The body avoids explaining concepts Claude already knows, but the Overview rephrases the description and the inline Error Handling table duplicates errors.md, so it could be tightened rather than earning anchor 3.

It offers concrete pip commands and specific parameter ranges (contamination 0.01-0.05, label conventions), but contains no executable code blocks for the actual detection pipeline, leaving key implementation detail missing per anchor 2.

A clear 10-step numbered sequence is present, but validation/verification checkpoints are not embedded in the workflow and this is a batch operation, which the rubric caps at 2 when checkpoints are absent.

References to implementation.md and errors.md are well-signaled and one level deep (both verified as real files), but the inline error table and inline Examples duplicate reference content, and examples.md, the scripts, and asset templates are never linked from the body, fitting anchor 2.

It names the anomaly/outlier domain and the action of identifying them, but only lists one general action with broken 'Process identify' grammar rather than multiple specific concrete actions, so it falls short of anchor 3.

It explicitly answers both what (identify anomalies and outliers in datasets) and when via a 'Use when analyzing data for unusual patterns...' clause plus an explicit trigger-phrases section, satisfying anchor 3.

It gives natural phrases a user would actually say — 'detect anomalies', 'find outliers', 'identify unusual patterns' — providing good coverage of common variations, matching anchor 3.

Anomaly/outlier detection is a clear niche with distinct, specific triggers unlikely to fire for unrelated skills, matching anchor 3; the broad 'analyzing data' phrasing keeps it from being unmistakable but the outlier-specific triggers dominate.