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managing-amazon-msk

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.

Quality

91%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is managing-amazon-msk in aws/agent-toolkit-for-aws

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 progressive-disclosure hub: it validates the broker type first, routes every customer intent to a real one-level-deep reference file, and handles the MCP-vs-local file-resolution ambiguity explicitly. Its weaknesses are modest — some inline Express statistics and dense table cells duplicate what the sizing reference already covers, and a small typo in the routing table ('Serqverless') suggests a light editorial pass.

Suggestions

Trim the Express brokers paragraph to the one-line recommendation plus critical constraints (no customer-managed EBS, fixed RF 3 / min.insync.replicas 2) and let references/size-and-choose-cluster.md carry the comparative statistics ('3x ingress', '20x faster scaling', '180x faster rebalancing', '90% quicker recovery') that are currently duplicated there.

Add one short copy-paste-ready example beyond the single describe-cluster-v2 command — e.g., a minimal 'run scripts/msk_sizing.py' invocation line with its key arguments — so the most common sizing path is executable from the body without opening the reference.

Tighten the longest intent-table cells (e.g., the configure-cluster and Streaming Tables eligibility rows) to intent keywords plus the reference link, move their qualifying detail into the linked reference files, and fix the 'Serqverless' typo.

DimensionReasoningScore

Conciseness

The 96-line body is efficient — a routing table instead of prose, no explanation of concepts Claude already knows, and a compact scripts/guardrail section. Minor trims remain: the Express paragraph inlines performance statistics ('3x ingress', '20x faster', '180x faster', '90% quicker') partially duplicated in references/size-and-choose-cluster.md, and several intent-table cells are dense enough to compress.

4 / 5

Actionability

Concrete, executable guidance is present: the exact entry command ('aws kafka describe-cluster-v2 --cluster-arn <arn>' with the field to check, 'Provisioned.BrokerNodeGroupInfo.InstanceType'), the sizing script with its credentials/snapshot fallback behavior, and literal retrieve_skill file parameters. It falls short of fully copy-paste-ready coverage only because most executable detail beyond the entry command is delegated to the reference files.

4 / 5

Workflow Clarity

The body sequences its decisions clearly and checkpoints the risky ones: broker type must be determined first via a concrete command before routing, and the MCP-vs-local load path must be resolved before any file is read. It is not a 5 because the per-workflow validation and feedback loops live in the reference files rather than the body, leaving minor validation gaps at the overview level.

4 / 5

Progressive Disclosure

A model overview-hub structure: a 'Which Workflow Do You Need?' intent-to-reference routing table, one-level-deep references (all 14 referenced files verified to exist, with no references nested further), a documented script, and a guardrail for resolving the skill's own file locations. Navigation is easy and content is appropriately split.

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.

An exceptionally well-engineered description: it states what the skill does, when to use it with concrete trigger phrases, and when not to use it with explicit exclusions and cross-skill routing. The only caution is length — it is several times longer than typical good examples — but every clause is functional (a capability, a trigger, or a boundary) rather than padded, so no verbosity penalty applies to any dimension.

DimensionReasoningScore

Specificity

Enumerates many concrete capabilities across the full MSK Provisioned domain — '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 ... Streaming Tables ... setup, IAM, monitoring'. Coverage is comprehensive rather than having minor gaps, matching the top anchor; the actions are specific and concrete (setup, tuning, patching, monitoring), not generic.

5 / 5

Completeness

Both halves are explicit: what ('Operates Amazon MSK Provisioned clusters (Standard and Express brokers)... Covers...') and when ('Required for ANY MSK Provisioned task' plus a dedicated 'Triggers:' clause with concrete trigger phrases). The 'Use when...' cap of 3 does not apply because explicit trigger guidance is present.

5 / 5

Trigger Term Quality

The explicit 'Triggers:' list covers natural user phrasing comprehensively: 'MSK Provisioned, Kafka, cross-cluster replication, Kafka to Iceberg on S3 Tables, Kafka to S3, lakehouse, data lake from Kafka, Kafka Connect S3 Sink or Firehose alternative', including synonyms (lakehouse / data lake). No commonly-said phrase for this domain is missing, so it exceeds the 'good keyword coverage with a few missing' level.

5 / 5

Distinctiveness Conflict Risk

Clear niche with active disambiguation rather than just minor overlap risk: 'DO NOT USE for MSK Connect — search docs instead. Serverless: only S3 Tables/data delivery eligibility. Prefer Flink for Kafka Iceberg sink questions. Migrations: use migrate-to-msk.' These negative triggers and cross-skill routing rules make wrong-skill triggering unlikely.

5 / 5

Total

20

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

aws/agent-toolkit-for-aws

Path

plugins/aws-data-analytics/skills/managing-amazon-msk/SKILL.md

Reviewed

Table of Contents

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