Detecting Data Anomalies

foryourhealth111-pixel/detecting-data-anomalies

| Investigate outliers, rare events, spikes, and suspicious records in datasets. Use as an explicit anomaly-analysis helper when you want concrete anomaly-detection workflow guidance, not generic data validation or end-to-end ML ownership.

4k tokens

context cost

the whole folder, loaded on every use

11

files

ships runnable scripts

0

copies elsewhere

how many repositories repackaged it

2583

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/foryourhealth111-pixel/Vibe-Skills --skill detecting-data-anomalies

What comes with it

16 383 bytes besides the instruction

assets/README.md

references/README.md

references/errors.md

references/examples.md

references/implementation.md

scripts/README.md

scripts/algorithm_selector.py

scripts/anomaly_visualizer.py

scripts/data_loader.py

scripts/report_generator.py

What it tells the agent to use

found in the instruction text

Bash runs shell commands — read the instruction before connecting

The instruction itself

6 sections, as written by the author

Detecting Data Anomalies

Positioning

Treat this skill as an explicit/manual helper.

In governed ML routing, anomaly-detection ownership normally belongs to scikit-learn.

When to Use

Use this skill when:

Reviewing outlier transactions, fraud candidates, sensor spikes, or rare failures
Comparing isolation forest, one-class SVM, LOF, or threshold-based anomaly workflows
Turning suspicious records into a shortlist for human inspection

Not For / Boundaries

Null/duplicate/schema/range validation: use exploratory-data-analysis
Full model training or end-to-end pipeline ownership: use scikit-learn or ml-pipeline-workflow
Publication-grade figure production: use scientific-visualization

Typical Outputs

Candidate anomaly-detection methods and thresholds
A review checklist for false positives and false negatives
Suggested tables or plots for the suspicious subset

scikit-learn as the governed routed owner for classical anomaly-detection workflows
creating-data-visualizations after anomalies are identified

How to use it

Copy the folder

Take foryourhealth111-pixel/detecting-data-anomalies from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.