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Agent skill · Audited from source

Calculate And Classify Outlier Score 2

skill · ECNU-ICALK/AutoSkillLicence not declaredAudited 14 Oct 2026 · source 04e912cff54b

★ 602pushed 157 days agoversion 0.1.1
✓SAFEGrade B · 89/100 · 14 Oct 2026 · 04e912c
602repo stars · whole ECNU-ICALK/AutoSkill
InstallView source on GitHub ↗

Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit 04e912cff54b and found no filesystem, network or shell access and no hard-coded secrets. What it does: 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.

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ModelsAtlas. “Calculate And Classify Outlier Score 2 (ECNU-ICALK/AutoSkill)”. Retrieved 14 Oct 2026. https://modelsatlas.com/skills/ecnu-icalk/calculate-and-classify-outlier-score-2

Trust audit

Audit 0.4.1 · source read 14 Oct 2026

Trust score89/100✓SAFE B
0 critical0 high0 medium0 low0 info

SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 16,667 of 17,602 audited skills.

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

LayerWhat we checkResultFindings
L0 ProvenanceRepo owner, licence, archive status, activityPASS0
L1 Static sourceSecrets, path traversal, supply chain, hidden codeNA0
L2 InstructionsPrompt injection, authority framing, hidden directivesPASS0
L3 InventoryThe files and declarations match the sourcePASS0
L4 SandboxRun in isolation and watch behaviourSKIPPED why0

Gates applied: no behavioural pass.

Findings (0)

No findings at any severity. We re-audit when the source changes.

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

Provenance

Who published it, and what the verdict is pinned to

What it tells the agent

Full source on GitHub ↗

Its description: 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.

---
id: "b7bdf40f-c632-49b3-9446-7e93c0040054"
name: "calculate_and_classify_outlier_score"
description: "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."
version: "0.1.1"
tags:
  - "statistics"
  - "outlier detection"
  - "data analysis"
  - "variation"
  - "classification"
triggers:
  - "calculate the outlier score"
  - "classify the outlier score"
  - "find variation or outliers in data"
  - "analyze dataset variation"
  - "assess data variation using MAD"
---

# 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

Excerpt, first 2,065 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.

Install

Pinned to the audited commit 04e912c

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

The author documents no install command, so this uses the repository directly. Our verdict covers commit 04e912cff54b only.

Works in

The author has not said which agents this skill supports, and we have not tested it in any.

Audit history

Every audit, every source change

  1. ✓SAFE B 89 / 100

    first audit · source 04e912cff54b

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Questions about Calculate And Classify Outlier Score 2

What does the Calculate And Classify Outlier Score 2 skill do?
AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
Is Calculate And Classify Outlier Score 2 safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Calculate And Classify Outlier Score 2 access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (04e912cff54b), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.

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