anomaly-detector
Anomaly and outlier detection using Isolation Forest, One-Class SVM, autoencoders, and statistical methods. Activates for "anomaly detection", "outlier detection", "fraud detection", "intrusion detection", "abnormal behavior", "unusual patterns", "detect anomalies", "system monitoring". Handles supervised and unsupervised anomaly detection with SpecWeave increment integration.
npx skills add majiayu000/claude-skill-registry --skill anomaly-detector-anton-abyzov-specweave-9dc68c1f --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Anomaly Detector ## Overview Detect unusual patterns, outliers, and anomalies in data using statistical methods, machine learning, and deep learning. Critical for fraud detection, security monitoring, quality control, and system health monitoring—all integrated with SpecWeave's increment workflow. ## Why Anomaly Detection is Different **Challenge**: Anomalies are rare (0.1% - 5% of data) **Standard classification doesn't work**: - ❌ Extreme class imbalance - ❌ Unknown anomaly patterns - ❌ Expensive to label anomalies - ❌ Anomalies evolve over time **Anomaly detection approaches**: - ✅ Unsupervised (no labels needed) - ✅ Semi-supervised (learn from normal data) - ✅ Statistical (deviation from expected) - ✅ Context-aware (what's normal for this user/time/location?) ## Anomaly Detection Methods ### 1. Statistical Methods (Baseline) **Z-Score / Standard Deviation**: ```python from specweave import AnomalyDetector detector = AnomalyDetector( method="statistical", increment="0042" ) # Flag values > 3 standard deviations from mean anomalies = detector.detect( data=transaction_amounts, threshold=3.0 ) # Simple, fast, but assumes normal distribution ``` **IQR (Interquartile Range)**: ```p
- Overview
- Why Anomaly Detection is Different
- Anomaly Detection Methods
- 1. Statistical Methods (Baseline)
- 2. Isolation Forest (Recommended)
- 3. One-Class SVM
- 4. Autoencoders (Deep Learning)
- 5. LOF (Local Outlier Factor)
- Anomaly Detection Workflows
- Workflow 1: Fraud Detection
- Workflow 2: System Anomaly Detection
- Workflow 3: Manufacturing Quality Control
- Workflow 4: Network Intrusion Detection
- Evaluation Metrics
Train anomaly detector Evaluate detector Explain top anomalies
What does the anomaly-detector skill do?
Anomaly and outlier detection using Isolation Forest, One-Class SVM, autoencoders, and statistical methods. Activates for "anomaly detection", "outlier detection", "fraud detection", "intrusion detection", "abnormal behavior", "unusual patterns", "detect anomalies", "system monitoring". Handles supervised and unsupervised anomaly detection with SpecWeave increment integration.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill anomaly-detector-anton-abyzov-specweave-9dc68c1f --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.