Outlier Detection & Handling

Identify and manage statistical outliers.

When to Use

• Use this skill when the task needs Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.
• Packaged executable path(s): scripts/main.py.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.