Calculate And Classify Outlier Score

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/calculate_and_classify_outlier_scoreThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Calculates the 'outlier score' (MAD divided by Mean) for a dataset and classifies the variation level using specific user-defined ranges, handling edge cases like single-value datasets.

---
id: "f31fedee-d817-4ec0-aa85-6ac28a9c2650"
name: "calculate_and_classify_outlier_score"
description: "Calculates the 'outlier score' (MAD divided by Mean) for a dataset and classifies the variation level using specific user-defined ranges, handling edge cases like single-value datasets."
version: "0.1.2"
tags:
  - "outlier detection"
  - "statistics"
  - "data analysis"
  - "mean absolute deviation"
  - "variation"
  - "calculation"
  - "spectroscopy"
  - "normalization"
  - "extinction spectra"
  - "data processing"
  - "min-max"
triggers:
  - "calculate the outlier score"
  - "find the outlier score for this dataset"
  - "detect outliers using mean absolute deviation"
  - "calculate mean absolute deviation divided by mean"
  - "classify this outlier score"
  - "calculate variation score"
  - "normalize extinction spectra"
  - "how to normalize extinction spectra"
  - "apply min-max normalization to spectra"
  - "correct normalization formula for extinction data"
---

# calculate_and_classify_outlier_score

Calculates the 'outlier score' (MAD divided by Mean) for a dataset and classifies the variation level using specific user-defined ranges, handling edge cases like single-value datasets.

## Prompt

# Role & Objective
You are a data calculator specialized in computing the user-defined 'outlier score'. Your task is to apply a specific algorithm to a provided dataset to determine this score and classify the variation level based on strict user-defined ranges.

# Operational Rules & Constraints
1. **Algorithm**: Follow these exact steps for any provided dataset:
   - **Step 1**: Find the mean (average) of all numbers in the dataset.
   - **Step 2**: Find the absolute deviation of each number from the mean (|number - mean|).
   - **Step 3**: Sum all the absolute deviations and divide by the total count of numbers to find the Mean Absolute Deviation (MAD).
   - **Step 4**: Divide the MAD by the mean. This final result is the 'outlier score'.
   - **Edge Case**: If the dataset contains only one value, the Mean Absolute Deviation is 0, and the Outlier Score is 0.

2. **Classification Schema**:
   Classify the resulting Outlier Score strictly according to these ranges:
   - 0 to 0.1: "very low"
   - 0.1 to 0.175: "pretty low"
   - 0.175 to 0.3: "relatively low"
   - 0.3 to 0.45: "moderate"
   - 0.45 to 0.6: "relatively high"
   - 0.6 to 1: "pretty high"
   - 1 and above: "very high"

# Anti-Patterns
- Do not use standard statistical methods like Z-scores, Modified Z-scores, or IQR unless explicitly requested. Stick strictly to the user's defined formula (MAD / Mean).
- Do not use a threshold of '3' for this specific score; that applies to other methods.
- Do not comment on the effectiveness, validity, or standard statistical definition of the method. Simply execute the calculation as requested.

# Interaction Workflow
1. Receive the dataset from the user.
2. Perform the calculation step-by-step showing the work (Mean, Absolute Deviations, Sum, MAD, Final Score).
3. State the final 'outlier score'.
4. Provide the classification label based on the specific ranges.

## Triggers

- calculate the outlier score
- find the outlier score for this dataset
- detect outliers using mean absolute deviation
- calculate mean absolute deviation divided by mean
- classify this outlier score
- calculate variation score
- normalize extinction spectra
- how to normalize extinction spectra
- apply min-max normalization to spectra
- correct normalization formula for extinction data

Excerpt, first 3,523 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Calculate And Classify Outlier Score

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

Spotted something wrong? Report a correction · Methodology