Detects anomalies and outliers in datasets using ML like Isolation Forest, One-Class SVM, LOF, autoencoders. For analyzing unusual patterns or deviations.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin anomaly-detection-systemThis skill is limited to using the following tools:
Identify anomalies and outliers in datasets using statistical and machine learning algorithms including Isolation Forest, One-Class SVM, Local Outlier Factor, and autoencoders. This skill handles the full detection pipeline from data ingestion and feature scaling through algorithm selection, threshold tuning, and result interpretation with anomaly scoring.
Identifies outliers and anomalies in data using statistical methods like Z-score, Isolation Forest, Local Outlier Factor, DBSCAN, and autoencoders. Useful for fraud detection, quality monitoring, and system health checks.
Detects anomalies in datasets using Z-score, IQR, modified Z-score, Isolation Forest, LOF, rolling windows, and STL decomposition for tabular and time-series data.
Provides step-by-step guidance and generates code for anomaly detection in data analytics, including SQL queries, data visualization, statistical analysis, and business intelligence.
Share bugs, ideas, or general feedback.
Identify anomalies and outliers in datasets using statistical and machine learning algorithms including Isolation Forest, One-Class SVM, Local Outlier Factor, and autoencoders. This skill handles the full detection pipeline from data ingestion and feature scaling through algorithm selection, threshold tuning, and result interpretation with anomaly scoring.
pip install scikit-learn)pip install pandas numpy)pip install matplotlib seaborn)See ${CLAUDE_SKILL_DIR}/references/implementation.md for the detailed implementation guide.
| Error | Cause | Solution |
|---|---|---|
| Insufficient data volume | Fewer than 100 data points for model fitting | Collect additional data or switch to simple statistical methods (z-score, IQR) |
| High false positive rate | Contamination parameter set too high or features not scaled | Lower contamination to 0.01; verify StandardScaler applied; refine feature selection |
| Algorithm OOM on large dataset | Isolation Forest or LOF exceeds available memory | Subsample data for training; use max_samples parameter; switch to streaming approach |
| Feature scaling mismatch | Mixed numeric and categorical features without proper encoding | One-hot encode categoricals separately; scale numeric features independently |
| No ground truth for validation | Unlabeled dataset prevents accuracy measurement | Use domain expert review on top-N anomalies; implement feedback loop to refine threshold |
See ${CLAUDE_SKILL_DIR}/references/errors.md for the full error reference.
Scenario 1: Network Intrusion Detection -- Apply Isolation Forest to 50K network flow records with features: packet count, byte volume, duration, protocol type. Expected contamination: 2%. Target: flag port-scan and DDoS patterns with precision above 0.85.
Scenario 2: Manufacturing Quality Control -- Run LOF on sensor readings (temperature, vibration, pressure) from 10K production cycles. Detect equipment degradation anomalies. Visualize flagged cycles on a time-series plot with normal operating bands.
Scenario 3: Financial Transaction Monitoring -- Train an autoencoder on 100K legitimate transactions. Reconstruct test transactions and flag those with reconstruction error above the 99th percentile. Report flagged transactions with amount, merchant category, and time-of-day features.