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研究检索敏感数据github未标认证来源可访问许可证需确认审计异常

aws-agentic-aiAWS agentic AI 搜索

Agent Skill

用于辅助云资源、部署、容器、基础设施和运维自动化任务。它适合让 Agent 检查配置、整理部署步骤、分析资源状态、生成排障思路或辅助云服务接入。使用时需要明确目标环境、账号权限、区域和资源组,区分本地测试与生产操作;涉及删除资源、重启服务、修改网络或权限配置时,应先确认影响范围。

总安装

261

周安装

11

GitHub Stars

46

下载量

92
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:aws-agentic-ai(AWS agentic AI 搜索)
来源仓库:https://github.com/commandcodeai/agent-skills
仓库路径:skills/aws-agentic-ai
安装命令:
npx skills add https://github.com/commandcodeai/agent-skills --skill aws-agentic-ai
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/commandcodeai/agent-skills --skill aws-agentic-ai

简介

协助在 AWS Bedrock AgentCore 上部署与扩展 AI 智能体。

  • 涵盖服务选型、架构设计与集成工作流的完整指导。
  • 必须配合 AWS MCP 工具获取最新文档,确保信息准确性。
  • 操作前需明确账号权限与环境边界,防止越权访问资源。
  • aws-agentic-ai 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

AWS Bedrock AgentCore

AWS Bedrock AgentCore provides a complete platform for deploying and scaling AI agents with seven core services. This skill guides you through service selection, deployment patterns, and integration workflows using AWS CLI.

AWS Documentation Requirement

CRITICAL: This skill requires AWS MCP tools for accurate, up-to-date AWS information.

Before Answering AWS Questions

  1. Always verify using AWS MCP tools (if available):

- mcp__aws-mcp__aws___search_documentation or mcp__*awsdocs*__aws___search_documentation - Search AWS docs - mcp__aws-mcp__aws___read_documentation or mcp__*awsdocs*__aws___read_documentation - Read specific pages - mcp__aws-mcp__aws___get_regional_availability - Check service availability

  1. If AWS MCP tools are unavailable:

- Guide user to configure AWS MCP: See AWS MCP Setup Guide - Help determine which option fits their environment: - Has uvx + AWS credentials → Full AWS MCP Server - No Python/credentials → AWS Documentation MCP (no auth) - If cannot determine → Ask user which option to use

When to Use This Skill

Use this skill when you need to:

  • Deploy REST APIs as MCP tools for AI agents (Gateway)
  • Execute agents in serverless runtime (Runtime)
  • Add conversation memory to agents (Memory)
  • Manage API credentials and authentication (Identity)
  • Enable agents to execute code securely (Code Interpreter)
  • Allow agents to interact with websites (Browser)
  • Monitor and trace agent performance (Observability)

Available Services

ServiceUse ForDocumentation
GatewayConverting REST APIs to MCP toolsservices/gateway/README.md
RuntimeDeploying and scaling agentsservices/runtime/README.md
MemoryManaging conversation stateservices/memory/README.md
IdentityCredential and access managementservices/identity/README.md
Code InterpreterSecure code execution in sandboxesservices/code-interpreter/README.md
BrowserWeb automation and scrapingservices/browser/README.md
ObservabilityTracing and monitoringservices/observability/README.md

Common Workflows

Deploying a Gateway Target

MANDATORY - READ DETAILED DOCUMENTATION: See services/gateway/README.md for complete Gateway setup guide including deployment strategies, troubleshooting, and IAM configuration.

Quick Workflow:

  1. Upload OpenAPI schema to S3
  2. *(API Key auth only)* Create credential provider and store API key
  3. Create gateway target linking schema (and credentials if using API key)
  4. Verify target status and test connectivity
Note: Credential provider is only needed for API key authentication. Lambda targets use IAM roles, and MCP servers use OAuth.

Managing Credentials

MANDATORY - READ DETAILED DOCUMENTATION: See cross-service/credential-management.md for unified credential management patterns across all services.

Quick Workflow:

  1. Use Identity service credential providers for all API keys
  2. Link providers to gateway targets via ARN references
  3. Rotate credentials quarterly through credential provider updates
  4. Monitor usage with CloudWatch metrics

Monitoring Agents

MANDATORY - READ DETAILED DOCUMENTATION: See services/observability/README.md for comprehensive monitoring setup.

Quick Workflow:

  1. Enable observability for agents
  2. Configure CloudWatch dashboards for metrics
  3. Set up alarms for error rates and latency
  4. Use X-Ray for distributed tracing

Service-Specific Documentation

For detailed documentation on each AgentCore service, see the following resources:

Gateway Service

Runtime, Memory, Identity, Code Interpreter, Browser, Observability

Each service has comprehensive documentation in its respective directory:

Cross-Service Resources

For patterns and best practices that span multiple AgentCore services:

Additional Resources

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.19%
按下载量换算31

Claude

31.71%
按下载量换算29

Cursor

18.62%
按下载量换算17

Gemini CLI

9.07%
按下载量换算8

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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