foryourhealth111-pixel/anomaly-detector
bundled/skills/anomaly-detector/SKILL.md
Detect outliers, spikes, rare events, and abnormal records in tabular or time-series data. Use when the task is anomaly detection or suspicious-pattern review, not generic data-quality linting or full ML pipeline ownership.
$ install --global
skillsauthnpx skillsauth add foryourhealth111-pixel/vco-skills-codex anomaly-detectorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 20, 2026, 2:20 AM9.0s1 file scanned
SKILL.md
- name:
- anomaly-detector
- description:
- |
- allowed-tools:
- Read, Write, Edit, Bash, Grep
- version:
- 1.0.0
- license:
- MIT
- author:
- Jeremy Longshore <[email protected]>
Anomaly Detector
Purpose
Use this skill when the user needs to identify abnormal rows, drift, spikes, or rare-event behavior in data.
When to Use
Use this skill when:
- Investigating outlier transactions, sensor spikes, fraud candidates, or rare failures
- Comparing statistical, distance-based, or density-based anomaly detection approaches
- Setting anomaly thresholds and reviewing false positives or false negatives
Not For / Boundaries
- Generic schema/null/range validation: use
data-quality-checker - Publication-grade figure polishing: use
scientific-visualization - End-to-end supervised model training: use
training-machine-learning-models
Typical Outputs
- Candidate anomaly rules or model choices
- Thresholding and review workflow
- Follow-up plots or tables showing suspicious records
Related Skills
data-quality-checkerfor dataset sanity checks before anomaly reviewcreating-data-visualizationsfor general charts after anomalies are identified
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