
  • yarn && yarn test:all
  • yarn pack .

Anforderungen und Hinweise von upstream:

Hinweise zur grundlegenden Nutzung oder zum Einstieg:

Quelle

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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act Local GitHub Actions Runner

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Use AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.

Add Claude design-system and accessibility workflows with UX/UI

Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

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Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

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Add multimodal lifelong memory to MCP and Python agents with Sim

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Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

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Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

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