aws-serverless

AWS Serverless

Domain expertise for building serverless applications on AWS: Lambda, API Gateway, Step Functions, EventBridge, event source mappings, concurrency, cold starts, deployment, and troubleshooting.

Works best with the AWS MCP server — run CLI commands, query CloudWatch, validate configs directly. All guidance also works with standard AWS CLI access.

Specialized skills — check these first

These cover capabilities and procedures the general references below do not. Several are specialized features or step-by-step tested procedures you would otherwise miss. Route to the matching skill before falling back to the references.

Advanced Lambda compute (easy to overlook)

Use this skill	When the workload involves
aws-lambda-microvms	Strong tenant isolation, sandboxed/untrusted code execution (AI agent code sandboxes, REPLs, notebooks, CI runners), long-lived sessions, suspend/resume with preserved state, port-listening servers (gRPC, WebSocket, custom TCP), Firecracker microVMs, snapshot-resumable compute, up to 8-hour lifetimes
aws-lambda-durable-functions	Durable execution, checkpoint-and-replay, long-running multi-step workflows written as plain code (TS/Python/Java), automatic state persistence, saga pattern in code, human-in-the-loop callbacks, executions up to 1 year, context.step/context.wait/context.invoke, withDurableExecution, durable-execution-sdk
aws-lambda-managed-instances	Lambda Managed Instances (LMI), capacity providers, EC2-backed Lambda, steady high-volume traffic (50M+ req/mo) wanting Savings Plans / Reserved Instance pricing, PerExecutionEnvironmentMaxConcurrency, CapacityProviderConfig, multi-concurrent execution environments

Workflow orchestration

Route here when the user wants to coordinate multiple steps, services, or functions. Triggers include "orchestration", "workflow", "state machine", "multi-step coordination", "coordinate Lambda functions", "durable execution", "pipeline with retries", or intent to build saga/compensation, human-in-the-loop approval, fan-out, or long-running async coordination.

When starting a new orchestration or multi-step workflow, you MUST surface the choice between AWS Step Functions and AWS Lambda Durable Functions before implementing — do not silently pick one. Route on the signals below. When the request names only a generic pattern (saga/compensation, human-in-the-loop, fan-out, or "workflow orchestration") with no technology, present both options and the one-line tradeoff, then let the user decide. Do not lead with the tradeoff caveats when the signals already point to one service.

Use this skill	When the workload involves
aws-step-functions	Orchestration whose primary work is calling AWS services directly; coordinating non-Lambda compute (ECS/Fargate, Glue, SageMaker, Batch) through native managed integrations; a visual, auditable workflow definition required for compliance, cross-team operational observability, or as a shared contract between teams that do not share a codebase (ASL is the specification, not application code); authoring or editing state machines and Amazon States Language (ASL) — state types, JSONata data transformation, Retry/Catch error handling, .sync/waitForTaskToken service integrations, Distributed Map, TestState unit testing, JSONPath-to-JSONata migration
aws-lambda-durable-functions	Code-first orchestration in-process when already building on Lambda (context.step/context.wait/context.invoke, withDurableExecution); many fine-grained steps per execution where cumulative Step Functions Standard state-transition cost may be significant — compare Step Functions pricing (Standard vs Express) against Lambda invocation cost at the expected volume before choosing; orchestration steps written in a general-purpose language within the same application codebase (share modules, data types, and test suites with application code); teams applying standard software-engineering practices (unit tests, code review, type checking) to orchestration logic without learning a declarative workflow language

Tradeoff (use when either fits): Durable Functions keeps orchestration in your Lambda codebase; Step Functions externalizes it into a managed, visual state machine with built-in service integrations.

Security: Both services persist workflow state and payloads — Step Functions records full input/output in execution history (viewable in the console and, if logging is enabled, CloudWatch Logs). As a baseline, enable execution logging (CloudTrail) and CloudWatch alarms on execution failures, and use least-privilege per-workflow execution roles. Do not pass secrets, tokens, or PII through workflow state; reference them by Secrets Manager/ARN pointer, and apply a customer-managed KMS key to encrypt state when the data is sensitive.

Event-driven architectures

Route here when the user is designing or operating an event-driven system rather than orchestrating a known sequence of steps. Triggers include "event bus", "event-driven architecture", "pub/sub", "publish/subscribe", "fan-out", "event ordering", "ordered delivery", "FIFO events", "event replay", "replay events", "event retention", "event store", "CloudEvents", "Avro", "Protobuf", "schema registry", "deduplication", "choreography", "decoupling", "asynchronous integration", "event broker", or a central bus shared across teams and accounts.

Use this skill

When the workload involves

amazon-eventbridge-event-bus

A new event-driven workload; a platform team owning a central bus that many teams and accounts publish to and subscribe from; governance of that bus (subscriber control, revocation, per-account throttling, cost allocation, end-to-end observability); open event formats (Avro, Protobuf, CloudEvents) decoded through a schema registry; ordered or FIFO delivery per event group; high fan-out where per-account forwarding cost matters; durable retention and replay of past events into a newly created subscriber; per-subscriber JSONata transformation; deduplication; choreography and decoupling across services; event-driven architecture spanning multiple accounts. This skill MUST NOT be used for questions regarding the EventBridge classic custom event bus, EventBridge Scheduler, EventBridge Pipes, EventBridge Global Endpoints, the EventBridge Schema Registry, EventBridge API Destinations, or EventBridge Connections.

Step-by-step task procedures (tested CLI SOPs)

Use this skill	For the task
connecting-lambda-to-api-gateway	Wire an existing Lambda to a new REST/HTTP API: proxy integration, permissions, CORS, throttling, access logging, deployment
connecting-lambda-to-dynamodb	Connect Lambda to DynamoDB: IAM execution role, read/write permissions, stream event source mapping
creating-api-gateway-stage	Create an API Gateway stage with CloudWatch logging, X-Ray tracing, throttling, WAF association, and authorization
deploying-custom-domain-rest-api	Deploy a Regional REST API with custom domain: ACM cert, Lambda backend, request authorizer, base path mapping, Route 53 DNS
debugging-lambda-timeouts	Systematically diagnose a timing-out Lambda: config, CloudWatch logs/metrics, VPC, cold starts, memory, downstream calls
processing-s3-uploads-with-step-functions	Deploy an event-driven workflow: S3 upload → EventBridge → Step Functions → Lambda (small files) or Fargate (large files), with VPC/ECR/ECS/IAM

Routing (general references in this skill)

User need	Read
Building a new serverless app — pattern selection	architecture.md
Lambda config, cold starts, SnapStart, memory, VPC, layers, Function URLs	lambda.md
Concurrency (reserved, provisioned, ESM controls)	concurrency.md
Event sources (SQS, DynamoDB Streams, SNS, Kinesis), filtering, batch failures	event-sources.md
Step Functions, EventBridge rules/pipes/scheduler	orchestration.md
API Gateway quotas, authorizers, WebSocket	api-gateway.md
SAM/CDK resource types and fast iteration	deployment.md
Production readiness, observability, anti-patterns	production.md
Debugging an error (exact string → cause → fix)	troubleshooting.md
Powertools handler template	powertools-handler.py

Note: Reference files contain specific runtime versions, quotas, and feature matrices that change. When precision matters (production, runtime choice, quotas), confirm against current AWS documentation. The references focus on values and gotchas that are easy to get wrong — not on basics.