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AWS CloudTrail Log Normalizer

Normalise et enrichit les logs JSON AWS CloudTrail au format OCSF (Open Cybersecurity Schema Framework). Associe les paires eventSource/eventName aux identifiants de techniques MITRE ATT&CK via l'API STIX MITRE ATT&CK.

Qu'est-ce que AWS CloudTrail Log Normalizer ?

Cette compétence transforme les logs bruts AWS CloudTrail au format JSON en un format standardisé OCSF, ce qui facilite leur analyse et leur intégration dans des pipelines de sécurité. Elle enrichit également chaque événement en mappant les paires eventSource/eventName aux identifiants des techniques MITRE ATT&CK en interrogeant l'API STIX. Cette capacité est essentielle pour les équipes de sécurité et les analystes SOC qui souhaitent automatiser la détection des menaces et la réponse aux incidents. Elle fonctionne avec n'importe quel agent compatible (Claude Code, Cursor, etc.) et peut être utilisée dans des workflows CI/CD ou des environnements d'analyse de logs. L'installation nécessite Node.js et yarn, et le code source est disponible sur GitHub. En résumé, cet outil comble le fossé entre les logs cloud bruts et un cadre de cybersécurité structuré et exploitable.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/aws-cloudtrail-log-normalizer
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Documentation

Normalisateur de logs AWS CloudTrail

Normalise et enrichit les logs JSON AWS CloudTrail au format OCSF (Open Cybersecurity Schema Framework). Associe les paires eventSource/eventName aux identifiants de techniques MITRE ATT&CK via l'API STIX MITRE ATT&CK.

Installation

Utilisez le chemin d'installation ou de configuration en amont correspondant à votre environnement :

  • Parcourons ensemble la création d'un projet dépendant de DynamoDB à partir du SDK et effectuons un appel de service simple. Les étapes suivantes utilisent yarn comme exemple. Ces étapes supposent que Node.js et yarn sont déjà installés.
  • git clone https://github.com/aws/aws-sdk-js-v3.git
  • yarn && yarn test:all
  • yarn pack .

Exigences et mises en garde de la source amont :

Notes d'utilisation de base ou de démarrage :

Source

Individual skills in this repo

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

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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.

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

Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

Add managed long-term memory to LangGraph agents with LangMem

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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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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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