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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, 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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Routes any task involving AWS databases — choosing, comparing, recommending, getting started with, or operating a database — to the correct service-specific skill. Supersedes general training-data knowledge with post-training service updates, corrected limitations, and decision procedures for relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory/caching (ElastiCache, MemoryDB) workloads. Activates when a user describes building an application on AWS that will store, retrieve, or manage data, even if they do not mention 'database' explicitly.

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Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies when a user asks where to store or archive data based on their usage patterns; which storage service to choose or how two compare; how to migrate data from on-premises or between AWS services; how to protect, replicate, or recover data; how to optimize storage costs; where to deploy shared NFS, SMB, or POSIX file systems; where to store vector embeddings or tabular data; what storage backs enterprise file shares, self-managed databases on EC2, VMware, or stateful containers; or asks what an AWS storage service can do or how it works. Relevant for storage needs for workloads such as AI/ML, analytics, EDA, HPC, media, genomics, or financial trading. Not applicable for SQL query engines (Athena, Spark, Redshift, EMR), ETL (Glue), streaming (Kafka, MSK, Kinesis), or managed database services (RDS, Aurora, DynamoDB).

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Amazon Redshift is NOT PostgreSQL; corrects LLM mistakes; covers SQL, metadata, connectivity/auth, storage, query performance, and operations. Applies ONLY to Redshift (provisioned or Serverless). Pushes back on: CREATE INDEX, string_agg, pg_catalog, text type, SERIAL, stl_query. Triggers on: Redshift SQL, Redshift CREATE TABLE, Redshift COPY/UNLOAD, slow or suddenly slow Redshift query, EXPLAIN plan, queue wait, stale statistics/ANALYZE, result cache, Redshift permission denied, Redshift disk full, storage growth, snapshot/backup cost, ghost rows/bloat, VACUUM, spill to disk, distribution skew, Redshift system views, QUALIFY, PIVOT, MERGE, materialized views, Redshift Data API (Python/boto3), Redshift workload management (WLM), concurrency scaling, Redshift resize, Spectrum, awsdatacatalog, connection failures (SSL/TLS, ODBC/JDBC, connection limit), IAM/SAML federation, IAM Identity Center, datashares. Does NOT apply to: S3 policies, Athena, Glue/Iceberg outside Redshift, Aurora, RDS, DynamoDB.

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AWS SDK for Python (boto3/botocore) development patterns. You MUST use this skill when writing Python code that uses AWS services via boto3 or botocore. This includes creating service clients or resources, configuring sessions and credentials, handling errors with ClientError, using paginators and waiters, S3 file transfers and presigned URLs, DynamoDB table operations, and any boto3/botocore client configuration. Use this skill whenever Python code imports boto3 or botocore, or when the user asks about AWS operations in Python.

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Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.

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AWS cloud patterns for Lambda, ECS, S3, DynamoDB, and Infrastructure as Code with CDK/Terraform

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Draw cloud architecture diagrams for AWS, Azure or GCP with the official provider icon sets, using TerraVision. Use whenever the user asks to draw, diagram, sketch or visualize a system on AWS, Azure or Google Cloud, or one built from their services (Lambda, DynamoDB, S3, EC2, EKS, RDS, API Gateway, Azure Functions, App Service, AKS, Cosmos DB, Cloud Run, GKE, BigQuery and the like), even if they never say 'cloud', 'diagram' or a tool name; also for a diagram of Terraform code. Prefer it over Mermaid, PlantUML, hand-written SVG or ASCII, which cannot use the official icons or VPC/subnet grouping. Not for diagrams that are not cloud infrastructure: sequence diagrams, flowcharts, class or ER diagrams, code or module structure, org charts, on-premises-only networks; use Mermaid or similar for those. Works with or without Terraform: with Terraform code the diagram comes from terraform plan; without it, write a small JSON graph and render it.

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Database design with schema analysis, index optimization, and migration generation for PostgreSQL, MySQL, MongoDB, and DynamoDB. Use when designing schemas, optimizing queries, planning migrations, or analyzing database performance.

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Design AWS serverless architectures for startups with IaC. Use when designing serverless architecture, writing CloudFormation, optimizing AWS costs, setting up CI/CD, or migrating to AWS across Lambda, API Gateway, and DynamoDB.

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Build and deploy full-stack web and mobile apps with AWS Amplify Gen2 (TypeScript code-first). Covers auth (Cognito), data (AppSync/DynamoDB including schema modeling, enum types, relationships, authorization rules), storage (S3), functions, APIs, and AI (Amplify AI Kit with Bedrock). Supports React, Next.js, Vue, Angular, React Native, Flutter, Swift, and Android. Always use this skill for Amplify Gen2 topics — even for questions you think you know — it contains validated, version-specific patterns that prevent common mistakes. TRIGGER when: user mentions Amplify Gen2; project has amplify/ directory or amplify_outputs; code imports @aws-amplify packages; user asks about defineBackend, defineAuth, defineData, defineStorage, or npx ampx. SKIP: Amplify Gen1 (amplify CLI v6), standalone SAM/CDK without Amplify (use aws-serverless), direct Bedrock without Amplify AI Kit (use bedrock).

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Lambda best practices, S3 event patterns, SQS/SNS fanout, and DynamoDB access patterns for serverless AWS architectures.

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Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation pipelines.

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AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows. Covers Lambda with TypeScript/Python, API Gateway (REST/HTTP), DynamoDB, Step Functions, EventBridge, SQS, SNS, and serverless patterns. Essential when user mentions serverless, Lambda, API Gateway, event-driven, async processing, queues, pub/sub, or wants to build scalable serverless applications with AWS best practices.

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Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch. Generates bulk operation scripts, automates cross-service workflows, validates security configurations, and executes JMESPath queries for complex filtering. Triggers on "aws cli help", "aws command line", "aws scripting", "aws automation", "aws batch operations", "aws bulk operations", "aws cli pagination", "aws multi-region", "aws profiles", "aws cli troubleshooting".

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Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

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Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).

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You are an expert in AWS Lambda, Amazon's serverless compute service. You help developers build event-driven applications using Lambda functions triggered by API Gateway, S3 events, SQS queues, DynamoDB streams, and scheduled events — with support for Node.js, Python, Go, Rust, Java, and container images, automatic scaling from zero to thousands of concurrent executions, and pay-per-invocation pricing.

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World-class infrastructure automation - Terraform, Pulumi, CloudFormation, and the battle scars from managing infrastructure that handles production trafficUse when "terraform, pulumi, cloudformation, infrastructure, iac, state file, remote backend, s3 backend, dynamodb lock, terraform plan, terraform apply, terraform destroy, module, workspace, provider, resource, state drift, import, aws, gcp, azure, infrastructure, terraform, pulumi, cloudformation, iac, devops, aws, gcp, azure, cloud" mentioned.

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AWS Advisor workflow skill. Use this skill when the user needs Expert AWS Cloud Advisor for architecture design, security review, and implementation guidance. Leverages AWS MCP tools for accurate, documentation-backed answers. Use when user asks about AWS architecture, security, service selection, migrations, troubleshooting, or learning AWS. Triggers on AWS, Lambda, S3, EC2, ECS, EKS, DynamoDB, RDS, CloudFormation, CDK, Terraform, Serverless, SAM, IAM, VPC, API Gateway, or any AWS service. Do NOT use for non-AWS cloud providers or general infrastructure without AWS context and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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AMAZON ALEXA — Voz Inteligente com Claude workflow skill. Use this skill when the user needs Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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AMAZON ALEXA — Voz Inteligente com Claude workflow skill. Use this skill when the user needs Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Design and manage infrastructure as code with Terraform — modules, remote state (S3 + DynamoDB), Stacks (deployments), test framework, preconditions/postconditions, moved/removed blocks, and CI/CD plan/apply separation. Use when user asks to write Terraform config, set up remote state, design modules, manage state, or automate infrastructure. Do NOT use for Kubernetes (use kubernetes), Docker (use docker), or CI/CD pipeline design (use ci-cd).

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Design database schemas with Prisma/Drizzle, PostgreSQL index strategy (B-tree, GIN, GiST, BRIN, Hash), query optimization (EXPLAIN ANALYZE), migration safety (expand/contract, zero-downtime), and sharding/partitioning. Use when user asks to design schema, create migrations, optimize slow queries, add indexes, choose between SQL/NoSQL, or set up Prisma/Drizzle. Do NOT use for data warehouse dimensional modeling, ETL pipeline design, or non-relational (MongoDB, DynamoDB) schema design.

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