快速判断
Common AWS CDK patterns and constructs for building cloud infrastructure with TypeScript, Python, or Java. Use when designing reusable CDK stacks and L3 constructs.
适合任务
- 把重复任务整理成可复用的 AI 操作流程。
- 让 AI 在特定场景下按统一规范执行。
- 为团队或个人工作流提供可复制的任务说明。
输入与输出
输入:任务目标、上下文材料、文件路径、约束条件或需要处理的内容。
输出:按 Skill 说明生成的文档、代码、检查结果、计划、建议或操作步骤。
示例任务
- 使用 cdk-patterns 帮我处理当前任务,并说明执行前需要确认的输入。
- 根据 cdk-patterns 的说明,给我一个安全的使用步骤清单。
安装方式
- 下载本站提供的 Skill ZIP 并解压。
- 把解压后的 Skill 目录放入当前 AI 工具支持的
skills目录。 - 如需在线查看原始内容,可打开 GitHub 的
SKILL.md。
在线原始地址:cdk-patterns/SKILL.md
风险边界
使用前请检查权限、外部依赖和要处理的数据类型。不要把密码、密钥、身份信息或敏感客户资料交给未经确认的 Skill。
SKILL.md 文档介绍
You are an expert in AWS Cloud Development Kit (CDK) specializing in reusable patterns, L2/L3 constructs, and production-grade infrastructure stacks.
Use this skill when
- Building reusable CDK constructs or patterns
- Designing multi-stack CDK applications
- Implementing common infrastructure patterns (API + Lambda + DynamoDB, ECS services, static sites)
- Reviewing CDK code for best practices and anti-patterns
Do not use this skill when
- The user needs raw CloudFormation templates without CDK
- The task is Terraform-specific
- Simple one-off CLI resource creation is sufficient
Instructions
1. Identify the infrastructure pattern needed (e.g., serverless API, container service, data pipeline).
2. Use L2 constructs over L1 (Cfn*) constructs whenever possible for safer defaults.
3. Apply the principle of least privilege for all IAM roles and policies.
4. Use RemovalPolicy and Tags appropriately for production readiness.
5. Structure stacks for reusability: separate stateful (databases, buckets) from stateless (compute, APIs).
6. Enable monitoring by default (CloudWatch alarms, X-Ray tracing).
Examples
Example 1: Serverless API Pattern
import { Construct } from "constructs";
import * as apigateway from "aws-cdk-lib/aws-apigateway";
import * as lambda from "aws-cdk-lib/aws-lambda";
import * as dynamodb from "aws-cdk-lib/aws-dynamodb";
export class ServerlessApiPattern extends Construct {
constructor(scope: Construct, id: string) {
super(scope, id);
const table = new dynamodb.Table(this, "Table", {
partitionKey: { name: "pk", type: dynamodb.AttributeType.STRING },
billingMode: dynamodb.BillingMode.PAY_PER_REQUEST,
removalPolicy: cdk.RemovalPolicy.RETAIN,
});
const handler = new lambda.Function(this, "Handler", {
runtime: lambda.Runtime.NODEJS_20_X,
handler: "index.handler",
code: lambda.Code.fromAsset("lambda"),
environment: { TABLE_NAME: table.tableName },
tracing: lambda.Tracing.ACTIVE,
});
table.grantReadWriteData(handler);
new apigateway.LambdaRestApi(this, "Api", { handler });
}
}Best Practices
- ✅ Do: Use
cdk.Tags.of(this).add()for consistent tagging - ✅ Do: Separate stateful and stateless resources into different stacks
- ✅ Do: Use
cdk diffbefore every deploy - ❌ Don't: Use L1 (
Cfn*) constructs when L2 alternatives exist - ❌ Don't: Hardcode account IDs or regions — use
cdk.Aws.ACCOUNT_ID
Troubleshooting
Problem: Circular dependency between stacks
Solution: Extract shared resources into a dedicated base stack and pass references via constructor props.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.