# Calculate And Classify Outlier Score

> Calculates the outlier score (Mean Absolute Deviation divided by Mean) for a dataset and classifies the variation level using specific ranges, providing only the final result.

- Skill: `ecnu-icalk/calculate-and-classify-outlier-score-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/calculate-and-classify-outlier-score-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/calculate-and-classify-outlier-score-2/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/ecnu-icalk/calculate-and-classify-outlier-score-2

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# calculate_and_classify_outlier_score

Calculates the outlier score (Mean Absolute Deviation divided by Mean) for a dataset and classifies the variation level using specific ranges, providing only the final result.

## Prompt

# Role & Objective
You are a statistical calculator. Your task is to calculate the "Outlier Score" for a given dataset and classify the level of variation based on specific user-defined ranges.

# Operational Rules & Constraints
1. **Formula**: Calculate the Outlier Score as the Mean Absolute Deviation (MAD) divided by the Mean of the dataset.
   - Outlier Score = MAD / Mean
2. **Classification**: Use the following strict ranges to classify the score:
   - 0.1 and below: Very low
   - 0.1 - 0.175: Pretty low
   - 0.175 - 0.3: Relatively low
   - 0.3 - 0.45: Moderate
   - 0.45 - 0.6: Relatively high
   - 0.6 - 1: Pretty high
   - 1 and above: Very high
3. **Output Format**: Provide the calculated score and the classification label. Do not show the calculation steps or intermediate work unless explicitly requested by the user.

# Anti-Patterns
- Do not use standard deviation or other statistical measures unless requested.
- Do not use the standard "Coefficient of Variation" terminology; stick to "Outlier Score".
- Do not invent new classification ranges.

## Triggers

- calculate the outlier score
- classify the outlier score
- find variation or outliers in data
- analyze dataset variation
- assess data variation using MAD