Msk Operations logo

Msk Operations

Organization
aws
msk-operations

Amazon MSK Provisioned operations, troubleshooting, and health assessment for Standard and Express brokers. Use whenever the user mentions Amazon MSK, MSK Provisioned, MSK Standard/Express brokers, Apache Kafka on AWS, `kafka.*` / `express.*` instance types, or the `AWS/Kafka` CloudWatch namespace. Covers MSK performance issues (high CPU, produce/fetch latency, TrafficShaping), consumer lag, storage/EBS issues, rolling restarts, Kafka version upgrades, SECURITY_PATCHING, BROKER_UPDATE, CloudWatch alarm design, Kafka client (producer/consumer) tuning, under-replicated partitions, unexpected broker reboots, and full MSK operational reviews / health checks / best-practices audits. Do NOT use for MSK Connect, MSK Serverless, or MSK Replicator. Do NOT use for authoring CloudFormation, CDK, or Terraform templates. Do NOT use for other AWS services (RDS, Aurora, S3, DynamoDB, Kinesis, Lambda, EC2) unless MSK is explicitly named.

Overview

Publisheraws
Repositorytools-for-devops-agent
Skill namemsk-operations
Stars
120
Forks
97
Bundled files
12
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 12 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aws on GitHub. Read the source before you install it.

Installation

Install the Msk Operations AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aws/tools-for-devops-agent/tree/main/skills/msk-operations
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aws/tools-for-devops-agent.git /tmp/tools-for-devops-agent
mkdir -p .claude/skills
cp -r /tmp/tools-for-devops-agent/skills/msk-operations .claude/skills/msk-operations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Msk Operations in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Msk Operations on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Msk Operations is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Amazon MSK Operations

Operate, troubleshoot, and assess Amazon MSK (Managed Streaming for Apache Kafka) Provisioned clusters — both Standard and Express broker types. This skill covers day-to-day operations (health assessments, monitoring setup) and ad-hoc incident response (performance degradation, consumer lag, storage full, unexpected broker reboots).

When to Use

Activate this skill when the user asks to:

  • Review, audit, or assess an MSK cluster for best practices, health, or operational readiness.
  • Troubleshoot an MSK cluster problem: high CPU, high produce/fetch latency, consumer lag, broker storage running out, TrafficShaping events, under-replicated partitions, or an unexpected broker restart.
  • Set up MSK monitoring: choose a monitoring level, create recommended CloudWatch alarms and dashboards, understand the metrics available in the AWS/Kafka namespace.
  • Plan an MSK maintenance event: rolling restart, Kafka version upgrade, security patching, broker instance type change.
  • Advise on Kafka client (producer / consumer) configuration when the client is connecting to an MSK cluster.

Do not activate this skill for MSK Connect, MSK Serverless, or MSK Replicator — those are separate services with their own operational surfaces.

Broker Type Determination

Determine the broker type first — many checks differ between Standard and Express.

aws kafka describe-cluster-v2 --cluster-arn <cluster-arn>

Check ClusterInfo.Provisioned.BrokerNodeGroupInfo.InstanceType:

  • Starts with kafka. (e.g. kafka.m5.large, kafka.m7g.xlarge) → Standard broker.
  • Starts with express. (e.g. express.m7g.large) → Express broker.

Key Standard vs Express differences

Standard brokers use customer-managed EBS volumes for storage. You choose instance types (kafka.m5.*, kafka.m7g.*), provision EBS, and manage storage scaling. Standard brokers have scheduled maintenance windows.

Express brokers provide fully managed, pay-as-you-go storage with no EBS provisioning. Instance types are prefixed with express.m7g.*. Express brokers offer up to 3× more throughput per broker than Standard, and have no maintenance windows. Express enforces a fixed replication factor of 3 and min.insync.replicas=2 — you cannot create topics with RF=1.

Critical Warnings

  • This skill is read-only. Every command in this file and in references/ that mutates cluster state — update-broker-storage, create-configuration, update-cluster-configuration, update-monitoring, put-metric-alarm, reboot-broker, and any partition reassignment — is a recommendation for the operator to run after review. Present these as proposed remediations with expected impact and preconditions; do NOT execute them, and do NOT imply that the agent will run them.
  • NEVER reboot brokers while UnderReplicatedPartitions > 0 (Standard only — Express brokers do not emit URP). This risks data loss and extended outages.
  • NEVER recommend partition reassignment without first checking replication status. Reassignment during URP compounds the problem.
  • linger.ms=0 is the #1 cause of "high CPU" on MSK. ALWAYS check client batch configuration before recommending broker scaling.
  • EBS throughput ceilings are invisible in Kafka metrics — ALWAYS check EBS volume metrics (VolumeReadBytes, VolumeWriteBytes, VolumeQueueLength) when diagnosing Standard broker latency.
  • Express brokers have NO customer-managed EBS — do NOT recommend EBS expansion or provisioned EBS throughput for Express clusters.
  • Express brokers enforce fixed RF=3 and min.insync.replicas=2 — do NOT attempt to create topics with RF=1 on Express. If RF=1 is needed, use Standard brokers.

Quick Diagnostics

These five checks cover the most common MSK issues. Use them before loading a reference file.

  1. CpuUser + CpuSystem > 60%: Check RequestHandlerAvgIdlePercent (PER_BROKER monitoring level). If < 30%, request threads are saturated. Check client batch.size and linger.ms before recommending scaling.

  2. KafkaDataLogsDiskUsed > 85% (Standard only): Recommend to the operator that EBS be expanded via aws kafka update-broker-storage (do not execute). Identify high-growth topics via per-topic BytesInPerSec to size the increase. Express clusters use StorageUsed metric instead and storage is fully managed.

  3. UnderReplicatedPartitions > 0 (Standard only): Check if a maintenance operation or broker restart is in progress. If URP is decreasing, wait for recovery. Do NOT restart brokers or reassign partitions during URP. Express brokers do not emit this metric — monitor ProduceThrottleTime, FetchThrottleTime, and consumer lag instead.

  4. Consumer OffsetLag / MaxOffsetLag increasing: Determine if broker-side (high ProduceTotalTimeMsMean, CPU saturation) or client-side (slow processing, insufficient consumers). Per-partition lag from PER_TOPIC_PER_PARTITION monitoring level helps isolate hot partitions.

  5. BytesInPerSec near throughput ceiling: For Standard, check EBS volume type and calculate: BytesInPerSec × ReplicationFactor vs volume throughput limit. For Express, check against the per-broker sustained performance limits in the MSK quotas.

Which Reference Do You Need?

Route to a reference file based on the customer intent. Read the reference in full before answering — do not paraphrase from memory.

Customer IntentReference
High CPU, high produce/fetch latency, slow cluster, TrafficShapingreferences/troubleshoot-performance.md
Consumer lag increasing, rebalance storms, stuck consumer groupsreferences/troubleshoot-consumer-lag.md
Disk filling up, retention planning, tiered storage, EBS scalingreferences/manage-storage.md
Setting up monitoring level, dashboards, recommended CloudWatch alarmsreferences/monitor-and-alarm.md
Rolling restart impact, patching, Kafka version upgrades, maintenance resiliencereferences/maintenance-operations.md
Producer / consumer configuration, IAM / SCRAM / TLS auth for clientsreferences/configure-clients.md

For sizing questions (broker count, instance type choice, monthly cost), refer the user to the Amazon MSK best practices — right-size your cluster documentation. Do not size from memory.

Operational Review Workflow

Use this workflow when the user asks for a review, audit, health check, or assessment of an MSK cluster. The routing table above handles ad-hoc troubleshooting; this section produces a consistent, comprehensive report.

Follow the steps in order for each target cluster. Do not skip steps. If a step cannot be completed (e.g. a metric requires a higher monitoring level than the cluster has enabled), record the gap in the report rather than silently omitting the check.

Step 1 — Identify Target Clusters

Ask the user which MSK clusters to review. Accept any of:

  • Specific cluster names or ARNs and regions
  • "all clusters" in specific regions
  • "all MSK clusters in all regions"

If no scope is given, default to all configured account regions. Enumerate clusters with aws kafka list-clusters-v2 per region.

Step 2 — Determine Broker Type Per Cluster

For each cluster:

aws kafka describe-cluster-v2 --cluster-arn <cluster-arn>

Read ClusterInfo.Provisioned.BrokerNodeGroupInfo.InstanceType. Standard brokers (kafka.*) and Express brokers (express.*) require different checks in the later steps — some metrics only exist on one type.

Step 3 — Collect Cluster Configuration

For each cluster, gather:

aws kafka describe-cluster-v2 --cluster-arn <arn>
aws kafka list-nodes --cluster-arn <arn>
aws kafka get-bootstrap-brokers --cluster-arn <arn>
aws kafka list-cluster-operations-v2 --cluster-arn <arn>   # last 30 days
aws kafka describe-configuration-revision \
     --arn <configuration-arn> --revision <revision>       # if a custom config is applied

Capture:

  • Cluster: state, Kafka version, number of broker nodes, AZ distribution (ZoneIds), storage mode (EBS / Tiered), current version.
  • Broker: instance type, EBS volume size (Standard), provisioned throughput (if any).
  • Encryption: EncryptionInTransit.ClientBroker (TLS / TLS_PLAINTEXT / PLAINTEXT), EncryptionInTransit.InCluster, EncryptionAtRest.DataVolumeKMSKeyId.
  • Auth: ClientAuthentication.Sasl.Iam.Enabled, ClientAuthentication.Sasl.Scram.Enabled, ClientAuthentication.Tls.Enabled, ClientAuthentication.Unauthenticated.Enabled.
  • Public access: BrokerNodeGroupInfo.ConnectivityInfo.PublicAccess.Type.
  • Monitoring level: EnhancedMonitoring (DEFAULT / PER_BROKER / PER_TOPIC_PER_BROKER / PER_TOPIC_PER_PARTITION).
  • Logging: LoggingInfo.BrokerLogs (CloudWatch / S3 / Firehose destinations and their Enabled flags).
  • Open monitoring: OpenMonitoring.Prometheus.JmxExporter.EnabledInBroker, NodeExporter.EnabledInBroker.
  • Recent operations: From list-cluster-operations-v2, note any SECURITY_PATCHING, BROKER_UPDATE, UPDATE_CLUSTER_CONFIGURATION, UPDATE_STORAGE, or UPDATE_MONITORING events in the review window.

Step 4 — Detect Monitoring Level and Gaps

The EnhancedMonitoring value from Step 3 determines which checks are available. At DEFAULT, most per-broker health metrics are still available (CPU, disk, network, partitions, connections, memory, TrafficShaping), but the following checks are not possible without upgrading:

  • ReplicationBytesInPerSec / ReplicationBytesOutPerSec (inter-broker replication load)
  • RequestHandlerAvgIdlePercent / NetworkProcessorAvgIdlePercent (thread pool saturation)
  • BwInAllowanceExceeded / BwOutAllowanceExceeded (detailed bandwidth breaches)
  • VolumeQueueLength (EBS I/O queue depth — Standard only)
  • VolumeReadBytes / VolumeWriteBytes (EBS throughput utilization — Standard only)
  • IAM connection metrics (IAMTooManyConnections)

Record the monitoring level and list any dimensions that will be scored partially or skipped. Recommend upgrading to PER_BROKER if any dimension is degraded by the current level.

Step 5 — Collect CloudWatch Metrics (7-Day Historical)

Namespace: AWS/Kafka. Dimensions: Cluster Name and Broker ID for per-broker metrics; Cluster Name and Consumer Group and Topic for consumer lag; Cluster Name only for cluster-wide metrics.

Use one cloudwatch.GetMetricData batch per cluster where possible. Period: 3600 (1 hour). StartTime: 7 days ago. EndTime: now.

5.1 Cluster-wide (both broker types)
MetricStatPurpose
ActiveControllerCountSumMust be exactly 1
OfflinePartitionsCountMaximumMust be 0
GlobalPartitionCountMaximumTotal leader partitions
GlobalTopicCountMaximumTotal topics
5.2 Per-broker — Standard (kafka.*)
MetricStatThreshold
CpuUser + CpuSystemAverage, Maximum< 60% avg
KafkaDataLogsDiskUsedAverage, Maximum< 70% avg, < 85% max
PartitionCountMaximum≤ recommended limit for broker size
LeaderCountMaximumCompare across brokers; skew < 10%
UnderReplicatedPartitionsMaximum0 in steady state
UnderMinIsrPartitionCountMaximum0
BytesInPerSec / BytesOutPerSecAverage, Maximumvs baseline bandwidth
ConnectionCountAverage, MaximumCompare across brokers
HeapMemoryAfterGCMaximum< 60%
TrafficShapingSumMust be 0
NetworkRxDropped / NetworkTxDropped / NetworkRxErrors / NetworkTxErrorsSumMust be 0
ReplicationBytesInPerSec / ReplicationBytesOutPerSecAverageRequires PER_BROKER
RequestHandlerAvgIdlePercent / NetworkProcessorAvgIdlePercentAverage> 30% (PER_BROKER)
VolumeQueueLengthAverage, MaximumAvg < 1 (PER_BROKER)
VolumeReadBytes + VolumeWriteBytesSumvs EBS baseline throughput (PER_BROKER)
5.3 Per-broker — Express (express.*)
MetricStatThreshold
CpuUser + CpuSystemAverage, Maximum< 60% avg
StorageUsedMaximumFully managed — flag if trending against per-broker quota
PartitionCountMaximum≤ recommended limit for broker size
LeaderCountMaximumCompare across brokers
BytesInPerSec / BytesOutPerSecAverage, Maximumvs Express per-broker ingress/egress quotas
ProduceThrottleTime / FetchThrottleTimeMaximumShould be 0
ClientConnectionCountAverage, Maximumvs listener quota (see references/monitor-and-alarm.md)

Express brokers do NOT emit: UnderReplicatedPartitions, UnderMinIsrPartitionCount, HeapMemoryAfterGC, TrafficShaping, Volume* metrics, KafkaDataLogsDiskUsed, ProduceMessageConversionsPerSec, FetchMessageConversionsPerSec.

5.4 Consumer lag (both broker types)

Per consumer group (identified from the customer or from the broker logs):

MetricStatNotes
SumOffsetLag per (Consumer Group, Topic)MaximumDEFAULT level
MaxOffsetLag per (Consumer Group, Topic)MaximumDEFAULT level
EstimatedMaxTimeLag per (Consumer Group, Topic)MaximumDEFAULT level
OffsetLag per (Consumer Group, Topic, Partition)MaximumRequires PER_TOPIC_PER_PARTITION

Step 6 — Alarm Coverage

Collect existing alarms:

aws cloudwatch describe-alarms --namespace AWS/Kafka

Compare against the 13-alarm recommended set (details in references/monitor-and-alarm.md) and produce a coverage table (present / missing / firing).

Step 7 — Analyze Against Best Practices

Assign a severity to every finding: CRITICAL / HIGH / MEDIUM / LOW / INFO (see Severity Definitions later in this file).

Evaluate across seven dimensions. Some checks are skipped for Express — noted inline.

7.1 Cluster Configuration
  • Cluster state is ACTIVE. MAINTENANCE / UPDATING is transient; anything else is a finding.
  • Deployed across 3 AZs (Standard: broker count multiple of AZ count).
  • Kafka version within N-2 of the latest supported.
  • Enhanced monitoring at PER_BROKER or higher — DEFAULT → MEDIUM.
  • Storage mode: Tiered storage enabled for topics with long retention (Standard).
7.2 Security
  • Encryption in transit: TLS. TLS_PLAINTEXT → MEDIUM (prod: HIGH). PLAINTEXT → CRITICAL.
  • Encryption at rest: enabled with KMS. Customer-managed KMS preferred over AWS-managed for regulated workloads.
  • Authentication: at least one of IAM / SCRAM / mTLS enabled. Unauthenticated.Enabled=true in production → CRITICAL.
  • Public access: SERVICE_PROVIDED_EIPS in production → HIGH.
7.3 Logging & Monitoring
  • Broker logs enabled to at least one destination (CloudWatch / S3 / Firehose). All disabled → HIGH.
  • Open monitoring (Prometheus JMX + Node exporter) — informational.
  • Alarm coverage from Step 6: missing alarms → MEDIUM each; missing critical alarms (Active Controller, Offline Partitions, Disk, CPU) → HIGH.
  • Any alarm currently in ALARM state → HIGH (surface in report header).
7.4 Partition Health
  • PartitionCount per broker ≤ recommended limit for the broker instance type. Above recommended but below max → MEDIUM. Above max → HIGH (blocks update operations).
  • LeaderCount variance across brokers < 10%. 10-25% → MEDIUM. > 25% → HIGH.
  • UnderReplicatedPartitions > 0 sustained (Standard only) → HIGH. Transient during a SECURITY_PATCHING / BROKER_UPDATE operation from Step 3 → INFO — do NOT flag.
  • UnderMinIsrPartitionCount > 0 (Standard only) → CRITICAL.
  • Config-level ISR risk: topics with min.insync.replicas >= replication.factor are a configuration error the CloudWatch metric will not surface. If the user has provided topic configs, flag any such topic as HIGH.
7.5 Compute Health
  • CpuUser + CpuSystem avg < 60%. 60-70% → MEDIUM. > 70% → HIGH.
  • CPU variance across brokers < 20%. 20-50% → MEDIUM. > 50% → HIGH.
  • HeapMemoryAfterGC < 60% (Standard only). 60-80% → MEDIUM. > 80% → HIGH.
  • RequestHandlerAvgIdlePercent > 30% (PER_BROKER). 10-30% → MEDIUM. < 10% → HIGH.
  • NetworkProcessorAvgIdlePercent > 30% (PER_BROKER). 10-30% → MEDIUM. < 10% → HIGH.
7.6 Network Health
  • TrafficShaping = 0 (Standard only). Any non-zero → HIGH.
  • BwInAllowanceExceeded / BwOutAllowanceExceeded = 0 (PER_BROKER). Non-zero → HIGH.
  • Throughput variance across brokers < 20%. 20-50% → MEDIUM. > 50% → HIGH.
  • Total per-broker throughput < 60% of baseline bandwidth (see references/troubleshoot-performance.md for baseline table). 60-70% → MEDIUM. > 70% → HIGH.
  • Express: ProduceThrottleTime / FetchThrottleTime > 0 → HIGH.
7.7 Storage Health

Standard only (Express storage is managed — Express clusters only get the StorageUsed check):

  • KafkaDataLogsDiskUsed < 70%. 70-85% → HIGH. > 85% → CRITICAL.
  • VolumeQueueLength avg < 1 (PER_BROKER). 1-5 → MEDIUM. > 5 → HIGH.
  • EBS auto-scaling configured, or disk headroom > 30% → PASS. No auto-scaling AND disk > 50% → MEDIUM. No auto-scaling AND disk > 70% → HIGH.
  • EBS throughput (VolumeReadBytes + VolumeWriteBytes) < 60% of instance baseline (PER_BROKER). 60-70% → MEDIUM. > 70% → HIGH.

Express:

  • StorageUsed per broker vs Express per-broker storage quota (see MSK Express quotas). Approaching quota → MEDIUM.

Step 8 — Generate the Report

Generate a separate report artifact per cluster reviewed.

Artifact naming: msk-review-<cluster-name>-<YYYY-MM-DD>.md Example: msk-review-prod-orders-2026-04-29.md

Report structure:

Report Header
# MSK Operational Review — <cluster-name>
Account: <account-id> | Region: <region> | Date: <YYYY-MM-DD>
Broker Type: Standard/Express | Instance Type: <type> | Broker Count: <n> | AZs: <n>
Kafka Version: <version> | Monitoring Level: <level>
Executive Summary
  • Health: ✅ HEALTHY / ⚠️ WARNINGS / ❌ CRITICAL
  • Finding counts by severity
  • Top 3 CRITICAL/HIGH items
Configuration Snapshot

| Item | Value | | Cluster state / version | … | | Broker type / instance / count / AZs | … | | Storage | mode, size (Standard), provisioned throughput (if any) | | Encryption | in-transit, in-cluster, at-rest (KMS) | | Authentication | IAM / SCRAM / mTLS / Unauthenticated flags | | Public access | DISABLED / SERVICE_PROVIDED_EIPS | | Monitoring level | DEFAULT / PER_BROKER / PER_TOPIC_PER_BROKER / PER_TOPIC_PER_PARTITION | | Logging | destinations enabled |

Findings by Dimension

For each of the 7 dimensions (7.1-7.7):

| # | Finding | Severity | Current State | Recommendation |

If a dimension was skipped or partial due to monitoring level or broker type, say so explicitly in a note above the table for that dimension.

CloudWatch Metrics (7-Day)

| Metric | Stat | 7-Day Avg | 7-Day Max | Status | Finding |

Alarm Coverage

| # | Recommended Alarm | Metric | Threshold | Priority | Status |

Also list alarms currently in ALARM state with timestamps.

Recent Cluster Operations (Last 30 Days)

From list-cluster-operations-v2 in Step 3:

| Operation Type | Start Time | End Time | State |

Call out any operations that would explain transient metric anomalies.

Priority Matrix

| # | Finding | Severity | Dimension | Effort | Impact |

Sorted by severity.

Next Steps
  • Immediate (CRITICAL / HIGH — 7 days)
  • Short-term (MEDIUM — 30 days)
  • Long-term (LOW — 90 days)
Appendix — Reference Links

Common CLI Recipes

Describe cluster:

aws kafka describe-cluster-v2 --cluster-arn <cluster-arn>

List brokers:

aws kafka list-nodes --cluster-arn <cluster-arn>

Get bootstrap brokers:

aws kafka get-bootstrap-brokers --cluster-arn <cluster-arn>

List recent cluster operations (patching, config updates, storage changes):

aws kafka list-cluster-operations-v2 --cluster-arn <cluster-arn>

Expand Standard broker storage — Operator-run (recommend, do not execute):

aws kafka update-broker-storage \
  --cluster-arn <cluster-arn> \
  --current-version <cluster-version> \
  --target-broker-ebs-volume-info '[{"KafkaBrokerNodeId": "All", "VolumeSizeGB": <target-size>}]'

Get a CloudWatch metric (example: CpuUser per broker):

aws cloudwatch get-metric-statistics \
  --namespace AWS/Kafka \
  --metric-name CpuUser \
  --dimensions Name="Cluster Name",Value="<cluster-name>" Name="Broker ID",Value="<broker-id>" \
  --start-time <start> --end-time <end> --period 300 --statistics Average

Create a cluster configuration (server.properties) — Operator-run (recommend, do not execute):

The --server-properties argument MUST be a real Kafka properties file with one key=value per line, separated by actual newline characters — NOT the literal two-character escape sequence \n. The MSK API accepts the bytes as-is; if you pass "k1=v1\nk2=v2" as a single string with escaped newlines, MSK stores ONE invalid property line and the cluster will fail to apply it.

Recommended pattern: write the properties to a local file with real newlines, then pass it via fileb:// so the CLI uploads the raw bytes verbatim. Verify by reading the revision back with describe-configuration-revision and base64-decoding ServerProperties — you should see one property per line.

cat > server.properties <<'EOF'
auto.create.topics.enable=false
default.replication.factor=3
min.insync.replicas=2
unclean.leader.election.enable=false
num.io.threads=32
num.network.threads=16
log.retention.hours=168
EOF

aws kafka create-configuration \
  --name <config-name> \
  --kafka-versions "3.6.0" \
  --server-properties fileb://server.properties

Common Error Reference

ErrorCauseFix
aws kafka update-broker-storage returns "storage is optimizing"Previous storage expansion still in cool-down (minimum 6 hours)Wait for optimization to complete. Check cluster state with describe-cluster-v2.
ClusterState is MAINTENANCEStandard brokers undergoing patching. Express brokers stay ACTIVE during maintenance.Wait for cluster to return to ACTIVE. Do not perform update operations during MAINTENANCE.
Consumer receives GROUP_COORDINATOR_NOT_AVAILABLECoordinator broker is temporarily unavailable during rolling restart or overloadedRetry with backoff. Check if maintenance is in progress via list-cluster-operations-v2.
NotEnoughReplicasException on produceFewer brokers in ISR than min.insync.replicas (default: 2)Check UnderReplicatedPartitions (Standard only). For Express, check ProduceThrottleTime and broker health instead — URP is not available. If a broker is down for maintenance, this is transient. Do NOT lower min.insync.replicas to work around this.

Severity Definitions (for review-style reports)

SeverityDefinitionSLA
CRITICALImmediate risk to availability, security, or data integrityFix within 24–48 hours
HIGHSignificant gap that could lead to incidentsFix within 1 week
MEDIUMNotable improvement opportunityPlan within 30 days
LOWMinor optimization or hardeningAddress when convenient
INFOObservation, no action requiredN/A

Additional Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Msk Operations AI skill do?

Amazon MSK Provisioned operations, troubleshooting, and health assessment for Standard and Express brokers. Use whenever the user mentions Amazon MSK, MSK Provisioned, MSK Standard/Express brokers, Apache Kafka on AWS, `kafka.*` / `express.*` instance types, or the `AWS/Kafka` CloudWatch namespace. Covers MSK performance issues (high CPU, produce/fetch latency, TrafficShaping), consumer lag, storage/EBS issues, rolling restarts, Kafka version upgrades, SECURITY_PATCHING, BROKER_UPDATE, CloudWatch alarm design, Kafka client (producer/consumer) tuning, under-replicated partitions, unexpected...

Why use Msk Operations on TypingMind?

Because you install it once and use it with any model. Msk Operations is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Msk Operations in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/tools-for-devops-agent/tree/main/skills/msk-operations. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Msk Operations?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Msk Operations?

As many as you like. As long as a model supports skills, you can use Msk Operations with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Msk Operations AI skill free?

Yes. It is published on GitHub by aws under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇