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Agent Skill · 开发

ministack-aws-emulator(ministack AWS emulator 部署)

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。

ministack-aws-emulator 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 3,137。

总安装 12,582周安装 509GitHub Stars 39下载 3,950核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:ministack-aws-emulator(ministack AWS emulator 部署)
来源仓库:https://github.com/aradotso/trending-skills
仓库路径:skills/ministack-aws-emulator
安装命令:
npx skills add https://github.com/aradotso/trending-skills --skill ministack-aws-emulator
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/aradotso/trending-skills --skill ministack-aws-emulator

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于辅助云资源、部署、容器、基础设施和运维自动化任务。适合检查配置、整理部署步骤、分析资源状态或生成排障思路。使用时需明确目标环境、账号权限、区域和资源组,区分本地测试与生产操作。安装方式:github,通过 npx skills add 命令从 aradotso/trending-skills 仓库添加。

适用于 Codex、Claude、Cursor、Gemini CLI 等宿主环境。

来源仓库内路径:skills/ministack-aws-emulator

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/aradotso/trending-skills --skill ministack-aws…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ministack-aws-emulator 怎么安装?

在终端执行 npx skills add https://github.com/aradotso/trending-skills --skill ministack-aws-emulator。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ministack-aws-emulator 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ministack-aws-emulator 需要什么权限?

权限级别是接触敏感数据。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。

ministack-aws-emulator 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ministack-aws-emulator 用的人多吗?

公开数据是累计安装 12,582 次,近一周安装 509 次,下载 3,950 次,GitHub 39 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ministack-aws-emulator 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
ministack-aws-emulator

ministack AWS emulator 部署 · 本页

12,58239接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

MiniStack AWS Emulator

Skill by ara.so — Daily 2026 Skills collection.

MiniStack is a free, MIT-licensed drop-in replacement for LocalStack that emulates 25+ AWS services (S3, SQS, DynamoDB, Lambda, SNS, IAM, STS, Kinesis, EventBridge, SecretsManager, SSM, CloudWatch, SES, and more) on a single port (4566). No account, no API key, no telemetry. Works with boto3, AWS CLI, Terraform, CDK, and any SDK.


Installation

Option 1: PyPI (simplest)

pip install ministack
ministack
# Server runs at http://localhost:4566
# Change port: GATEWAY_PORT=5000 ministack

Option 2: Docker Hub

docker run -p 4566:4566 nahuelnucera/ministack

Option 3: Docker Compose (from source)

git clone https://github.com/Nahuel990/ministack
cd ministack
docker compose up -d

Verify it's running

curl http://localhost:4566/_localstack/health

Configuration

Environment VariableDefaultDescription
GATEWAY_PORT4566Port to listen on
S3_PERSIST0Set to 1 to persist S3 data to disk

AWS CLI Usage

# Set credentials (any non-empty values work)
export AWS_ACCESS_KEY_ID=test
export AWS_SECRET_ACCESS_KEY=test
export AWS_DEFAULT_REGION=us-east-1

# S3
aws --endpoint-url=http://localhost:4566 s3 mb s3://my-bucket
aws --endpoint-url=http://localhost:4566 s3 cp ./file.txt s3://my-bucket/
aws --endpoint-url=http://localhost:4566 s3 ls s3://my-bucket

# SQS
aws --endpoint-url=http://localhost:4566 sqs create-queue --queue-name my-queue
aws --endpoint-url=http://localhost:4566 sqs list-queues

# DynamoDB
aws --endpoint-url=http://localhost:4566 dynamodb list-tables
aws --endpoint-url=http://localhost:4566 dynamodb create-table \
  --table-name Users \
  --attribute-definitions AttributeName=userId,AttributeType=S \
  --key-schema AttributeName=userId,KeyType=HASH \
  --billing-mode PAY_PER_REQUEST

# STS (identity check)
aws --endpoint-url=http://localhost:4566 sts get-caller-identity

# Use a named profile instead
aws configure --profile local
# Enter: test / test / us-east-1 / json
aws --profile local --endpoint-url=http://localhost:4566 s3 ls

awslocal wrapper (from source)

chmod +x bin/awslocal
./bin/awslocal s3 ls
./bin/awslocal dynamodb list-tables

boto3 Usage Patterns

Universal client factory

import boto3

ENDPOINT = "http://localhost:4566"

def aws_client(service: str):
    return boto3.client(
        service,
        endpoint_url=ENDPOINT,
        aws_access_key_id="test",
        aws_secret_access_key="test",
        region_name="us-east-1",
    )

def aws_resource(service: str):
    return boto3.resource(
        service,
        endpoint_url=ENDPOINT,
        aws_access_key_id="test",
        aws_secret_access_key="test",
        region_name="us-east-1",
    )

S3

s3 = aws_client("s3")

# Create bucket and upload
s3.create_bucket(Bucket="my-bucket")
s3.put_object(Bucket="my-bucket", Key="hello.txt", Body=b"Hello, MiniStack!")

# Download
obj = s3.get_object(Bucket="my-bucket", Key="hello.txt")
print(obj["Body"].read())  # b'Hello, MiniStack!'

# List objects
response = s3.list_objects_v2(Bucket="my-bucket")
for item in response.get("Contents", []):
    print(item["Key"])

# Copy object
s3.copy_object(
    Bucket="my-bucket",
    CopySource={"Bucket": "my-bucket", "Key": "hello.txt"},
    Key="hello-copy.txt",
)

# Enable versioning
s3.put_bucket_versioning(
    Bucket="my-bucket",
    VersioningConfiguration={"Status": "Enabled"},
)

# Presigned URL (works locally)
url = s3.generate_presigned_url(
    "get_object",
    Params={"Bucket": "my-bucket", "Key": "hello.txt"},
    ExpiresIn=3600,
)

SQS

sqs = aws_client("sqs")

# Standard queue
queue = sqs.create_queue(QueueName="my-queue")
queue_url = queue["QueueUrl"]

sqs.send_message(QueueUrl=queue_url, MessageBody='{"event": "user_signup"}')

messages = sqs.receive_message(QueueUrl=queue_url, MaxNumberOfMessages=10)
for msg in messages.get("Messages", []):
    print(msg["Body"])
    sqs.delete_message(QueueUrl=queue_url, ReceiptHandle=msg["ReceiptHandle"])

# FIFO queue
fifo = sqs.create_queue(
    QueueName="my-queue.fifo",
    Attributes={"FifoQueue": "true", "ContentBasedDeduplication": "true"},
)

# Dead-letter queue setup
dlq = sqs.create_queue(QueueName="my-dlq")
dlq_attrs = sqs.get_queue_attributes(
    QueueUrl=dlq["QueueUrl"], AttributeNames=["QueueArn"]
)
sqs.set_queue_attributes(
    QueueUrl=queue_url,
    Attributes={
        "RedrivePolicy": json.dumps({
            "deadLetterTargetArn": dlq_attrs["Attributes"]["QueueArn"],
            "maxReceiveCount": "3",
        })
    },
)

DynamoDB

import json
ddb = aws_client("dynamodb")

# Create table
ddb.create_table(
    TableName="Users",
    KeySchema=[
        {"AttributeName": "userId", "KeyType": "HASH"},
        {"AttributeName": "createdAt", "KeyType": "RANGE"},
    ],
    AttributeDefinitions=[
        {"AttributeName": "userId", "AttributeType": "S"},
        {"AttributeName": "createdAt", "AttributeType": "N"},
    ],
    BillingMode="PAY_PER_REQUEST",
)

# Put / Get / Delete
ddb.put_item(
    TableName="Users",
    Item={
        "userId": {"S": "u1"},
        "createdAt": {"N": "1700000000"},
        "name": {"S": "Alice"},
        "active": {"BOOL": True},
    },
)

item = ddb.get_item(
    TableName="Users",
    Key={"userId": {"S": "u1"}, "createdAt": {"N": "1700000000"}},
)
print(item["Item"]["name"]["S"])  # Alice

# Query
result = ddb.query(
    TableName="Users",
    KeyConditionExpression="userId = :uid",
    ExpressionAttributeValues={":uid": {"S": "u1"}},
)

# Batch write
ddb.batch_write_item(
    RequestItems={
        "Users": [
            {"PutRequest": {"Item": {"userId": {"S": "u2"}, "createdAt": {"N": "1700000001"}, "name": {"S": "Bob"}}}},
        ]
    }
)

# TTL
ddb.update_time_to_live(
    TableName="Users",
    TimeToLiveSpecification={"Enabled": True, "AttributeName": "expiresAt"},
)

SNS + SQS Fanout

sns = aws_client("sns")
sqs = aws_client("sqs")

topic = sns.create_topic(Name="my-topic")
topic_arn = topic["TopicArn"]

queue = sqs.create_queue(QueueName="fan-queue")
queue_attrs = sqs.get_queue_attributes(
    QueueUrl=queue["QueueUrl"], AttributeNames=["QueueArn"]
)
queue_arn = queue_attrs["Attributes"]["QueueArn"]

sns.subscribe(TopicArn=topic_arn, Protocol="sqs", Endpoint=queue_arn)

# Publish — message is fanned out to subscribed SQS queues
sns.publish(TopicArn=topic_arn, Message="hello fanout", Subject="test")

Lambda

import zipfile, io

# Create a zip with handler code
buf = io.BytesIO()
with zipfile.ZipFile(buf, "w") as zf:
    zf.writestr("handler.py", """
def handler(event, context):
    print("event:", event)
    return {"statusCode": 200, "body": "ok"}
""")
buf.seek(0)

lam = aws_client("lambda")

lam.create_function(
    FunctionName="my-function",
    Runtime="python3.12",
    Role="arn:aws:iam::000000000000:role/role",
    Handler="handler.handler",
    Code={"ZipFile": buf.read()},
)

# Invoke synchronously
import json
response = lam.invoke(
    FunctionName="my-function",
    InvocationType="RequestResponse",
    Payload=json.dumps({"key": "value"}),
)
result = json.loads(response["Payload"].read())
print(result)  # {"statusCode": 200, "body": "ok"}

# SQS event source mapping
lam.create_event_source_mapping(
    EventSourceArn=queue_arn,
    FunctionName="my-function",
    BatchSize=10,
    Enabled=True,
)

SecretsManager

sm = aws_client("secretsmanager")

sm.create_secret(Name="db-password", SecretString='{"password":"s3cr3t"}')
secret = sm.get_secret_value(SecretId="db-password")
print(secret["SecretString"])  # {"password":"s3cr3t"}

sm.update_secret(SecretId="db-password", SecretString='{"password":"newpass"}')
sm.delete_secret(SecretId="db-password", ForceDeleteWithoutRecovery=True)

SSM Parameter Store

ssm = aws_client("ssm")

ssm.put_parameter(Name="/app/db/host", Value="localhost", Type="String")
ssm.put_parameter(Name="/app/db/password", Value="secret", Type="SecureString")

param = ssm.get_parameter(Name="/app/db/host")
print(param["Parameter"]["Value"])  # localhost

# Fetch all params under a path
params = ssm.get_parameters_by_path(Path="/app/", Recursive=True)
for p in params["Parameters"]:
    print(p["Name"], p["Value"])

Kinesis

import base64

kin = aws_client("kinesis")

kin.create_stream(StreamName="events", ShardCount=1)
kin.put_record(StreamName="events", Data=b'{"event":"click"}', PartitionKey="user1")

# Get records
shards = kin.list_shards(StreamName="events")
shard_id = shards["Shards"][0]["ShardId"]

iterator = kin.get_shard_iterator(
    StreamName="events",
    ShardId=shard_id,
    ShardIteratorType="TRIM_HORIZON",
)
records = kin.get_records(ShardIterator=iterator["ShardIterator"])
for r in records["Records"]:
    print(base64.b64decode(r["Data"]))

EventBridge

eb = aws_client("events")

# Create a custom bus
eb.create_event_bus(Name="my-bus")

# Put a rule targeting a Lambda
eb.put_rule(
    Name="my-rule",
    EventBusName="my-bus",
    EventPattern='{"source": ["myapp"]}',
    State="ENABLED",
)
eb.put_targets(
    Rule="my-rule",
    EventBusName="my-bus",
    Targets=[{"Id": "1", "Arn": "arn:aws:lambda:us-east-1:000000000000:function:my-function"}],
)

# Emit an event (triggers Lambda target)
eb.put_events(Entries=[{
    "Source": "myapp",
    "DetailType": "UserSignup",
    "Detail": '{"userId": "123"}',
    "EventBusName": "my-bus",
}])

CloudWatch Logs

import time

logs = aws_client("logs")

logs.create_log_group(logGroupName="/app/service")
logs.create_log_stream(logGroupName="/app/service", logStreamName="stream-1")

logs.put_log_events(
    logGroupName="/app/service",
    logStreamName="stream-1",
    logEvents=[
        {"timestamp": int(time.time() * 1000), "message": "App started"},
        {"timestamp": int(time.time() * 1000), "message": "Request received"},
    ],
)

events = logs.get_log_events(
    logGroupName="/app/service",
    logStreamName="stream-1",
)
for e in events["events"]:
    print(e["message"])

# Filter with glob patterns (* and ?), AND terms, -exclusions
filtered = logs.filter_log_events(
    logGroupName="/app/service",
    filterPattern="Request*",
)

Testing Patterns

pytest fixture (recommended)

import pytest
import boto3

MINISTACK_ENDPOINT = "http://localhost:4566"

@pytest.fixture(scope="session")
def aws_endpoint():
    return MINISTACK_ENDPOINT

@pytest.fixture
def s3_client(aws_endpoint):
    return boto3.client(
        "s3",
        endpoint_url=aws_endpoint,
        aws_access_key_id="test",
        aws_secret_access_key="test",
        region_name="us-east-1",
    )

@pytest.fixture
def test_bucket(s3_client):
    bucket = "test-bucket"
    s3_client.create_bucket(Bucket=bucket)
    yield bucket
    # Cleanup
    objs = s3_client.list_objects_v2(Bucket=bucket).get("Contents", [])
    for obj in objs:
        s3_client.delete_object(Bucket=bucket, Key=obj["Key"])
    s3_client.delete_bucket(Bucket=bucket)

def test_upload_download(s3_client, test_bucket):
    s3_client.put_object(Bucket=test_bucket, Key="test.txt", Body=b"hello")
    resp = s3_client.get_object(Bucket=test_bucket, Key="test.txt")
    assert resp["Body"].read() == b"hello"

GitHub Actions CI integration

# .github/workflows/test.yml
name: Test

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    services:
      ministack:
        image: nahuelnucera/ministack
        ports:
          - 4566:4566
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.12"
      - run: pip install -r requirements.txt
      - run: pytest
        env:
          AWS_ACCESS_KEY_ID: test
          AWS_SECRET_ACCESS_KEY: test
          AWS_DEFAULT_REGION: us-east-1
          AWS_ENDPOINT_URL: http://localhost:4566

Using AWS_ENDPOINT_URL env var (boto3 >= 1.28)

import os
import boto3

# If AWS_ENDPOINT_URL is set, boto3 uses it automatically — no endpoint_url kwarg needed
# export AWS_ENDPOINT_URL=http://localhost:4566
s3 = boto3.client("s3")  # picks up AWS_ENDPOINT_URL automatically

Supported Services (25+)

ServiceKey Operations
S3CRUD, multipart, versioning, encryption, lifecycle, CORS, ACL, notifications
SQSStandard & FIFO queues, DLQ, batch ops
SNSTopics, subscriptions, fanout to SQS/Lambda, platform endpoints
DynamoDBTables, CRUD, Query, Scan, TTL, transactions, batch ops
LambdaPython runtimes, invoke, SQS event sources, Function URLs
IAMUsers, roles, policies, groups, instance profiles, OIDC
STSGetCallerIdentity, AssumeRole, GetSessionToken
SecretsManagerFull CRUD, rotation, versioning
SSM Parameter StoreString, SecureString, StringList, path queries
EventBridgeBuses, rules, targets, Lambda dispatch
KinesisStreams, shards, records, iterators
CloudWatch MetricsPutMetricData, alarms, dashboards, CBOR protocol
CloudWatch LogsLog groups/streams, filter with globs, metric filters
SESSend email, templates, configuration sets
Step FunctionsState machine CRUD
RDSSpins up real Postgres/MySQL containers
ElastiCacheSpins up real Redis containers
AthenaReal SQL via DuckDB
ECSReal Docker containers

Troubleshooting

Connection refused on port 4566

# Check if ministack is running
curl http://localhost:4566/_localstack/health
# Start it
ministack
# or
docker run -p 4566:4566 nahuelnucera/ministack

NoCredentialsError from boto3

export AWS_ACCESS_KEY_ID=test
export AWS_SECRET_ACCESS_KEY=test
export AWS_DEFAULT_REGION=us-east-1
# Any non-empty values work — MiniStack doesn't validate credentials

InvalidSignatureException

  • This is usually a region mismatch. Ensure region_name="us-east-1" matches across all clients.

Lambda function not found after create

  • MiniStack executes Python runtimes with a warm worker pool. Wait briefly or invoke with InvocationType="Event" for async.

S3 data lost on restart

# Enable persistence
S3_PERSIST=1 ministack
# or in Docker
docker run -p 4566:4566 -e S3_PERSIST=1 -v $(pwd)/data:/data nahuelnucera/ministack

Port conflict

GATEWAY_PORT=5000 ministack
# Then use http://localhost:5000 as endpoint

Migrating from LocalStack

  • Replace all http://localhost:4566 endpoint URLs — they stay the same.
  • Remove LOCALSTACK_AUTH_TOKEN / LOCALSTACK_API_KEY env vars (not needed).
  • Replace localstack/localstack Docker image with nahuelnucera/ministack.
  • All boto3 client code works without modification.

平台分布

Codex

37.78%
按下载量换算1,492

Claude

30.43%
按下载量换算1,202

Cursor

19.65%
按下载量换算776

Gemini CLI

9.01%
按下载量换算356

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

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

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