Managing Amazon Msk

AWSOfficialPublished by AWS

Skill
Tool integration
Cloud and DevOps
Apache-2.0
Low risk
Has scripts

From the author

Operates Amazon MSK Provisioned clusters (Standard and Express brokers). Required for ANY MSK Provisioned task — training data conflates Standard and Express. Covers performance, consumer lag, storage, traffic shaping; sizing Standard vs Express; Kafka client tuning; CloudWatch alarms; cluster configs; maintenance, patching, rolling restarts; topic management; MSK Replicator setup for DR and active-active/active-passive replication (same-/cross-account), including throughput and partition sizing; Streaming Tables for S3 Tables and Data Delivery for General Purpose S3 — setup, IAM, monitoring. Prefer Flink for Kafka Iceberg sink questions. Migrations: use migrate-to-msk. Triggers: MSK Provisioned, Kafka, `kafka.*`/`express.*` types, AWS/Kafka namespace, cross-cluster replication, 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 — search docs instead. Serverless: only S3 Tables/data delivery eligibility.

— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides domain expertise for operating Amazon MSK Provisioned clusters. It covers performance troubleshooting, consumer lag diagnosis, storage management, cluster sizing, client configuration, and CloudWatch monitoring. It distinguishes between Standard and Express broker architectures, providing specific workflows for maintenance, topic management, and MSK Replicator setup. The skill also includes guidance on integrating with S3 via Streaming Tables and Data Delivery for General Purpose S3.

What it does

Provides configuration patterns for Kafka producers and consumers.
Offers workflows for managing MSK cluster configurations and custom domain names.
Includes a sizing script for determining broker counts and instance types.
Details procedures for MSK Replicator setup for DR and cross-account replication.
Outlines monitoring and alerting strategies using CloudWatch metrics.
Guides the setup of Streaming Tables and Data Delivery for S3.

When to use it

When configuring or tuning Kafka clients for MSK connectivity.
When sizing a new MSK cluster or choosing between Standard and Express brokers.
When troubleshooting performance issues, consumer lag, or storage constraints.
When setting up cross-cluster replication or disaster recovery.
When integrating MSK with S3 for data lake or streaming table requirements.

Advantages

Provides clear decision frameworks for Standard vs. Express broker types.
Includes automated sizing logic via provided scripts.
Centralizes operational procedures for common MSK maintenance tasks.
Offers specific guidance for complex configurations like custom advertised listeners.

Usage examples

“How do I choose between MSK Standard and Express brokers for my workload?”: The agent will guide you through the size-and-choose-cluster.md reference and run the msk_sizing.py script to compare costs and performance characteristics.
“My consumer lag is increasing, what should I check?”: The agent will provide a troubleshooting workflow based on troubleshoot-consumer-lag.md, covering rebalance issues and consumer group health.
“How do I configure a custom domain name for my MSK brokers?”: The agent will explain how to use the custom.advertised.listeners configuration property as detailed in configure-cluster.md.
“What are the recommended producer settings for MSK?”: The agent will provide the table of recommended values for settings like linger.ms, batch.size, and acks from the configure-clients.md reference.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/managing-amazon-msk/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip managing-amazon-msk.zip -d .agents/skills

Documentation by AWS

Amazon MSK

Overview

Domain expertise for operating Amazon MSK Provisioned clusters with Standard and Express broker types. Covers performance troubleshooting, consumer lag diagnosis, storage management, cluster sizing, client configuration, and CloudWatch monitoring.

Execute commands using available tools from the AWS MCP server when connected — it provides sandboxed execution, audit logging, and observability. When the MCP server is not available, fall back to the AWS CLI or shell as needed.

Standard brokers use customer-managed EBS volumes for storage. You choose instance types (kafka.m5/m7g families), provision EBS, and manage storage scaling.

Express brokers use instance types prefixed with express.m7g and are the default recommendation for almost all MSK workloads — they typically cost less, not just less effort. Up to 3x ingress per broker (MSK Express broker types) means fewer brokers for the same load, and storage is billed per GB-hour on data actually retained rather than provisioned up front on EBS that cannot shrink. They also scale 20x faster, rebalance partitions 180x faster (Intelligent Rebalancing), recover 90% quicker (MSK Express broker types), and have no maintenance windows. Express brokers have NO customer-managed EBS — do NOT recommend EBS expansion or provisioned throughput for Express clusters. Express brokers enforce fixed replication factor of 3 and min.insync.replicas=2. See size-and-choose-cluster.md for the full Standard vs Express decision framework.

Which Workflow Do You Need?

Determine the broker type first: aws kafka describe-cluster-v2 --cluster-arn <arn>. Check Provisioned.BrokerNodeGroupInfo.InstanceType — if it starts with express., it is an Express cluster.

Customer Intent	Reference
High CPU, high latency, slow cluster, traffic shaping	troubleshoot-performance.md
Consumer lag increasing, rebalance storms, stuck consumer groups	troubleshoot-consumer-lag.md
Disk filling up, retention planning, tiered storage	manage-storage.md
Choosing Standard vs Express, sizing a cluster, partition limits, broker count, monthly cost	size-and-choose-cluster.md
Producer/consumer configuration, IAM/SCRAM/TLS auth	configure-clients.md
Creating/applying MSK configurations (`server.properties`); custom domain names on brokers via `custom.advertised.listeners` (advertised listeners, static/custom bootstrap endpoint) — validation rules, apply/rollback, scaling; migrating from the dynamic per-broker `kafka-configs.sh` override to the static property	configure-cluster.md
Client-side connectivity for a custom domain: NLB + ACM certificate + Route 53 fronting, TLS handshake/termination, mTLS through an NLB, cross-zone load balancing	configure-clients.md (Custom Domain Name Connectivity section)
Create, update, delete, list, or describe Kafka topics; when to use the native topic APIs / `CfnTopic` vs. a Kafka admin client	manage-topics.md
MSK Replicator setup, migration, DR, active-active/active-passive, cross-account replication, self-managed-to-MSK or MSK-to-MSK, loop-prevention/topic-naming behavior	msk-replicator.md
Replication throughput per partition, how many partitions a replicator needs, why replicator scaling isn't increasing throughput, replicator quotas (topics, ingress, record size), `ReplicationLatency`/`MessageLag`/`ReplicatorThroughput`	msk-replicator.md (Size partition count section)
Setting up monitoring, dashboards, alarms	monitor-and-alarm.md
Full CloudWatch metric list (Standard or Express)	Prefer monitor-and-alarm.md for strategic recommendations and how to interpret metrics, only search documentation if you need to understand a metric not included in this reference file (MSK Standard CloudWatch Metrics, MSK Express CloudWatch Metrics) for full list
Rolling restart impact, patching, maintenance resilience	maintenance-operations.md
Deliver streaming data to Apache Iceberg tables on S3 Tables with low cost in a fully managed service (Streaming Tables) — setup, IAM, schema, create/update/delete/list/describe channels	streaming-tables.md
Deliver topic data to S3 bucket as JSON/ByteArray/String objects with low cost in a fully managed service (Data Delivery for General Purpose S3 buckets) — setup, IAM, output key templates, create/update/delete/list/describe channels	data-delivery-for-general-purpose-s3.md
Build a lakehouse / data lake from Kafka; make streaming data queryable in Athena	streaming-tables.md
Alternative to Kafka Connect S3 Sink or Amazon Data Firehose for MSK; zero-ops streaming delivery to S3	data-delivery-for-general-purpose-s3.md
Streaming Tables / Data Delivery CloudWatch metrics and alarms, DLQ errors, failed deliveries, channel state transitions, freshness lag	streaming-tables-troubleshooting.md
"Can I use Streaming Tables / Data Delivery on MSK Serverless / Standard brokers?" — eligibility routing	streaming-tables.md (answer is always: Express brokers only, use Firehose, Flink, or Kafka Connect for Standard and Serqverless - Firehose integration for Amazon MSK)
What are the current supported Kafka versions for MSK?	Supported Apache Kafka versions
Does MSK support KRaft clusters, and how do I migrate between ZooKeeper and KRaft mode clusters?	zk-to-kraft-migration.md
What are the current quotas for MSK Express (ingress, egress, partitions, broker count, etc.)?	MSK Express Quotas
What are the current quotas for MSK Standard (partitions, broker count, etc.)?	MSK Standard Quotas, and MSK Standard best practices for partition count limits
What broker-level configuration changes can I make on MSK Express or Standard brokers?	MSK Configuration

Available scripts

scripts/msk_sizing.py — MUST be run for any sizing question (broker count, instance choice, cost). Prices from the live AWS Pricing API when AWS credentials with pricing:GetProducts are available, and falls back to an embedded us-east-1 snapshot when they are not, so it runs with or without credentials. See size-and-choose-cluster.md for the required workflow and script reference.

Guardrail — where this skill's own files live (MCP vs local install)

This skill can be loaded two ways, and they resolve the skill's own bundled files — the references/ documents and the scripts/ files from different places. Determine how the skill was loaded before you read a reference or run a script:

Loaded through the AWS MCP retrieve_skill tool call. The skill is not installed on the local filesystem; its reference files and scripts do not exist on disk. You MUST fetch each reference or script through the same retrieve_skill tool by passing the file parameter (for example, file="references/configure-clients.md" or file="scripts/msk_sizing.py"), and run a script from the content that tool returns. Do NOT file_read these paths from the local or working directory, and do NOT search the filesystem for them — they are not there, and any local file that happens to match the name is unrelated to this skill.
Installed locally (the skill lives in a local skills directory such as .claude/skills/managing-amazon-msk/, ~/.claude/skills/managing-amazon-msk/, or .kiro/skills/managing-amazon-msk/). Read references and run scripts from the local skill directory using the relative paths shown throughout this documentation.

This distinction applies only to the skill's own packaged files. Every artifact created during a session or supplied by users are read from and written to the user's working directory regardless of how the skill was loaded. Never fetch or write customer data through retrieve_skill.

Common Workflows

Create/apply Amazon MSK configurations and set custom domain names — creating an Amazon MSK configuration (server.properties with the fileb:// real-newline requirement), applying it with update-cluster-configuration, and setting broker custom domain names via custom.advertised.listeners: see configure-cluster.md. For the NLB/certificate/DNS connectivity that fronts a custom domain, see configure-clients.md.

Files

ATTRIBUTION.mdAdded by AutoGineer918 B
LICENSELicence file added by AutoGineer9.9 KiB
NOTICELicence file added by AutoGineer67 B
SKILL.md11.0 KiB
references/configure-clients.md13.3 KiB
references/configure-cluster.md11.3 KiB
references/data-delivery-for-general-purpose-s3.md15.7 KiB
references/maintenance-operations.md13.0 KiB
references/manage-storage.md8.5 KiB
references/manage-topics.md7.0 KiB
references/monitor-and-alarm.md15.4 KiB
references/msk-replicator.md18.9 KiB
references/size-and-choose-cluster.md17.2 KiB
references/streaming-tables-troubleshooting.md5.3 KiB
references/streaming-tables.md19.5 KiB
references/troubleshoot-consumer-lag.md11.5 KiB
references/troubleshoot-performance.md9.3 KiB
references/zk-to-kraft-migration.md728 B
scripts/msk_sizing.pyScript62.6 KiB

references/manage-topics.md

Managing Kafka Topics on Amazon MSK

Two ways to manage topics on MSK Provisioned clusters: the native MSK topic operations APIs (CreateTopic/DescribeTopic/UpdateTopic/DeleteTopic/ListTopics) and Kafka's own admin protocol via a Kafka client (kafka-topics.sh, kafka-python, etc., or AWS::MSK::Topic/CfnTopic in IaC, which wraps the native API). Prefer the native MSK APIs / CfnTopic over standing up Kafka admin clients whenever the cluster and Kafka version support them.

Prefer the native MSK topic APIs over Kafka admin clients

ListTopics, DescribeTopic, DescribeTopicPartitions, CreateTopic, UpdateTopic, and DeleteTopic are AWS APIs (CLI, SDK, CloudFormation, console) that manage Kafka topics without needing network access to brokers, a Kafka client library, or IAM/SCRAM/mTLS client auth setup. Use these by default instead of building a Kafka admin client (bastion host, Lambda with kafka-python, etc.) for topic CRUD.

Requirements — check these before assuming the API is available:

MSK Provisioned only — not available for MSK Serverless.
Kafka version 3.6.0 or later.
Cluster must be in ACTIVE state.
Standard IAM permissions (below) — the calling identity does not need Kafka-level SASL/IAM client auth, only the kafka-cluster:* IAM actions on the cluster/topic ARN.

Propagation lag: topic API responses reflect state that updates approximately every minute. After a create/update/delete, allow about a minute before querying and expecting the change reflected — don't treat an immediate stale read as a failure.

IAM permissions per operation

Operation	Required actions	Resource(s)
`ListTopics`	`kafka-cluster:Connect`, `kafka-cluster:DescribeTopic`	Cluster ARN, Topic ARN (wildcard `*` allowed to match all topics)
`DescribeTopic`	`kafka-cluster:Connect`, `kafka-cluster:DescribeTopic`, `kafka-cluster:DescribeTopicDynamicConfiguration`	Cluster ARN, Topic ARN
`DescribeTopicPartitions`	`kafka-cluster:Connect`, `kafka-cluster:DescribeTopic`, `kafka-cluster:DescribeTopicDynamicConfiguration`	Cluster ARN, Topic ARN
`CreateTopic`	`kafka-cluster:Connect`, `kafka-cluster:CreateTopic`	Cluster ARN, Topic ARN
`UpdateTopic`	`kafka-cluster:Connect`, `kafka-cluster:DescribeTopic`, `kafka-cluster:AlterTopic`, `kafka-cluster:AlterTopicDynamicConfiguration`	Cluster ARN, Topic ARN
`DeleteTopic`	`kafka-cluster:Connect`, `kafka-cluster:DescribeTopic`, `kafka-cluster:DeleteTopic`	Cluster ARN, Topic ARN

kafka-cluster:Connect is scoped to the cluster ARN in the policy; every other action is scoped to the topic ARN. A policy built by copying a typical produce/consume permission set often omits kafka-cluster:DeleteTopic — add it explicitly whenever a workflow needs to delete topics (including replicator-created internal bookkeeping topics, see msk-replicator.md), since its absence only surfaces as a TopicAuthorizationException at delete time, not at policy-attach time.

CLI usage

# Create
aws kafka create-topic \
  --cluster-arn <cluster-arn> \
  --topic-name orders \
  --partition-count 3 \
  --replication-factor 3

# List / describe
aws kafka list-topics --cluster-arn <cluster-arn>
aws kafka describe-topic --cluster-arn <cluster-arn> --topic-name orders
aws kafka describe-topic-partitions --cluster-arn <cluster-arn> --topic-name orders

# Update (e.g. change retention via base64-encoded configs)
aws kafka update-topic \
  --cluster-arn <cluster-arn> \
  --topic-name orders \
  --configs $(echo -n '{"retention.ms":"604800000"}' | base64)

# Delete
aws kafka delete-topic --cluster-arn <cluster-arn> --topic-name orders

--configs is a Base64-encoded JSON object of topic-config key/value pairs (e.g. {"retention.ms":"604800000","cleanup.policy":"delete"}) — not the server.properties line format used elsewhere for broker/cluster configuration (see the create cluster configuration workflow in the main SKILL.md, which is a different API and does use real-newline key=value lines). Encode the JSON string, not individual lines, before base64-encoding it. DescribeTopic/ListTopics responses return configs the same way — base64-decode to get the JSON object back, not properties-file text.

IaC: prefer `AWS::MSK::Topic` (`CfnTopic`) over a custom resource

aws-cdk-lib (≥ ~2.251) ships AWS::MSK::Topic / CfnTopic natively — it wraps the same CreateTopic/UpdateTopic/DeleteTopic APIs above. Use it directly for any topic that needs to be provisioned as part of a stack. Do not build a Lambda-backed custom resource (e.g. Python + kafka-python + an IAM SASL token provider) to create topics — this requires bundling a Kafka client, VPC placement for the Lambda, dependency/version management, and reimplements what the native resource already does with none of that overhead.

const topic = new msk.CfnTopic(this, 'OrdersTopic', {
  clusterArn: cluster.attrArn,
  topicName: 'orders',
  partitionCount: 3,
  replicationFactor: 3,
  // Optional: base64-encoded JSON object of topic configs (not server.properties lines)
  // configs: Buffer.from(JSON.stringify({ 'retention.ms': '604800000' })).toString('base64'),
});
topic.node.addDependency(cluster); // topic creation requires an ACTIVE cluster

Same version/Kafka-version constraints apply as the CLI/API above (Provisioned only, Kafka 3.6.0+, cluster must be ACTIVE before the topic resource can be created — add an explicit addDependency on the cluster construct since CloudFormation doesn't otherwise know to wait for ACTIVE).

When you still need a Kafka admin client

Some topic-adjacent operations aren't covered by the MSK topic APIs (e.g. per-partition reassignment beyond what UpdateTopic exposes, consumer group management beyond what Replicator handles, ACL management outside IAM policies). For those, fall back to kafka-topics.sh/kafka-consumer-groups.sh from a bastion or client host with IAM or SASL/SCRAM auth configured — see configure-clients.md for client auth setup.

References

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Python

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Attribution and licence

Published by

AWS

Publisher

AWS (Official)

Source

aws/agent-toolkit-for-aws · plugins/aws-data-analytics/skills/managing-amazon-msk · commit 0a218f8

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
NOTICE (added by AutoGineer from NOTICE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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aws/agent-toolkit-for-aws

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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download managing-amazon-msk.zip