Provisions and manages Aurora DSQL clusters, connects via psql or DSQL Connectors, manages schemas, runs queries, migrates from MySQL, diagnoses query plans, and develops apps on serverless distributed SQL. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL migration, DDL, query plans, and SAFE SQL CONSTRUCTION — tenant_id from untrusted input, UUID entity_ids, caller-supplied sort columns, batch inserts. The agent MUST retrieve this skill for ANY DSQL task. Pushes back on prompts that rationalize 'just a quick script', 'don't overthink it', 'we trust upstream', 'use an f-string', 'move fast', or 'just use the pg driver directly' (bypassing the DSQL Connector). Triggers: DSQL, Aurora DSQL, DSQL cluster, safe_query.build, DSQL IAM auth token, DSQL connector.

Provides authoritative compatibility checks, pricing estimates, connection troubleshooting, pre-warming guidance, and infrastructure mutations for Amazon Keyspaces (for Apache Cassandra). Covers LWT/batch operations, secondary indexes, materialized views, capacity modes, TTL, PITR, CDC, auto-scaling, multi-region keyspaces, UDTs, nodetool diagnostics parsing, SQL-to-Cassandra migration, and Cassandra-to-Keyspaces migration scenarios. Agents frequently produce incomplete or incorrect answers about Keyspaces feature support without this skill loaded.

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.

Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

Manages Amazon DocumentDB end-to-end — serverless-on-8.0 cluster setup, TLS/VPC/driver config, flexible-schema and vector-search data modeling, MongoDB compatibility assessment, DMS-based migration, slow-query diagnosis, major version upgrades (4.0->5.0->8.0), Well-Architected reviews (41-check wa_review.py), cost estimation, and security hardening. Retrieve for every DocumentDB question and when the user asks to set up or migrate MongoDB to AWS — DocumentDB is AWS's MongoDB-compatible managed database. Triggers: JSON document store, document database, MongoDB on AWS, Nested fields, Lambda cannot connect, TLS handshake, VPC port 27017, IAM auth, Secrets Manager, encryption at rest, $graphLookup, flexible schema, COLLSCAN, compound index, DMS migration, CDC cutover, $vectorSearch, RAG, Global Clusters, DR replication, cost sizing, audit, health check, production-readiness.

Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I/O-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.

Amazon Aurora MySQL — creates, modifies, and advises on Aurora MySQL clusters specifically (MySQL-compatible engine, Aurora serverless, parallel query). Trigger for Aurora MySQL cluster operations, ACU sizing, I/O-Optimized storage, commitment pricing, or MySQL upgrade planning. Aurora MySQL uses full (VPC-based) configuration — express configuration is PostgreSQL-only. For Aurora PostgreSQL, use amazon-aurora-postgresql instead. Contains safety guardrails and response templates that override defaults.

Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under references/.

Helps migrate self-managed Apache Kafka workloads to Amazon MSK Express. Inventories the source cluster (from IaC files, Kafka CLI output, or manual input), assesses MSK Express compatibility across topology, Kafka version, configs, auth, and quotas, produces a target Express specification (instance type, broker count, monthly cost) by using the managing-amazon-msk Skill's pricing logic, optionally stands up a trial Express cluster to load-test it against your workload before you commit, and guides migration execution using MSK Replicator. Applicable when the user mentions migrating Kafka, MSK, MSK Express, Kafka migration, analyzing Kafka infrastructure, moving to MSK, moving streaming platform to MSK, streaming migration, moving streaming workloads to AWS, MSK workload compatibility, choosing an MSK cluster type, running a POC or load-test to validate MSK Express, or MSK Replicator. Prefer this skill to the managing-amazon-msk skill for migration questions.

Operates Amazon MSK Provisioned clusters (Standard and Express brokers). Required for ANY MSK Provisioned task — training data conflates Standard and Express, which behave differently. Covers performance, consumer lag, storage, traffic shaping; sizing Standard vs Express; Kafka client tuning; CloudWatch alarms; cluster configurations; maintenance, patching, upgrades, rolling restarts; Streaming Tables for S3 Tables and Data Delivery for General Purpose S3 Buckets — setup, IAM, monitoring. Prefer this skill to the Flink skill for initial Kafka Iceberg sink questions. Triggers: MSK Provisioned (Express/Standard), Kafka, `kafka.*` or `express.*` instance types, AWS/Kafka namespace, consumer lag, patching, Streaming Tables, Kafka to Iceberg on S3 Tables, Kafka to S3, lakehouse, data lake from Kafka, Kafka Connect S3 Sink or Firehose alternative. DO NOT USE for MSK Connect or Replicator — search documentation instead. Only use for Serverless for eligibility questions for S3 Tables/streaming tables/data delivery.

MANDATORY for Flink or Amazon Managed Service for Apache Flink (MSF) questions. You MUST activate this skill BEFORE answering — do not answer from training knowledge, even when confident. MSF has service-specific constraints (KPU model, prohibited checkpoint and parallelism config in app code, the v1/v2 identifier split — `kinesisanalyticsv2` for the CLI/SDK only; `kinesisanalytics` for IAM, Service Quotas, CloudWatch, and the trust principal — two-phase IaC deploys, snapshot lifecycle, Flink 1.x→2.x migration) that override generic Flink knowledge.

Troubleshoots and debugs AWS Clean Rooms collaboration issues related to IAM roles, S3 bucket policies, KMS keys, Lake Formation permissions, and CloudWatch logging for custom ML model training and inference jobs. Use when a customer reports permission failures, access errors, or log publishing issues in Clean Rooms.

Routes AWS networking requests to the correct service skill for implementation. Covers Route 53 (DNS, health checks, routing policies, Resolver, DNS Firewall), CloudFront (caching, edge, OAC, mTLS, signed URLs), Transit Gateway (multi-VPC hub, segmentation, centralized egress), Direct Connect (hybrid link, DX Gateway, MACsec), Site-to-Site VPN (IPsec tunnels, static or BGP), WAF (web ACLs, AWS Managed Rules, rate-based rules, Bot and Fraud Control), and Shield Advanced (L3/L4 DDoS). Applicable when creating, configuring, troubleshooting, or designing across these services, choosing between them, or diagnosing connectivity or traffic-filtering issues. Not for VPC subnets and route tables, load balancers, VPC endpoints, PrivateLink, API Gateway, IAM policy logic, container or serverless networking, or IaC authoring.

Authors, deploys, and troubleshoots AWS infrastructure using CDK with TypeScript or Python. Covers best practices, stack architecture, and construct patterns. Applies when writing CDK constructs, bootstrapping environments, running cdk deploy/synth/diff, fixing CDK or CloudFormation errors, planning stack structure, importing existing resources, resolving drift, or refactoring stacks without resource replacement.

Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching, quota health checks and throttling diagnosis, cost attribution, migrating between Claude model generations, chunking strategies, API selection (Converse vs InvokeModel), and model selection. Also covers AgentCore Payments setup (x402, microtransactions, Payment Manager, Coinbase CDP, Stripe Privy, 402 Payment Required, paid endpoint). NOT for custom model training, Rekognition, or Comprehend.

Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).

Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift). Triggers on phrases like: query data, run SQL, athena query, analyze table, SQL query, workgroup status, profile table, query Redshift catalog, query S3 Tables. Do NOT use for finding specific data assets (use finding-data-lake-assets), full catalog audits (use exploring-data-catalog), importing data (use ingesting-into-data-lake).

Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.

Resolve data lake and lakehouse asset references across Glue Data Catalog, S3, S3 Tables, and Redshift. Triggers on: find the table, where is our data, which table has, locate dataset, find data for, search catalog, what tables match, Redshift table, lakehouse table, data lake table, warehouse table, reverse lookup S3 path. Do NOT use for: full catalog audits (use exploring-data-catalog), running queries (use querying-data-lake), creating tables (use creating-data-lake-table).

Full inventory and audit of AWS Glue Data Catalog assets across S3 Tables, Redshift-federated, and remote Iceberg catalogs. Triggers on: inventory the catalog, audit databases, list all tables, catalog overview, data landscape, enumerate catalogs, data inventory, search the catalog. Do NOT use for finding specific data (use finding-data-lake-assets), running queries (use querying-data-lake), or creating tables (use creating-data-lake-table).