Data Anomaly Detection

---
name: data-anomaly-detection
description: "Use when detecting anomalies in data."
version: 1.0.0
author: Hermes Agent
license: MIT
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [data-science, anomaly-detection, outlier-detection, ml]
    related_skills: ['anomaly-detection-ml']
---

## Overview
Detect anomalies and outliers in datasets using statistical and ML methods.

## When to Use
- "Data Anomaly Detection design and architecture"
- "Best practices for Data Anomaly Detection"
- "Data Anomaly Detection implementation and deployment"
- "Data Anomaly Detection optimization and monitoring"
- "Data Anomaly Detection troubleshooting and scaling"

## Key Concepts
1. Foundational concepts
2. Implementation approaches
3. Testing and validation

## Implementation Patterns
1. Define clear requirements and specifications
2. Choose appropriate tools and frameworks
3. Implement with modular, maintainable code
4. Write tests and automate verification
5. Document architecture and decisions
6. Monitor performance and iterate

## Common Pitfalls
1. **Not accounting for constraints** — resource or timeline limitations
2. **Ignoring industry standards** — not following established best practices
3. **Poor stakeholder alignment** — conflicting requirements
4. **Inadequate testing** — no validation of critical functions
5. **Not documenting decisions** — lost knowledge transfer

## Verification Checklist
- [ ] Requirements documented
- [ ] Standards reviewed
- [ ] Design validated
- [ ] Testing established
- [ ] Documentation complete