Conciseness

The body is a dense routing table plus four terse rules with no explanations of concepts Claude already knows; however, several routing cells repeat the same condition across multiple sentences (e.g., the planning row's long parenthetical enumeration and the model-selection row's repeated ordering prose) that could be trimmed. Not 5: minor padding exists in the longest cells; not 3: the overwhelming majority of tokens carry routing-relevant signal.

Actionability

Every intent maps to a concrete folder path (all verified present in the bundle, e.g. references/planning/, references/model-selection/references/select-for-deployment.md) and the rules include a copy-paste command ('export AWS_SDK_UA_APP_ID=AWSSkill-SageMaker'). Not 5: some routing rules are conditional heuristics ('Always activate model-selection even for known model names') rather than directly executable steps; not 3: the guidance is overwhelmingly concrete and executable for a router skill.

Workflow Clarity

Sequencing is explicit — 'Load this reference FIRST when the request matches multiple rows', 'Always co-activate with other intents', and the recommended use-case-specification → model-selection ordering — giving a clear multi-intent workflow. Not 5: the body itself contains no validation checkpoints or error-recovery loops (these are delegated to the reference folders); not 3: the sequence and ordering rules are clearly and unambiguously defined.

Progressive Disclosure

Textbook progressive disclosure: one routing row per intent pointing to a one-level-deep reference folder, each folder carries its own overview.md for further navigation, and the rule 'Load only the reference folder relevant to the current user intent. Do not load all references at once.' makes the disclosure policy explicit. The bundle listing confirms all 15 referenced folders exist and are well organized.

Specificity

The description lists many concrete actions — 'Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics' — giving comprehensive coverage of the lifecycle rather than a vague domain claim.

Completeness

It explicitly answers both 'what' (the opening capability sentences) and 'when' via concrete trigger clauses: 'Use when fine-tuning models on SageMaker, choosing which base model to customize... transforming or validating training data, evaluating model quality, deploying or optimizing endpoints...' and 'Use for endpoint health, failures, latency, logs, metrics, errors.'

Trigger Term Quality

Natural user phrases are well covered ('fine-tuning models on SageMaker', 'deploying or optimizing endpoints', 'endpoint health, failures, latency, logs, metrics, errors', 'transforming or validating training data'), but product-internal jargon like 'SageMakerPublicHub', 'Serverless Model Customization', and 'PySDK v3' is not language users would naturally say, leaving a few natural synonyms missing.

Distinctiveness Conflict Risk

The niche is clear (SageMaker/Bedrock model customization and deployment) and it ends with an explicit negative boundary — 'NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure' — minimizing conflict with adjacent AWS skills.