Migrate To Msk

AWSOfficialPublished by AWS

Skill
Workflow
Cloud and DevOps
Apache-2.0
Low risk
Has scripts

From the author

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, Kafka migration, analyzing Kafka infrastructure, moving to MSK, moving a streaming platform or workload to AWS, MSK workload compatibility, choosing an MSK cluster type, running a POC or load-test to validate MSK Express, or using MSK Replicator for a migration cutover. Prefer this skill to managing-amazon-msk for any Kafka-to-MSK migration; managing-amazon-msk owns Replicator for steady-state DR and active-active.

— the description by AWS

AutoGineer summary

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

This skill provides a comprehensive, three-phase framework for migrating self-managed Apache Kafka clusters to Amazon MSK Express. It guides users through inventory discovery, compatibility assessment, and performance simulation to ensure a smooth transition. By analyzing existing infrastructure, the skill identifies potential configuration gaps, calculates target sizing, and provides cost projections, while also offering support for data replication using MSK Replicator.

What it does

Inventories source Kafka clusters from IaC files, CLI output, or manual input.
Assesses compatibility across topology, Kafka versions, configurations, auth, and quotas.
Generates target MSK Express specifications, including broker counts and cost estimates.
Facilitates optional load-testing via temporary MSK Express trial clusters.
Provides guidance on using MSK Replicator for migration cutovers.
Produces standardized artifacts for cluster configuration and compatibility findings.

When to use it

When planning a migration from self-managed Kafka to Amazon MSK Express.
When you need to assess the compatibility of your current Kafka setup with MSK Express.
When you require an accurate sizing and cost projection for an MSK Express target.
When you want to validate performance through a proof-of-concept load test before migrating.
When you need a structured approach to inventorying your existing streaming infrastructure.

Advantages

Reduces migration risk through automated compatibility and topology assessment.
Provides data-driven cost and sizing recommendations based on actual workload metrics.
Supports informed decision-making with optional pre-migration performance simulation.
Standardizes the migration process across discovery, assessment, and execution phases.
Integrates with existing IaC workflows for efficient inventory collection.

Usage examples

“I need to migrate my on-premises Kafka cluster to MSK Express. Where do I start?”: The agent will explain the three-phase migration process (Discovery, Assessment, Simulation) and ask for your infrastructure details or IaC files to begin the discovery phase.
“Here is my Terraform directory for our Kafka cluster. Can you assess if it is compatible with MSK Express?”: The agent will perform the discovery phase to generate a cluster configuration and then proceed to the assessment phase to validate compatibility and provide sizing recommendations.
“I have a cluster-config.json file. How do I get a cost estimate for moving to MSK Express?”: The agent will run the assessment phase using your configuration file to produce a compatibility report and a pricing summary for the recommended MSK Express setup.
“How do I test MSK Express performance with my current workload?”: The agent will guide you through the simulation phase, helping you deploy a temporary MSK Express cluster to run load tests and review performance metrics.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/aws/migrate-to-msk/download

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

mkdir -p .agents/skills && unzip migrate-to-msk.zip -d .agents/skills

Documentation by AWS

Migrating to MSK Express

Overview

This skill helps customers migrate self-managed Apache Kafka workloads to Amazon MSK Express. It provides three phases — Discovery, Assessment, and an optional Simulation — that can be run end-to-end or individually depending on the customer's needs.

Scope

This skill covers migrations from self-managed Apache Kafka (on-premises, EC2, Docker, Kubernetes, or other non-MSK deployments) to MSK Express. Migrations from MSK Standard (Provisioned) to MSK Express are out of scope.

Prerequisites

The AWS MCP server is recommended for documentation lookups and informational questions, but is not required. The assessment scripts are pure file processors with no AWS API calls.

Intent Routing

Route the customer's request based on their intent:

1. Open/exploratory question ("How do I migrate to MSK?")

Explain what this skill offers:

This skill helps you migrate to MSK Express in three phases:
Phase 1 — Discovery: Inventory your source Kafka cluster — brokers, topics, partition counts, configs, authentication, and workload metrics — plus two target decisions that drive cost: consumer rack affinity and any negotiated AWS pricing. I can discover this from IaC files (Terraform, CDK, Docker Compose, Kubernetes manifests), provide commands for you to run on your cluster, or you can provide the information manually. Output: migrate-to-msk-skill-artifacts/<cluster_name>/cluster-config.json.
Phase 2 — Assessment: Validate your cluster against MSK Express across 5 compatibility pillars (topology, Kafka version, configs, auth, quotas) and produce a target Express specification using the managing-amazon-msk Skill's pricing logic. I'll flag what Express will refuse vs what Express will silently convert. Outputs: compatibility.<cluster_name>.json, the pricing results in Markdown msk_sizing_pricing.md, and msk-sizing-inputs.<cluster_name>.json.
Phase 3 — Simulation: Spin up an MSK Express cluster with load-testing infrastructure to see how Express performs on your own workload, then run a vended test (End-to-End Latency or Broker Restart Under Load) and review the results on a CloudWatch dashboard.
Data replication: For migrating data to your Express cluster, you can use MSK Replicator. I can provide guidance on setup and configuration.
Where would you like to start? I can begin with discovery if you point me to your infrastructure code or describe your cluster, or jump to assessment if you already have a cluster-config.json file, or go straight to simulation if you already know your target Express configuration.

Guardrails for this overview response:

This response is an overview and a routing question only. Do NOT begin, simulate, or pre-empt any phase.
Do NOT produce or estimate assessment output here — no verdicts, pillar findings, compatibility conclusions, broker counts, instance recommendations, or cost figures. Those values exist only after you run the Phase 2 scripts against a real cluster-config.json.
Do NOT open, read, or summarize the internals of compatibility.py, simulation_load_test_config.py, or the reference files to explain how a phase works. Describe the phases at the level shown above; do not walk the customer through the implementation.
When the customer chooses a phase, run that phase's scripts or flow to produce real results. Always operate the skill to answer — never answer from having read its source. For the exact commands, see "Running the assessment" in references/assessment-compatibility.md for Phase 2, and references/simulation.md for Phase 3.

2. Discovery intent (DEFAULT when IaC files are provided)

If the customer provides a directory path, IaC files, or says "here's our infra" — this is discovery intent. Run ONLY Phase 1 (Discovery). Do NOT run assessment, do NOT suggest migration steps, do NOT mention blockers or compatibility. Produce the migrate-to-msk-skill-artifacts/<cluster_name>/cluster-config.json file and stop.

3. Assessment intent

Customer explicitly asks to assess or has a migrate-to-msk-skill-artifacts/<cluster_name>/cluster-config.json file already produced. Run Phase 2 (Assessment) only.

4. Simulation intent

Customer wants to test MSK Express with their workload. They can provide cluster sizing directly (instance type, broker count, Kafka version) or reference an earlier assessment. Proceed directly to Phase 3 — Simulation. An assessment is helpful but not required — the simulation asks for sizing inputs directly.

5. Informational questions

Customer asks about Express capabilities, constraints, configuration differences, authentication support, pricing, or compaction behavior without providing cluster-specific data. Use AWS documentation tools (aws___search_documentation, aws___read_documentation) if available to look up the answer from MSK Express documentation. If MCP tools are not available, reference the MSK Express documentation and answer based on knowledge of AWS MSK.

6. Migration strategy questions

Customer asks about MSK Replicator compatibility, version upgrade paths, MirrorMaker 2, or migration strategies. MSK Replicator is the native AWS-supported solution for data replication and works for both MSK-to-MSK and non-MSK-to-MSK migrations. Use AWS documentation tools (aws___search_documentation, aws___read_documentation) if available to retrieve current requirements and supported configurations. If MCP tools are not available, reference the MSK Replicator documentation and answer based on knowledge of AWS MSK.

Phase 1 — Discovery

Purpose: Inventory the source cluster to build a migration profile.

Input: One of:

A directory path containing IaC files (CDK, CloudFormation, Docker Compose, Kubernetes manifests, Terraform)
Output from Kafka CLI commands the customer runs on their cluster
Manual information provided by the customer in conversation

Output: migrate-to-msk-skill-artifacts/<cluster_name>/cluster-config.json — saved to the working directory.

Discovery rules

Before doing ANYTHING else in discovery, you MUST read references/discovery.md in full. It defines the input methods, the REQUIRED response template, the forbidden content, and the cluster-config.json schema. Do NOT respond until you have read it, and follow its template EXACTLY.
ALWAYS save migrate-to-msk-skill-artifacts/<cluster_name>/cluster-config.json in the working directory.
Do NOT proceed to Phase 2 without explicit customer confirmation.

Phase 2 — Assessment

Purpose: Assess the cluster against MSK Express requirements and produce a target Express specification (instance type, broker count, monthly cost projection).

Input: migrate-to-msk-skill-artifacts/<cluster_name>/cluster-config.json from Phase 1.

Outputs:

migrate-to-msk-skill-artifacts/<cluster_name>/compatibility.<cluster_name>.json — five-pillar verdict.
migrate-to-msk-skill-artifacts/<cluster_name>/msk_sizing_pricing.md — the managing-amazon-msk Skill's pricing report with broker count and cost recommendations.
migrate-to-msk-skill-artifacts/<cluster_name>/msk-sizing-inputs.<cluster_name>.json — a record of the six input values for sizing logic.

Assessment has two independent halves; run them in either order, and a failure in one does not block the other:

Compatibility — scripts/compatibility.py, a pure file processor (no live AWS API calls) run via uv run with PEP 723 inline dependencies. It validates the source across five pillars — topology, Kafka version, configs, auth, and quotas — and emits one verdict per pillar (INFO, ADVISORY, or ACTION_REQUIRED, worst-of for the overall). Use those three strings verbatim.
Sizing — not a script in this skill. Load the managing-amazon-msk Skill and run its scripts/msk_sizing.py with the workload inputs derived from cluster-config.json, passing --broker-classes express and --region for the target Region. The script prices from the live AWS Pricing API when credentials are available and from an embedded us-east-1 snapshot when they are not.

Assessment rules

Read references/assessment-compatibility.md before responding. It carries the invocation commands, the per-pillar thresholds and evidence codes, the verdict definitions, the full forbidden-behavior list, and the required response template covering both artifacts. Do not freestyle the post-script summary.
Read references/assessment-sizing.md before running sizing. It carries the input derivations (several are not one-to-one — getting them wrong silently produces a wrong broker count), the target-block flags for rack affinity and negotiated discounts, the Region and pricing-source handling, the Express-only presentation rule, and the source-footprint comparison.
Surface any ACTION_REQUIRED evidence to the user for awareness, but do not gate further phases on it. Express may still accept the workload with mitigations.
Do NOT pivot back into discovery. Assessment operates on the existing cluster-config.json as-is. Partial data is fine — the scripts emit ADVISORY evidence (METRICS_MISSING, AZ_COUNT_UNKNOWN, etc.) for missing fields; surface those findings and stop. Do not propose Kafka CLI commands, IaC walks, scripts, or questionnaires to fill the gaps.
Report broker counts and costs only as read verbatim from the sizing script output. Never round, re-derive, or estimate them. Report the pricing source the script used alongside the costs; when it is static, the rates are an embedded snapshot, not confirmed current pricing.

Phase 3 — Simulation (optional)

Deploy a temporary, isolated MSK Express cluster and client fleet in the customer's account so they can see how Express performs on their own workload, then run one of two vended tests (End-to-End Latency, Broker Restart Under Load) and hand over a CloudWatch dashboard. Follow the 12-step conversational flow and all deploy, sizing, and guardrail details in references/simulation.md; the deterministic artifacts it drives are scripts/simulation_load_test_config.py and the static assets/simulation-stack.yaml.

Execution model

Scripts run on the customer's local machine via uv run. They declare their own dependencies (PEP 723) and are pure file processors — no AWS API calls, no network access, and no third-party dependencies (standard library only).

Security Considerations

Apply these controls at every phase. For additional detail, see MSK Security best practices and MSK IAM access control.

Encryption in transit (mandatory). Enforce TLS for client-broker traffic on the MSK Express target (EncryptionInTransit.ClientBroker = TLS).
Encryption at rest (mandatory). Provision the target cluster with a customer-managed KMS key (or AWS-managed if your compliance posture allows).
Authentication — prefer IAM over long-lived credentials. Configure the MSK Express target with IAM authentication as the sole client auth method. This gives ephemeral, role-based credentials with full CloudTrail coverage.
Credential storage — use AWS Secrets Manager. Store SASL/SCRAM and TLS credentials for source cluster access in Secrets Manager. Never pass passwords as CLI arguments.
Network isolation. Deploy MSK clusters in private subnets. Use security groups scoped to specific CIDR ranges or security group references. Do NOT use 0.0.0.0/0 ingress rules.
CloudTrail logging and CloudWatch alarms. Ensure CloudTrail is enabled in the target account and covers kafka.amazonaws.com API calls. Configure alarms:
- ClientAuthenticationFailure — surge indicates credential problems or attack
- ConnectionCloseCount — abnormal spike may indicate connection-flooding
- CloudTrail metric filters for denied kafka-cluster:* actions
- Connection-rate alarms approaching the 100 conn/sec/broker IAM limit
Sensitive data handling. Discovery and assessment outputs contain broker addresses, auth hints, and broker config values. Treat these as sensitive — do not paste into public channels or ticketing systems without redaction.

Troubleshooting

Per-pillar findings, including the source topology and out-of-range config cases, are explained in references/assessment-compatibility.md.

Files

ATTRIBUTION.mdAdded by AutoGineer908 B
LICENSELicence file added by AutoGineer9.9 KiB
NOTICELicence file added by AutoGineer67 B
SKILL.md14.0 KiB
assets/simulation-stack.yaml47.9 KiB
references/assessment-compatibility.md23.1 KiB
references/assessment-sizing.md14.8 KiB
references/discovery.md14.8 KiB
references/simulation.md38.6 KiB
scripts/compatibility.pyScript46.7 KiB
scripts/simulation_load_test_config.pyScript17.7 KiB

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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/migrate-to-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

2026-11-08

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