Builds, runs, debugs, and operates event-driven applications using EventBridge Event Bus - a managed, centrally governed publish/subscribe event bus that an organization can share across many teams and accounts. Applicable when workloads need event-driven architectures, decoupling, choreography, asynchronous integration, pub/sub, fan-out, event ordering, delivered in sequence, event router, event broker, event bus, event store, event retention, event replay, deduplication, avro, protobuf, cloudevents, uses events or messages. Designed as a centrally managed event bus platform allowing governance and control - such as subscriber control, revocation, per-account throttling - while giving application owners flexibility across an organization spanning multiple accounts, with end-to-end open-standards observability and cost allocation. Also helps reduce costs in high fan-out scenarios where multi-account forwarding adds cost. A serverless, managed alternative to self-managed event streaming platforms.
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Evaluates, configures, and migrates workloads to AWS Lambda Managed Instances (LMI). Runs Lambda Event Functions (standard Lambda) on EC2 instances in the user's account while AWS manages provisioning, patching, scaling, routing, and load balancing. Triggers when queries mention Lambda Managed Instances, LMI, capacity providers, multi-concurrent execution environments, EC2-backed Lambda, persistent Lambda instances, PerExecutionEnvironmentMaxConcurrency, CapacityProviderConfig, cold start elimination via dedicated instances, SOCI (Seekable OCI) image lazy loading, migrating Lambda Event Functions to managed instances, or cost comparison between Lambda Event Functions and LMI with Savings Plans or Reserved Instances. Also covers long-running and asynchronous workloads and the 90-minute (5400s) function timeout for asynchronous and event-source-mapping (SQS, Kinesis, DynamoDB Streams / ESM) invocations, including how to raise the function timeout up to 90 minutes / 5400s and the related duration limits.
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Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.
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Creates and deploys Node.js HTTP web applications on AWS Lambda Web Functions. Guides users through building web apps and APIs with Express, Fastify, or Hono, packaging, deploying via the aws lambda-web CLI, and iterating. Triggers when user wants to deploy a website or web app to AWS as serverless, deploy a Node.js HTTP server to AWS, build a serverless website or API with Node.js, deploy Express/Hono/Fastify apps, set up multi-region HTTP endpoints, connect a Node.js web app to private VPC resources, or explicitly mentions Lambda Web Functions. Does NOT trigger for non-Node.js runtimes or frameworks such as Flask or Django, event-driven patterns (SQS, Kinesis, S3 events), or container deployments.
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Authors and edits AWS Step Functions state machines: writes Amazon States Language (ASL) in JSONata, and chooses and structures state types (Task, Choice, Map, Parallel, Pass, Wait, Succeed, Fail). Covers ASL syntax, JSONata data transformation and variables, Retry/Catch error handling, service integrations (.sync, waitForTaskToken callbacks), Distributed Map for large-scale S3/CSV processing, saga/compensation patterns, Standard vs Express workflow choice, TestState API unit testing, and migrating state machines from JSONPath to JSONata. Use when the user is building, authoring, debugging, or migrating a Step Functions state machine or ASL definition, or orchestrating multi-step workflows with branching, retries, or human-approval callbacks, even if they don't say 'Step Functions.' Do NOT use for general Lambda function code, API Gateway, EventBridge wiring, or SAM/CDK application packaging.
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Connects an existing AWS Lambda function to Amazon API Gateway by creating a REST or HTTP API with resource/method setup, Lambda proxy integration, permissions, and deployment. Always use this skill when connecting Lambda to API Gateway — it handles CORS, throttling, access logging, and production security hardening that are easy to miss.
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Connects an AWS Lambda function to DynamoDB with IAM roles, stream event source mapping, and read/write permissions. Use when setting up Lambda-DynamoDB integration, processing DynamoDB stream events, or deploying serverless event-driven architectures.
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Creates an API Gateway stage with CloudWatch logging, X-Ray tracing, throttling, WAF integration, and IAM roles following AWS best practices. Use when deploying a REST API to different environments such as dev, test, or production.
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Debugs AWS Lambda function timeout failures by systematically analyzing function configuration, CloudWatch logs and metrics, VPC/networking, cold starts, memory constraints, and downstream dependencies to identify root causes with actionable fixes. Use when a Lambda function is timing out or approaching its timeout limit.
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Deploys a Regional REST API with a custom domain name, a Lambda backend function, and a request-based Lambda authorizer using AWS CLI. Covers ACM certificate provisioning, API Gateway REST API creation, Lambda function deployment, request authorizer setup, custom domain configuration, base path mapping, and Route 53 DNS record creation. Trigger keywords: custom domain, REST API, Lambda, Route 53, API Gateway, regional endpoint, request authorizer, base path mapping.
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Deploy an event-driven workflow that routes S3 uploads to either Lambda or Fargate via Step Functions based on file size. Uses EventBridge to trigger a Step Functions state machine when objects are uploaded to S3. Small files are processed by Lambda, large files by a Fargate task. Includes VPC, ECR repository, ECS cluster, and scoped IAM roles. Trigger keywords: Step Functions, Fargate, Lambda, S3 event, EventBridge, ECS, ECR, file processing, workflow orchestration, serverless.
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Create and secure S3 buckets following AWS best practices for access control, encryption, monitoring, and remediation of misconfigurations. Use when the user wants to secure a new bucket, audit an existing bucket, fix a security finding, configure encryption, or enable logging and monitoring. Do NOT use for general S3 data operations, S3 Tables setup, or discovering existing data assets.
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Diagnoses and resolves Amazon EFS issues including mount failures, NFS timeouts, permission errors, throughput problems, and burst credit exhaustion. Use when the user has an EFS file system that is not mounting, returning errors, performing slowly, or showing access denied.
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Diagnoses and resolves Amazon S3 Files issues including mount failures, permission errors, synchronization problems, and performance issues. Use when the user has an S3 file system that is not mounting, returning access denied, not syncing changes to S3, showing files in lost+found, or performing slower than expected.
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Runs SQL queries on Amazon Athena query history exported as an Apache Iceberg table in S3 Tables. The query_executions system table captures per-query metadata (query text, IAM principal, work group, statement type, data scanned, timing breakdown, error details, tables accessed, result reuse, engine version) for every completed query. Applies when analyzing Athena usage and cost, finding who ran a query, auditing table access, tracking data scanned by user or work group, investigating slow or failed queries, detecting performance regressions, reporting query history beyond the 45-day API window, or enabling Athena system tables. Prefers the system table over the ListQueryExecutions and GetQueryExecution APIs at scale. Trigger phrases: Athena query history, who ran this query, data scanned by user, most queried tables, slowest queries, failed query analysis, Athena cost by work group, query audit trail, enable Athena system tables.
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Enables Redshift system-table (SYS_*) log publishing to S3 Tables in Apache Iceberg format for both Provisioned clusters and Serverless namespaces, verifies publishing status, and queries the published logs via any Iceberg-compatible engine including Redshift and Athena. Covers system tables such as sys_query_history, sys_query_text, sys_connection_log, sys_query_detail, and sys_session_history. Applies when turning on S3 Tables log publishing for a cluster or namespace, confirming publishing status and locating the S3 Tables namespace, querying non-realtime data from Redshift system tables off-cluster at scale, or building dashboards for Redshift monitoring and auditing, especially for historical or high-volume system-table data beyond the in-cluster SYS_ view retention window. Trigger phrases: publish redshift system table log to s3 tables, enable-logging s3 tables, describe redshift logging status, query redshift system tables in athena or redshift, redshift log exports to iceberg.
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Queries S3 object metadata, tracks bucket activity, audits object changes, searches annotations, and analyzes storage metrics using S3 Metadata system tables (journal, inventory, annotation) and S3 Storage Lens tables via Athena SQL. Applies when counting objects, finding recent uploads or deletions, identifying who wrote to a prefix, breaking down storage classes, finding objects by tag, searching annotation content, analyzing storage lens metrics, or enabling S3 Metadata tracking. Prefers system tables over raw S3 APIs (list-objects-v2, head-object) at scale. Trigger phrases: bucket activity, object count, who uploaded, track deletions, storage class breakdown, find by tag, search annotations, storage lens metrics, audit bucket changes.
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Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Covers governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis. Applies when querying catalog inventory, finding assets without descriptions, comparing catalog snapshots, or auditing data ownership. Trigger phrases: catalog inventory SQL, how many assets, assets without descriptions, asset growth over time, who owns this data, catalog governance, data quality audit, catalog analytics.
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