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migrate-to-msk

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

72

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a well-structured, actionable routing and overview document for a complex three-phase migration skill, with concrete artifact paths, script names, and verdict strings, and clean one-level-deep references to real bundle files. It is slightly long in its canned overview and repeated guardrails, and defers exact commands and deploy-time validation feedback loops to the reference files.

Suggestions

Tighten the canned overview blockquote and consolidate the repeated 'Do NOT' guardrail lists (Intent Routing section 1 vs. per-phase rules) to reduce redundancy and save tokens.

Inline the exact `uv run scripts/compatibility.py` invocation command rather than only deferring to references/assessment-compatibility.md, so the body is copy-paste ready for the most common phase.

Surface at least one explicit validate→fix→retry checkpoint for the Phase 3 simulation deploy in the body (e.g., confirm stack CREATE_COMPLETE before launching the load test) rather than delegating all deploy validation to simulation.md.

DimensionReasoningScore

Conciseness

Largely efficient and assumes Claude's intelligence (no explaining of what Kafka/MSK is), but the canned overview blockquote and repeated 'Do NOT' guardrail lists across intent-routing and per-phase rules add length that could be trimmed. Not a 5 because of these minor padded stretches; not a 3 because the body largely avoids unnecessary concept explanation.

4 / 5

Actionability

Gives concrete guidance — exact artifact paths ('cluster-config.json', 'compatibility.<cluster_name>.json'), exact script names ('scripts/compatibility.py'), CLI flags ('--broker-classes express'), and a verbatim verdict taxonomy ('INFO', 'ADVISORY', 'ACTION_REQUIRED'). Not a 5 because the exact copy-paste invocation commands are explicitly deferred to the reference files rather than inline.

4 / 5

Workflow Clarity

The three phases are clearly sequenced with Purpose/Input/Output, intent routing maps requests to phases, and explicit gating checkpoints exist ('Do NOT proceed to Phase 2 without explicit customer confirmation', surface-and-stop on missing data). Not a 5 because explicit validate→fix→retry feedback loops for the resource-creating simulation deploy are delegated to the reference rather than surfaced in the body.

4 / 5

Progressive Disclosure

The body is a clear overview that points to well-signaled one-level-deep references (discovery.md, assessment-compatibility.md, assessment-sizing.md, simulation.md) plus scripts and assets, all of which are real files; detailed thresholds, templates, and the 12-step flow are appropriately split out. Not a 4 because content is appropriately separated and navigation is easy with no nested-reference chains.

5 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is high quality: it states comprehensive concrete capabilities, an explicit and exhaustive trigger list, and a clear disambiguating niche against the sibling managing-amazon-msk skill. It answers both 'what' and 'when' concretely without vague fluff.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Inventories the source cluster', 'assesses MSK Express compatibility across topology, Kafka version, configs, auth, and quotas', 'produces a target Express specification (instance type, broker count, monthly cost)', 'stands up a trial Express cluster to load-test', 'guides migration execution using MSK Replicator' — giving comprehensive coverage. Not a 4 because the action set is comprehensive rather than having minor gaps.

5 / 5

Completeness

Explicitly answers both 'what' (inventories, assesses, produces a spec, stands up a trial cluster, guides execution) and 'when' ('Applicable when the user mentions...') with concrete trigger phrases. Not a 4 because the 'when' is fully explicit with concrete triggers rather than only weakly specified.

5 / 5

Trigger Term Quality

The 'Applicable when the user mentions...' clause enumerates many natural synonyms — 'migrating Kafka', 'moving to MSK', 'streaming migration', 'moving streaming workloads to AWS', 'running a POC or load-test', 'MSK Replicator' — that a user would naturally say. Not a 4 because coverage includes synonyms and variations rather than missing common terms.

5 / 5

Distinctiveness Conflict Risk

Clear niche (self-managed Kafka → MSK Express migration) with an explicit disambiguation sentence — 'Prefer this skill to the managing-amazon-msk skill for migration questions' — minimizing conflict with the sibling skill. Not a 4 because the explicit prefer-over guidance removes the residual overlap risk that a 4 would allow.

5 / 5

Total

20

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

14

/

16

Passed

Repository
aws/agent-toolkit-for-aws
Reviewed

Table of Contents

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