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使用 Cloudsplaining 审计 AWS IAM 策略中的风险权限

在 AWS 策略变更进入生产环境前,使用 Cloudsplaining 标记权限提升路径和过宽的 IAM 权限,帮助代理识别并阻止风险。

使用 Cloudsplaining 审计 AWS IAM 策略中的风险权限 是什么?

该技能使 AI 代理能够利用 Cloudsplaining 工具审计 AWS IAM 策略中的风险权限。技能的核心功能是检测特权升级路径、过度宽松的 IAM 权限以及潜在的信任边界漏洞,帮助开发者在策略变更部署到生产环境前提前发现安全隐患。它支持分析 AWS IAM 策略 JSON 文件或直接从 AWS 账户获取数据。该技能可用于多种平台,包括 Claude Code、Cursor 和 Codex 等 AI 代理环境。工作原理是通过执行 Cloudsplaining 扫描,生成详细的风险报告,并可灵活配置排除规则以过滤误报。其分析结果可帮助开发者遵循最小权限原则,显著降低 AWS 环境的安全风险。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/audit-aws-iam-policies-for-risky-permissions-with-cloudsplaining

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使用 Cloudsplaining 审计 AWS IAM 策略中的风险权限 是做什么的?

在 AWS 策略变更进入生产环境前,使用 Cloudsplaining 标记权限提升路径和过宽的 IAM 权限,帮助代理识别并阻止风险。

前提条件

Python 3、AWS IAM 策略 JSON 或账户数据,以及 Cloudsplaining。

安装

使用适合您环境的上游安装或设置路径:

来自上游的要求和注意事项:

  • Python Version
  • 您必须具有运行 iam:GetAccountAuthorizationDetails 的权限。arn:aws:iam::aws:policy/SecurityAudit 策略…
  • default-iam-results.json:包含报告的原始 JSON 输出。您可以使用此数据文件对扫描结果进行各种处理。例如,您可以编写一个 Python 脚本,解析该文件…

基本使用或入门说明:

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来源

Individual skills in this repo

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

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ACF to Gutenberg Block Migrator

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Activepieces Open Source Workflow Automation

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ActivityWatch Cross-Platform Automated Time Tracker and Producti

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act Local GitHub Actions Runner

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

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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

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

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Add persistent project memory and hook-driven synthesis with Cla

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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.

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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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Calls Adobe Firefly

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