AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS...
npx skills add https://github.com/lingxling/awesome-skills-cn --skill aws-cdk-development
This skill provides comprehensive guidance for developing AWS infrastructure using the Cloud Development Kit (CDK), with integrated MCP servers for accessing latest AWS knowledge and CDK utilities.
Always verify AWS facts using MCP tools (mcp__aws-mcp__* or mcp__*awsdocs*__*) before answering. The aws-mcp-setup dependency is auto-loaded — if MCP tools are unavailable, guide the user through that skill's setup flow.
AWS Labs replaced the dedicated CDK MCP server (awslabs.cdk-mcp-server) with the broader awslabs.aws-iac-mcp-server, which covers CDK alongside CloudFormation and other AWS infrastructure-as-code workflows.
For CDK construct lookups, best-practice recommendations, and pattern guidance, install awslabs.aws-iac-mcp-server. It ships in the deploy-on-aws plugin from awslabs/agent-plugins, or can be registered directly with claude mcp add aws-iac uvx awslabs.aws-iac-mcp-server@latest.
When to reach for it:
Use this skill when:
CRITICAL: Do NOT explicitly specify resource names when they are optional in CDK constructs.
Why: CDK-generated names enable:
Pattern: Let CDK generate unique names automatically using CloudFormation's naming mechanism.
// ❌ BAD - Explicit naming prevents reusability and parallel deployments
new lambda.Function(this, 'MyFunction', {
functionName: 'my-lambda', // Avoid this
// ...
});
// ✅ GOOD - Let CDK generate unique names
new lambda.Function(this, 'MyFunction', {
// No functionName specified - CDK generates: StackName-MyFunctionXXXXXX
// ...
});
Security Note: For different environments (dev, staging, prod), follow AWS Security Pillar best practices by using separate AWS accounts rather than relying on resource naming within a single account. Account-level isolation provides stronger security boundaries.
Use the appropriate Lambda construct based on runtime:
TypeScript/JavaScript: Use @aws-cdk/aws-lambda-nodejs
import { NodejsFunction } from 'aws-cdk-lib/aws-lambda-nodejs';
new NodejsFunction(this, 'MyFunction', {
entry: 'lambda/handler.ts',
handler: 'handler',
// Automatically handles bundling, dependencies, and transpilation
});
Python: Use @aws-cdk/aws-lambda-python
import { PythonFunction } from '@aws-cdk/aws-lambda-python-alpha';
new PythonFunction(this, 'MyFunction', {
entry: 'lambda',
index: 'handler.py',
handler: 'handler',
// Automatically handles dependencies and packaging
});
Benefits:
Use a multi-layer validation strategy for comprehensive CDK quality checks:
For TypeScript/JavaScript projects:
Install cdk-nag for synthesis-time validation:
npm install --save-dev cdk-nag
Add to your CDK app:
import { Aspects } from 'aws-cdk-lib';
import { AwsSolutionsChecks } from 'cdk-nag';
const app = new App();
Aspects.of(app).add(new AwsSolutionsChecks());
Optional - VS Code users: Install CDK NAG Validator extension for faster feedback on file save.
For Python/Java/C#/Go projects: cdk-nag is available in all CDK languages and provides the same synthesis-time validation.
cdk synth # cdk-nag runs automatically via Aspects
import { NagSuppressions } from 'cdk-nag';
// Document WHY the exception is needed
NagSuppressions.addResourceSuppressions(resource, [
{
id: 'AwsSolutions-L1',
reason: 'Lambda@Edge requires specific runtime for CloudFront compatibility'
}
]);
npm run build # or language-specific build command
npm test # or pytest, mvn test, etc.
./scripts/validate-stack.sh
The validation script now focuses on:
Always verify before implementing:
Example scenarios:
Leverage for CDK-specific guidance:
Example scenarios:
For detailed CDK patterns, anti-patterns, and architectural guidance, refer to the comprehensive reference:
File: references/cdk-patterns.md
This reference includes:
scripts/validate-stack.sh - Pre-deployment validationreferences/cdk-patterns.md - Detailed pattern libraryWhen GitHub Actions workflow files exist in the repository, ensure all checks defined in .github/workflows/ pass before committing. This prevents CI/CD failures and maintains code quality standards.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take lingxling/aws-cdk-development from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npm, uvx.
Without those the skill loads but fails at the first command.