/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.
$ npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill detecting-data-anomalies --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/detecting-data-anomalies
Context preview
The summary Claude sees to decide when to auto-load this skill.
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.
SKILL.md
detecting-data-anomalies.SKILL.mdname: detecting-data-anomalies
description: |
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.
allowed-tools: Read, Bash(python:*), Grep, Glob
version: 1.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT
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
Related Skills
- `scikit-learn` as the governed routed owner for classical anomaly-detection workflows
- `creating-data-visualizations` after anomalies are identified
Read more
name: detecting-data-anomalies description: | 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. allowed-tools: Read, Bash(python:*), Grep, Glob version: 1.0.0 author: Jeremy Longshore <jeremy@intentsolutions.io> license: MIT
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
Related Skills
- `scikit-learn` as the governed routed owner for classical anomaly-detection workflows
- `creating-data-visualizations` after anomalies are identified
VibeSkills is a general-purpose Skill that automatically routes local Skills and intelligently orchestrates harness workflows.
Repo: foryourhealth111-pixel/Vibe-Skills
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