Conciseness

The body is code-dense and lean in Core Patterns, and time-sensitive information (retirement date, beta version) is properly quarantined in a deprecation-style 'Service status' section. Minor over-explanation remains — a 'Trigger Phrases' section redundant with the description, plus generic 'When to Use'/'Limitations' boilerplate ("This skill is applicable to execute the workflow or actions described in the overview") — fitting anchor 4 rather than anchor 3 since the padding is small relative to the whole.

Actionability

Concrete Java snippets cover all major operations (clients, batch/last detection, change points, training, inference, model management, error handling), but they are not copy-paste ready: the body itself acknowledges "The snippets omit surrounding application/imports and are not a tested complete program", later snippets reuse undefined variables (options, modelId), and data sources are SAS-token placeholders. This matches anchor 4 rather than the fully executable anchor 5.

Workflow Clarity

The multivariate Train → Inference → Results sequence is stated and the sections follow that order, but validation is only implicit: batch inference shows a single getBatchDetectionResult call with no status check or poll-with-deadline loop before results are accessed (the prose mentions polling but no code demonstrates it), and the destructive deleteMultivariateModel has no verification. Per the batch/destructive cap, workflow clarity cannot exceed 3.

Progressive Disclosure

There are no bundle files (no references/, scripts/, or assets/), and the full ~260-line API pattern catalog is inlined in SKILL.md where anchor guidance says content that clearly belongs in a separate file should not be inlined. Section headers give reasonable structure, matching anchor 3 (some structure, content that should be separate is inline) rather than anchor 4's appropriately split layout.

Specificity

The description names the SDK, language, and concrete capability areas ("univariate/multivariate anomaly detection", "time-series analysis", "AI-powered monitoring"), but the lead action "Build anomaly detection applications" is generic and concrete operations (train models, detect, change-point) are not enumerated, matching anchor 4 rather than the comprehensive coverage of anchor 5.

Completeness

Both questions are explicitly answered: the first sentence states what the skill does ("Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java") and the "Use when implementing..." clause gives concrete trigger phrases (univariate/multivariate anomaly detection, time-series analysis, AI-powered monitoring), matching the anchor 5 example structure exactly; it is not 4 because the 'when' is specific rather than vague.

Trigger Term Quality

Good natural keyword coverage ("anomaly detection", "time-series analysis", "Java", "Azure AI Anomaly Detector") that users would plausibly say, but common synonyms like "detect outliers" or "metric monitoring" are missing and "AI-powered monitoring" is slightly buzzwordy, fitting anchor 4 rather than the comprehensive synonym coverage of anchor 5.

Distinctiveness Conflict Risk

The Azure Anomaly Detector Java SDK is a clear niche with distinct triggers, but "time-series analysis" and "AI-powered monitoring" are broad phrases that could overlap with general time-series, forecasting, or monitoring skills, matching anchor 4 (minor overlap risk) rather than anchor 5 (minimal conflict risk).