Detecting data anomalies
Skill foryourhealth111-pixel/Vibe-Skills/bundled/skills/detecting-data-anomalies
VibeSkills is a general-purpose Skill that automatically routes local Skills and intelligently orchestrates harness workflows.
npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill detecting-data-anomaliesAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
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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.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
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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-learnorml-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
Related Skills
scikit-learnas the governed routed owner for classical anomaly-detection workflowscreating-data-visualizationsafter anomalies are identified