domains / families / data / outlier-detection-handler

outlier-detection-handler

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Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

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.

When not to use

• Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
• Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
• Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

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