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aws-emr-skillsAWS EMR skills 部署

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

4,967

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199

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下载量

1,608
OpenClaw

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

MIT-0

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-emr-skills(AWS EMR skills 部署)
来源仓库:https://github.com/yhyyz/aws-emr-skills
安装命令:
openclaw skills install aws-emr-skills
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install aws-emr-skills

简介

统一管理 EMR Serverless、EC2 和 EKS 三种部署模式的 Spark/Hive 作业。

  • 简化大数据处理任务的提交、监控与资源调度流程。
  • 支持跨集群作业管理与日志聚合查询,提升运维效率。
  • 操作前需确认目标 VPC 网络连通性与安全组规则设置。
  • aws-emr-skills 属于运维类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
aws-emr-skills
description
|
version
2.0.0
metadata
openclaw
requires
env
bins
primaryEnv
AWS_REGION
emoji
homepage
https://github.com/yhyyz/aws-emr-skills

AWS EMR Skills

A Python skill for interacting with AWS EMR across three deployment modes: EMR Serverless, EMR on EC2, and EMR on EKS. Submit Spark and Hive jobs, manage clusters and applications, monitor job status, and retrieve logs.

When to Use (Trigger Phrases)

Invoke this skill when the user mentions:

"Submit a Spark job on EMR"
"List EMR Serverless applications"
"Add a step to my EMR cluster"
"Get EMR job logs"
"Check EMR job status"
"Cancel running EMR job"
"List EMR clusters"
"Create an EMR on EKS virtual cluster"
"Submit PySpark to EMR Serverless"
"Get step logs from EMR cluster"

Any request involving EMR Serverless applications/jobs, EMR on EC2 clusters/steps, or EMR on EKS virtual clusters/job runs.

Feature List

EMR Serverless

  • Applications: List, describe, start, stop EMR Serverless applications
  • Job Submission: Submit Spark SQL, Spark JAR, PySpark, and Hive jobs (sync/async)
  • Job Lifecycle: Get status, cancel, list job runs
  • Results: Retrieve SQL query results from S3
  • Logs: Get driver stdout/stderr logs with secret masking

EMR on EC2

  • Clusters: List, describe, terminate EMR clusters
  • Step Submission: Add Spark, PySpark, and Hive steps via command-runner.jar
  • Step Lifecycle: List, describe, cancel steps
  • Logs: Get step logs (stderr, stdout, controller, syslog) from S3

EMR on EKS

  • Virtual Clusters: List, describe, create, delete virtual clusters
  • Job Submission: Submit Spark and Spark SQL jobs to EKS
  • Job Lifecycle: Describe, list, cancel job runs
  • Logs: Get job logs from S3

Initial Setup

  1. Python 3.8+ with boto3>=1.26.0:
   pip install boto3>=1.26.0
  1. AWS credentials via boto3 default chain (env vars, config files, IAM roles).
  1. Environment variables (all optional, validated at point of use):
   export AWS_REGION="us-east-1"

   # EMR Serverless
   export EMR_SERVERLESS_APP_ID="00abcdef12345678"
   export EMR_SERVERLESS_EXEC_ROLE_ARN="arn:aws:iam::123456789:role/emr-role"
   export EMR_SERVERLESS_S3_LOG_URI="s3://my-bucket/emr-logs/"

   # EMR on EC2
   export EMR_CLUSTER_ID="j-XXXXXXXXXXXXX"

   # EMR on EKS
   export EMR_EKS_VIRTUAL_CLUSTER_ID="abc123def456"
   export EMR_EKS_EXEC_ROLE_ARN="arn:aws:iam::123456789:role/emr-eks-role"

How to Manage EMR

1. EMR Serverless

Fully managed serverless Spark/Hive execution. No infrastructure to manage.

  • Application management: scripts/on_serverless/emr_serverless_cli.py — 14 @tool functions
  • Detailed guide: references/emr_serverless/application_guide.md — Application lifecycle
  • Detailed guide: references/emr_serverless/job_guide.md — Job submission, results, logs

2. EMR on EC2

Traditional EMR clusters on EC2 instances. Submit work as Steps.

  • Cluster & step management: scripts/on_ec2/emr_on_ec2_cli.py — 10 @tool functions
  • Detailed guide: references/emr_on_ec2/cluster_guide.md — Cluster lifecycle
  • Detailed guide: references/emr_on_ec2/step_guide.md — Step submission, logs

3. EMR on EKS

Spark workloads on Amazon EKS via the emr-containers API.

  • Virtual cluster & job management: scripts/on_eks/emr_on_eks_cli.py — 10 @tool functions
  • Detailed guide: references/emr_on_eks/virtual_cluster_guide.md — Virtual cluster lifecycle
  • Detailed guide: references/emr_on_eks/job_run_guide.md — Job submission, logs

Available Scripts

ScriptDescription
scripts/on_serverless/emr_serverless_cli.pyEMR Serverless @tool functions (14 tools)
scripts/on_ec2/emr_on_ec2_cli.pyEMR on EC2 @tool functions (10 tools)
scripts/on_eks/emr_on_eks_cli.pyEMR on EKS @tool functions (10 tools)
scripts/config/emr_config.pyUnified configuration management
scripts/client/boto_client.pyboto3 client factory

References

DocumentDescription
references/emr_serverless/application_guide.mdEMR Serverless application management guide
references/emr_serverless/job_guide.mdEMR Serverless job submission and management guide
references/emr_on_ec2/cluster_guide.mdEMR on EC2 cluster management guide
references/emr_on_ec2/step_guide.mdEMR on EC2 step submission and management guide
references/emr_on_eks/virtual_cluster_guide.mdEMR on EKS virtual cluster management guide
references/emr_on_eks/job_run_guide.mdEMR on EKS job run management guide

Requirements

  • When writing temporary files (scripts, notes, etc.), place them in the ./tmp folder.
  • When importing scripts packages, add the skill root to path: sys.path.append(${emr_skill_root})
  • AWS credentials are handled by boto3's default credential chain — never pass access keys directly.
  • All configuration environment variables are optional and validated at the point of use.

Data Privacy & Trust

  • No credential storage: AWS credentials are resolved via boto3 default chain. No keys are stored or logged.
  • Secret masking: Log retrieval functions automatically mask potential AWS credentials in output.
  • Read-only by default: Most operations are read-only queries. Write operations (job submission, cluster termination) require explicit user action.

External Endpoints

This skill connects to:

  • AWS EMR Serverless API (emr-serverless.{region}.amazonaws.com)
  • AWS EMR API (elasticmapreduce.{region}.amazonaws.com)
  • AWS EMR Containers API (emr-containers.{region}.amazonaws.com)
  • AWS S3 API (s3.{region}.amazonaws.com) — for log and result retrieval

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

80.25%
按下载量换算1,290

安全审计

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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。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。

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