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AWS CloudFormation Stack Diagnostics

Diagnostiziert fehlgeschlagene AWS CloudFormation Stack-Operationen mithilfe der AWS CLI (aws cloudformation describe-stack-events) und des cfn-lint Validators. Verfolgt Ressourcen-Erstellungsfehler, Rollback-Ursachen und Abhängigkeitsketten verschachtelter Stacks.

Was ist AWS CloudFormation Stack Diagnostics?

Diese Fähigkeit für KI-Agenten (z. B. Claude Code, Cursor, Codex) hilft bei der Diagnose von Problemen in AWS CloudFormation Stacks, die während der Erstellung, Aktualisierung oder Löschung fehlschlagen. Sie nutzt die AWS CLI (`aws cloudformation describe-stack-events`), um detaillierte Ereignisprotokolle abzurufen und die Ursache von Rollbacks oder fehlgeschlagenen Ressourcen zu identifizieren. Zusätzlich wird der `cfn-lint` Validator eingesetzt, um Vorlagenfehler zu erkennen und die Konformität zu prüfen. Die Fähigkeit analysiert auch Abhängigkeitsketten bei verschachtelten Stacks und bietet konkrete Handlungsempfehlungen zur Behebung. Sie eignet sich besonders für Entwickler und DevOps-Ingenieure, die schnell die Fehlerursache in komplexen CloudFormation-Stack-Operationen ermitteln möchten.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/aws-cloudformation-stack-diagnostics
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Dokumentation

Was macht AWS CloudFormation Stack Diagnostics?

Diagnostiziert fehlgeschlagene AWS CloudFormation Stack-Operationen mithilfe der AWS CLI (aws cloudformation describe-stack-events) und des cfn-lint Validators. Verfolgt Ressourcen-Erstellungsfehler, Rollback-Ursachen und Abhängigkeitsketten verschachtelter Stacks.

Installation

Anforderungen und Hinweise aus dem Upstream:

  • Ressourcen, die Drift-Erkennung unterstützen und Anhänge erfordern oder zulassen

Grundlegende Nutzungs- oder Einstiegshinweise:

Dokumentation

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

Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.

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.

Add IDE-style semantic code navigation and symbol-safe refactors

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Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

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.

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