Agent skill · data analytics · jeremylongshore
detecting-data-anomalies
Process identify anomalies and outliers in datasets using machine learning
Why this skill is useful
Provides specific machine learning algorithms and a detailed pipeline for anomaly detection that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, python, scikit-learn, seaborn installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Detecting Data Anomalies Overview 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. Prerequisites Python 3.9+ with scikit-learn >= 1.3 (pip install scikit-learn) pandas and NumPy for data manipulation (pip install pandas numpy) matplotlib or seaborn for anomaly visualizations (pip install matplotlib seaborn) Dataset in CSV, JSON, Parquet, or database-queryable format Minimum 500 data points for statistical significance (1000+ recommended) Optional: PyTorch or TensorFlow for autoencoder-based detection on complex patterns Instructions 1. Load the dataset using the Read tool and verify schema, column types, and row count 2. Profile feature distributions using descriptive statistics to understand baseline behavior 3. Handle missing values via imputation (median for numeric, mode for categorical) or row exclusion 4. Apply StandardScaler or MinMaxScaler to numeric features to normalize magnitude differences 5. Select the detection algorithm based on data characteristics: Isolation Forest: high-dimensional data, no assumptions on distribution One-Class SVM: well-defined normal class with clear decision boundary Local Outlier Factor: density-varying data with local anomaly patterns Autoencoder: complex temporal or image data with non-linear relationships 6. Set the contamination parameter to the expected anomaly proportion (start with 0.01-0.05) 7. Fit the model on the training partition and generate anomaly scores for each data point 8. Apply the decision threshold to classify points as normal (-1) or anomalous (1) 9. Analyze flagged anomalies for common characteristics, temporal clusters, or feature correlations 10. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/detecting-data-anomalies--e87323