aws-serverless-eda▌
zxkane/aws-skills · updated Apr 8, 2026
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This skill provides comprehensive guidance for building serverless applications and event-driven architectures on AWS based on Well-Architected Framework principles.
AWS Serverless & Event-Driven Architecture
This skill provides comprehensive guidance for building serverless applications and event-driven architectures on AWS based on Well-Architected Framework principles.
AWS Documentation Requirement
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.
Serverless MCP Servers
This skill leverages the CDK MCP server (provided via aws-cdk-development dependency) and AWS Documentation MCP for serverless guidance.
Note: The following AWS MCP servers are available separately via the Full AWS MCP Server (see
aws-mcp-setupskill) and are not bundled with this plugin:
- AWS Serverless MCP — SAM CLI lifecycle (init, deploy, local test)
- AWS Lambda Tool MCP — Direct Lambda invocation
- AWS Step Functions MCP — Workflow orchestration
- Amazon SNS/SQS MCP — Messaging and queue management
When to Use This Skill
Use this skill when:
- Building serverless applications with Lambda
- Designing event-driven architectures
- Implementing microservices patterns
- Creating asynchronous processing workflows
- Orchestrating multi-service transactions
- Building real-time data processing pipelines
- Implementing saga patterns for distributed transactions
- Designing for scale and resilience
AWS Well-Architected Serverless Design Principles
1. Speedy, Simple, Singular
Functions should be concise and single-purpose
// ✅ GOOD - Single purpose, focused function
export const processOrder = async (event: OrderEvent) => {
// Only handles order processing
const order = await validateOrder(event);
await saveOrder(order);
await publishOrderCreatedEvent(order);
return { statusCode: 200, body: JSON.stringify({ orderId: order.id }) };
};
// ❌ BAD - Function does too much
export const handleEverything = async (event: any) => {
// Handles orders, inventory, payments, shipping...
// Too many responsibilities
};
Keep functions environmentally efficient and cost-aware:
- Minimize cold start times
- Optimize memory allocation
- Use provisioned concurrency only when needed
- Leverage connection reuse
2. Think Concurrent Requests, Not Total Requests
Design for concurrency, not volume
Lambda scales horizontally - design considerations should focus on:
- Concurrent execution limits
- Downstream service throttling
- Shared resource contention
- Connection pool sizing
// Consider concurrent Lambda executions accessing DynamoDB
const table = new dynamodb.Table(this, 'Table', {
billingMode: dynamodb.BillingMode.PAY_PER_REQUEST, // Auto-scales with load
});
// Or with provisioned capacity + auto-scaling
const table = new dynamodb.Table(this, 'Table', {
billingMode: dynamodb.BillingMode.PROVISIONED,
readCapacity: 5,
writeCapacity: 5,
});
// Enable auto-scaling for concurrent load
table.autoScaleReadCapacity({ minCapacity: 5, maxCapacity: 100 });
table.autoScaleWriteCapacity({ minCapacity: 5, maxCapacity: 100 });
3. Share Nothing
Function runtime environments are short-lived
// ❌ BAD - Relying on local file system
export const handler = async (event: any) => {
fs.writeFileSync('/tmp/data.json', JSON.stringify(data)); // Lost after execution
};
// ✅ GOOD - Use persistent storage
export const handler = async (event: any) => {
await s3.putObject({
Bucket: process.env.BUCKET_NAME,
Key: 'data.json',
Body: JSON.stringify(data),
});
};
State management:
- Use DynamoDB for persistent state
- Use Step Functions for workflow state
- Use ElastiCache for session state
- Use S3 for file storage
4. Assume No Hardware Affinity
Applications must be hardware-agnostic
Infrastructure can change without notice:
- Lambda functions can run on different hardware
- Container instances can be replaced
- No assumption about underlying infrastructure
Design for portability:
- Use environment variables for configuration
- Avoid hardware-specific optimizations
- Test across different environments
5. Orchestrate with State Machines, Not Function Chaining
Use Step Functions for orchestration
// ❌ BAD - Lambda function chaining
export const handler1 = async (event: any) => {
const result = await processStep1(event);
await lambda.invoke({
FunctionName: 'handler2',
Payload: JSON.stringify(result),
});
};
// ✅ GOOD - Step Functions orchestration
const stateMachine = new stepfunctions.StateMachine(this, 'OrderWorkflow', {
definition: stepfunctions.Chain
.start(validateOrder)
.next(processPayment)
.next(shipOrder)
.next(sendConfirmation),
});
Benefits of Step Functions:
- Visual workflow representation
- Built-in error handling and retries
- Execution history and debugging
- Parallel and sequential execution
- Service integrations without code
6. Use Events to Trigger Transactions
Event-driven over synchronous request/response
// Pattern: Event-driven processing
const bucket = new s3.Bucket(this, 'DataBucket');
bucket.addEventNotification(
s3.EventType.OBJECT_CREATED,
new s3n.LambdaDestination(processFunction),
{ prefix: 'uploads/' }
);
// Pattern: EventBridge integration
const rule = new events.Rule(this, 'OrderRule', {
eventPattern: {
source: ['orders'],
detailType: ['OrderPlaced'],
},
});
rule.addTarget(new targets.LambdaFunction(processOrderFunction));
Benefits:
- Loose coupling between services
- Asynchronous processing
- Better fault tolerance
- Independent scaling
7. Design for Failures and Duplicates
Operations must be idempotent
// ✅ GOOD - Idempotent operation
export const handler = async (event: SQSEvent) => {
for (const record of event.Records) {
const orderId = JSON.parse(record.body).orderId;
How to use aws-serverless-eda on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add aws-serverless-eda
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches aws-serverless-eda from GitHub repository zxkane/aws-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate aws-serverless-eda. Access the skill through slash commands (e.g., /aws-serverless-eda) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★25 reviews- ★★★★★Ganesh Mohane· Dec 28, 2024
Useful defaults in aws-serverless-eda — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yusuf Mehta· Dec 28, 2024
Useful defaults in aws-serverless-eda — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakshi Patil· Nov 19, 2024
aws-serverless-eda is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Chaitanya Patil· Oct 10, 2024
Keeps context tight: aws-serverless-eda is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Li Reddy· Sep 25, 2024
Solid pick for teams standardizing on skills: aws-serverless-eda is focused, and the summary matches what you get after install.
- ★★★★★Diya Haddad· Sep 17, 2024
Useful defaults in aws-serverless-eda — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Rahul Santra· Sep 1, 2024
We added aws-serverless-eda from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yusuf Gonzalez· Sep 1, 2024
We added aws-serverless-eda from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Pratham Ware· Aug 20, 2024
aws-serverless-eda fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Evelyn Anderson· Aug 20, 2024
aws-serverless-eda fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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