Conciseness

The body is a lean router with no explanation of concepts Claude already knows — intro sentence, routing table, four rules. However, several routing cells are wordy: the planning cell runs ~90 words, and the model-selection cell states the use-case-specification recommendation twice ('**Recommended:** route to use-case-specification first... Routing to use-case-specification first is not required if user provides a specific model name/ID or declines'). This fits anchor 4, 'Efficient; minor instances of over-explanation that could be trimmed', rather than 5, since those cells could be tightened without losing routing content.

Actionability

Every routing row maps a concrete user utterance to a specific verified path (all 14 referenced folders exist on disk), the model-selection row pins a specific file ('Base model filtering for deployment MUST go through select-for-deployment.md and its scripts'), and the rules include a copy-paste command ('export AWS_SDK_UA_APP_ID=AWSSkill-SageMaker'). For an instruction-only routing skill this is fully actionable; matches anchor 5 under the code_vs_instruction_skills note.

Workflow Clarity

Sequencing is explicit: 'Load this reference FIRST when the request matches multiple rows', 'Requires a base model to be selected first', and ambiguity gates such as 'model-selection MUST confirm which path before proceeding' and 'ask one service-choice question. Do not select a service by default.' This is a single-purpose router under 50 lines whose single action is unambiguous with explicit decision checkpoints, meeting the simple-skill exception for a 5; no destructive/batch cap applies.

Progressive Disclosure

Scored against the actual bundle: SKILL.md is a clean overview whose only content is intent-to-folder routing, all 14 linked paths are real, and each of the 17 topic folders leads with a substantive overview.md (verified 98–171 lines of real routing rules and prerequisites, not pointer-chains) that in turn signals its own references/, scripts/, and code_templates/. Content is appropriately split across 117 files with nothing inlined that belongs in a reference, matching anchor 5, 'Clear overview with well-signaled... references; content appropriately split; easy navigation'.

Specificity

Third-person verbs ('Selects, deploys, and customizes AI models on Amazon SageMaker') introduce an enumeration of 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' — comprehensive coverage of the domain with named techniques. This matches the anchor 'Lists multiple specific concrete actions; comprehensive coverage' rather than 4, since no capability area of the lifecycle is visibly missing.

Completeness

Both questions are answered explicitly: 'what' via the opening capability enumeration and 'when' via the concrete 'Use when fine-tuning models on SageMaker, choosing which base model to customize...' trigger list plus a second 'Use for endpoint health, failures...' clause. This matches the anchor 'Clearly and explicitly answers both what AND when with concrete trigger phrases'; not 4, because the when-clause is fully explicit rather than merely present.

Trigger Term Quality

Natural user phrasings are comprehensively covered with synonyms: 'fine-tuning models on SageMaker', 'choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog', 'transforming or validating training data', 'evaluating model quality', 'deploying or optimizing endpoints', 'configuring IAM/S3 for training', and 'endpoint health, failures, latency, logs, metrics, errors'. Matches the anchor 'Comprehensive coverage of natural terms including synonyms'; no common variant (train, deploy, benchmark, diagnose) is missing.

Distinctiveness Conflict Risk

The niche (SageMaker model customization) is distinct and the explicit negative boundary 'NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure' plus the service boundary (SageMaker vs Bedrock) minimizes wrong-skill triggering. Matches the anchor 'Clear niche with distinct triggers; minimal conflict risk'.