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://autogineer.com/agent-skills/aws/migrate-to-msk/downloadUnzip 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 Markdownmsk_sizing_pricing.md, andmsk-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.jsonfile, 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.jsonschema. Do NOT respond until you have read it, and follow its template EXACTLY.ALWAYS save
migrate-to-msk-skill-artifacts/<cluster_name>/cluster-config.jsonin 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 viauv runwith 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, orACTION_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.pywith the workload inputs derived fromcluster-config.json, passing--broker-classes expressand--regionfor the target Region. The script prices from the live AWS Pricing API when credentials are available and from an embeddedus-east-1snapshot 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_REQUIREDevidence 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.jsonas-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 sourcethe script used alongside the costs; when it isstatic, 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.comAPI calls. Configure alarms:ClientAuthenticationFailure— surge indicates credential problems or attackConnectionCloseCount— abnormal spike may indicate connection-floodingCloudTrail metric filters for denied
kafka-cluster:*actionsConnection-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
references/discovery.md
Discovery Phase
Process ONE cluster at a time. If multiple clusters are found in the IaC files, list them and ask the user which one to process first. Do NOT process all clusters at once.
Your response MUST follow the template exactly.
Input methods
Use whichever of these the customer's input supports; they combine.
IaC analysis — Read infrastructure files and extract cluster metadata.
Kafka CLI commands — Display standard Kafka CLI commands for the customer to run on their cluster (
kafka-topics.sh,kafka-configs.sh,kafka-broker-api-versions.sh). Do NOT generate or offer Python scripts.Runtime metrics intake — Ingest metrics provided by the customer.
Manual conversation — Ask the customer for cluster details.
Target inputs — Ask the customer whether consumers will use rack-aware (local-AZ) fetching on the target and whether they have a PPA or enterprise discount to apply. Record both in the
targetblock; recordnullfor "unknown". Never guess a discount percentage.
FORBIDDEN content — do NOT include any of the following:
Compatibility observations ("not supported by MSK", "should migrate smoothly")
Blockers, warnings, or recommendations
Migration steps or deployment commands
Sections called "Key Observations", "Important Notes", "Next Steps", or similar
Any assessment of whether configs will work on Express
Python scripts
Response Template
## Discovery Complete — <cluster_name>
### Kafka
- **Version:** <version>
- **Coordination Mechanism:** <KRaft | ZooKeeper | Unknown>
### Topology
- **Brokers:** <count>
- **Availability Zones:** <count or "unknown">
- **Instance Type:** <type or "not determined">
### Security
- **Authentication:** <method>
- **Encryption in transit:** <TLS | PLAINTEXT | TLS_PLAINTEXT | UNKNOWN>
### Topics Found
| Topic | Partitions | Replication | Non-default configs |
|-------|-----------|-------------|---------------------|
| <name> | <count> | <factor> | <configs or "none"> |
### Non-default Broker Configs
- `<config.key>`: `<value>`
### Target Inputs
- **Consumer rack affinity:** <local-AZ (rack-aware) fetching | leaders in any AZ | unknown>
- **Negotiated pricing discount:** <N% | none | unknown>
---
### Information I Could Not Determine
The following require runtime data that isn't available in IaC:
- Topics, partitions, replication factors, and topic-level configs
- Broker-level configuration overrides
- Peak throughput (MB/s ingress/egress per broker)
- Connection count per broker
**Options to fill these gaps:**
- **A.** Run these commands on your cluster and share the output files:
```bash
kafka-topics.sh --bootstrap-server <addr> --describe > kafka-topics-output.txt
kafka-configs.sh --bootstrap-server <addr> --entity-type brokers --entity-default --describe > kafka-configs-output.txt
kafka-broker-api-versions.sh --bootstrap-server <addr> > kafka-versions-output.txtThen share the files or paste their contents here.
B. Proceed with partial data (assessment will be less accurate)
Target Inputs I Need From You
These two are choices about the target cluster, not properties of your source, and they drive the cost estimate:
Consumer rack affinity — will your consumers fetch from local-AZ replicas (rack-aware fetching, with
client.rackset on each consumer andreplica.selector.class=RackAwareReplicaSelectoron the cluster), or will they fetch from partition leaders in any AZ?Negotiated pricing — does your organization have a Private Pricing Agreement (PPA), Enterprise Discount Program (EDP), or other negotiated AWS pricing? If so, what discount percentage should I apply? Without one I will use public on-demand pricing.
Answer "unknown" to either and I will record it as unknown.
Would you like to proceed to assessment, or provide additional information first?
## Rules
- Fill in values from IaC files/commands. Remove sections where no data was found.
- Save to `migrate-to-msk-skill-artifacts/<cluster_name>/cluster-config.json` for the selected cluster.
- Do NOT add extra sections beyond what the template shows.
- Do NOT summarize, characterize, or editorialize the findings ("solid foundation",
"straightforward migration", etc.). Only state facts.
- Do NOT mention Express, MSK, compatibility, blockers, or migration steps. The two
Target Inputs questions are the sole exception: they may refer to the target
cluster, since they are decisions about it. Ask them as written — no compatibility
commentary, no cost figures, no recommendation about which option to pick.
- ALWAYS ask the two Target Inputs questions unless the user already supplied both
answers. Record what they said in the `target` block; record `null` for anything
they answered "unknown" or did not answer. Never guess a discount percentage and
never infer rack affinity from the source cluster's AZ layout.
- Skip the "Target Inputs I Need From You" section only when both answers are
already in hand; the `### Target Inputs` section of the report is always present.
- Do NOT offer or generate Python scripts. Only show Kafka CLI commands.
- Do NOT reference CLI commands unless you actually displayed them in the response.
- If the user provided all information manually and there are no gaps, skip the
"Information I Could Not Determine" section entirely. Keep the Target Inputs
questions if either answer is still missing, then end with:
"Would you like to proceed to assessment, or provide additional information first?"
---
## JSON Output Schema
Save to the following path:
`migrate-to-msk-skill-artifacts/<cluster_name>/cluster-config.json`
Where `<cluster_name>` is the cluster name (lowercase, hyphenated).
Create the directories if they don't exist.
ALL fields MUST be present in the output file unless marked as `(optional)`.
Fields marked `(optional)` MAY be omitted entirely. For all other fields, if the
value cannot be determined, use `null` for strings/numbers, `false` for booleans,
`[]` for arrays, and `{}` for objects.
The output file contains sensitive data (broker addresses, authentication details).
Treat it accordingly — do not commit to version control or share in public channels.
Do NOT store passwords, private keys, or secret values in this file. For
`auth_identity`, record only the username or a Secrets Manager ARN reference —
never the password or key material.
Note: `broker_configs` and `topics[].configs` carry the **full** Kafka config
dump (every config the source exposed), not just non-default values.
`compatibility.py` filters against per-Kafka-version Apache defaults internally,
so values matching the default for the source's `kafka.version` produce no
evidence — only divergences from default are evaluated against Express's
constraints.
### `security` enum values
`encryption_in_transit` is a closed enum describing how client-broker traffic
is encrypted on the source:
- `TLS` — clients connect on a TLS-only listener (port 9094 or 9096 typically).
- `PLAINTEXT` — clients connect on a plaintext listener (port 9092 typically).
- `TLS_PLAINTEXT` — the cluster exposes both; some clients use TLS, others plaintext.
- `UNKNOWN` — use when the listener configuration cannot be determined from available IaC or CLI output.
To determine the value: check the broker's `listeners` / `advertised.listeners`
config, or look at the `security.protocol` clients use (`SSL` / `SASL_SSL` →
TLS; `PLAINTEXT` / `SASL_PLAINTEXT` → PLAINTEXT; mix → TLS_PLAINTEXT).
`authentication` is a closed enum covering the mechanism the source cluster
expects from Kafka clients:
- `UNAUTHENTICATED` — no `sasl.mechanism` or `ssl.keystore` configured on clients; the broker allows anonymous connections.
- `TLS` — clients present X.509 certificates (`ssl.keystore.location` configured); broker has `ssl.client.auth=required`.
- `SASL_SCRAM` — `sasl.mechanism=SCRAM-SHA-256` or `SCRAM-SHA-512` in client config.
- `SASL_IAM` — `sasl.mechanism=AWS_MSK_IAM`, or clients use the AWS MSK IAM signer with `sasl.mechanism=OAUTHBEARER` on an existing MSK cluster.
- `SASL_OAUTHBEARER` — `sasl.mechanism=OAUTHBEARER` with a **non-AWS** token provider (e.g. Keycloak, Okta, custom OAuth server).
- `OTHER` — any mechanism not covered above (e.g. `GSSAPI`/Kerberos, `PLAIN`, custom callback handlers).
- `UNKNOWN` — use when the mechanism cannot be determined from available IaC or CLI output.
To determine the value: check the client's `sasl.mechanism` property, or the
broker's `sasl.enabled.mechanisms` / `listener.security.protocol.map` config.
If both `OAUTHBEARER` and the AWS signer library are present, use `SASL_IAM`
(it's the AWS IAM path). Use `SASL_OAUTHBEARER` only for custom providers.
For how each value is evaluated against MSK Express, see
[assessment-compatibility.md](./assessment-compatibility.md) (Pillar 4 — Auth).
Discovery MUST emit one of these exact strings. `compatibility.py`'s
`validate_input` rejects unrecognized values.
### `target` block values
Both fields are user-stated decisions about the target cluster, not measurements of
the source. Discovery asks for them; Assessment reads them and passes them to the
sizing script.
`rack_affined_consumers`:
- `true` — consumers will fetch from local-AZ replicas (`client.rack` on each
consumer, `replica.selector.class=RackAwareReplicaSelector` on the cluster).
- `false` — consumers will fetch from partition leaders in any AZ, or no rack
configuration is planned.
- `null` — the user answered "unknown" or was not asked.
`pricing_discount_pct`:
- A number in `[0, 100)` — the discount percentage the user stated (`0` means they
confirmed no negotiated discount).
- `null` — the user answered "unknown" or was not asked.
Record only what the user stated. Do not derive `rack_affined_consumers` from
`topology.num_azs` or the source's rack configuration, and do not supply a discount
percentage the user did not give.
### Partition counts: leaders vs. total replicas
Two different partition numbers come up, and they are not interchangeable. Be
explicit about which one you are recording.
- **Configured (leader) partitions** — the partition count set on a topic, one
leader per partition. This is the `PartitionCount` shown by
`kafka-topics.sh --describe`, and the number set with `--partitions`. It does
**not** include replicas.
- **Total partition replicas** — configured partitions multiplied by the
replication factor (leaders + followers). This is the basis AWS uses for
per-broker partition limits (see `peak_partitions_per_broker` below and the
MSK Express broker partition quota).
How each contract field is counted:
- `topics[].num_partitions` — the **configured (leader)** count for that topic.
Record the per-topic partition count, never a pre-multiplied total.
- `topics[].replication_factor` — the source topic's replication factor.
- `metrics.peak_partitions_per_broker` — **total replicas** (leaders +
followers) hosted on the busiest broker, matching the AWS quota basis.
Assessment converts when it needs the total: it multiplies the summed leader count
by the Express target replication factor (always 3) before passing
`--num-partitions` to the sizing script (see "Deriving the sizing inputs" in
[assessment-sizing.md](./assessment-sizing.md)). So you only ever enter leader
counts in `num_partitions` — do not pre-multiply by RF.
**If a user reports a partition number conversationally and it is ambiguous
which count they mean, ask before recording it:** "Is that the configured
partition count per topic (leaders), or the total including replicas?" If it is
a total-including-replicas figure, divide it back to the configured count (and
capture the replication factor separately) before writing `num_partitions`.
```json
{
"cluster_name": "<string>",
"source_type": "<string: 'self-managed' | 'cloud-hosted'>",
"discovered_at": "<string: ISO 8601 timestamp>",
"kafka": {
"version": "<string>",
"coordination_mechanism": "<string: 'KRaft' | 'ZooKeeper' | 'Unknown'>"
},
"topology": {
"num_brokers": "<integer>",
"num_azs": "<integer> (optional)",
"broker_instance_type": "<string> (optional)"
},
"topics": [
{
"name": "<string>",
"num_partitions": "<integer>",
"replication_factor": "<integer>",
"configs": {
"<config.key>": "<string: full Kafka topic config dump>"
}
}
],
"broker_configs": {
"<config.key>": "<string: full Kafka broker config dump>"
},
"security": {
"encryption_in_transit": "<enum: 'TLS' | 'PLAINTEXT' | 'TLS_PLAINTEXT' | 'UNKNOWN'>",
"authentication": "<enum: 'UNAUTHENTICATED' | 'TLS' | 'SASL_SCRAM' | 'SASL_IAM' | 'SASL_OAUTHBEARER' | 'OTHER' | 'UNKNOWN'>",
"auth_identity": "<string> (optional)"
},
"target": {
"rack_affined_consumers": "<boolean | null>",
"pricing_discount_pct": "<number | null>"
},
"metrics": {
"source": "<string: 'manual' | 'other' | null>",
"lookback_hours": "<integer | null>",
"peak_bytes_in_per_broker_mbps": "<number | null>",
"peak_bytes_out_per_broker_mbps": "<number | null>",
"avg_bytes_in_per_broker_mbps": "<number | null>",
"avg_bytes_out_per_broker_mbps": "<number | null>",
"peak_connections_per_broker": "<integer | null>",
"peak_partitions_per_broker": "<integer | null>"
},
"iac_index": [
{
"iac_type": "<string: 'terraform' | 'cdk' | 'cloudformation' | 'k8s_manifest' | 'docker_compose'>",
"iac_file": "<string: relative path>",
"service_name": "<string>",
"bootstrap_hints": ["<string>"]
}
]
}Example
{
"cluster_name": "orders-staging",
"source_type": "self-managed",
"discovered_at": "2026-05-27T18:00:00Z",
"kafka": {
"version": "3.6.0",
"coordination_mechanism": "KRaft"
},
"topology": {
"num_brokers": 12,
"num_azs": 3,
"broker_instance_type": null
},
"topics": [
{
"name": "orders",
"num_partitions": 24,
"replication_factor": 3,
"configs": {
"cleanup.policy": "delete",
"retention.ms": "604800000"
}
}
],
"broker_configs": {
"log.segment.bytes": "536870912",
"min.insync.replicas": "3"
},
"security": {
"encryption_in_transit": "TLS",
"authentication": "SASL_SCRAM",
"auth_identity": "kafka-admin"
},
"target": {
"rack_affined_consumers": true,
"pricing_discount_pct": 15
},
"metrics": {
"source": "manual",
"lookback_hours": 24,
"peak_bytes_in_per_broker_mbps": 142.7,
"peak_bytes_out_per_broker_mbps": 285.3,
"avg_bytes_in_per_broker_mbps": 78.4,
"avg_bytes_out_per_broker_mbps": 156.8,
"peak_connections_per_broker": 1240,
"peak_partitions_per_broker": 380
},
"iac_index": [
{
"iac_type": "k8s_manifest",
"iac_file": "deploy/orders.yaml",
"service_name": "order-svc",
"bootstrap_hints": ["kafka:9092"]
}
]
}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
Security
What our scanner noticed· Low risk
- Instructions that use the network
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
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
- Also distributed in
- aws/agent-toolkit-for-aws
AutoGineer is not affiliated with or endorsed by AWS or AWS.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download migrate-to-msk.zip