Detecting Data Anomalies
SkillDatabases & dataGuides your agent through finding outliers, spikes, and suspicious records in a dataset.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Detecting Data Anomalies skill
About this capability
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.
What this skill tells your AI
The instructions your AI receives, as published by foryourhealth111-pixel/vibe-skills in bundled/skills/detecting-data-anomalies/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 3k
- Forks
- 277
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
detecting-data-anomalies- Source
- github.com/foryourhealth111-pixel/vibe-skills