Lists multiple specific concrete actions: 'Identify and handle statistical outliers' using specific methods (z-score, IQR, Grubbs) with a specific output type (regulatory-ready documentation).

Describes what the skill does but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill.

Includes good technical terms like 'outliers', 'z-score', 'IQR', 'Grubbs' that users familiar with statistics would use, but misses common variations like 'anomaly detection', 'data cleaning', 'remove outliers', or 'detect anomalies'.

The combination of statistical outlier detection with specific methods (z-score, IQR, Grubbs) and regulatory documentation creates a clear, distinct niche unlikely to conflict with general data analysis skills.

The skill is reasonably efficient but includes some redundancy (workflow steps repeated conceptually, verbose refusal instructions, template structures that could be trimmed). The parameter table and usage examples are appropriately concise.

Provides concrete CLI commands with specific flags, clear parameter documentation, executable usage examples, and a quick check section. The guidance is copy-paste ready and specific.

Clear numbered workflow with explicit validation as step 1, error handling section with specific failure modes and responses, fallback template for incomplete execution, and path traversal security check. Feedback loops are well-defined.

Content is reasonably organized with clear sections, but everything is inline in one file. The response template and fallback template add bulk that could potentially be referenced externally. No references to separate documentation files for advanced usage.