flowchart TD
    DataStream["Time-Series & Multidimensional Numerical Stream"] --> AnomalyDetector["Statistical Z-Score & IQR Anomaly Detector"]
    
    AnomalyDetector -->|Outlier Detected| FlagAnomaly["Flag Outlier & Alert Telemetry"]
    AnomalyDetector -->|Clean Stream| ModelingBranch{"Analytical Objective"}
    
    ModelingBranch -->|Trend Forecasting| HoltForecaster["Holt Linear Exponential Smoothing Forecaster"]
    ModelingBranch -->|Supervised Fitting| LinearRegression["Multivariate Gradient Descent Regression Engine"]
    ModelingBranch -->|Hypothesis Testing| WelchTest["Welch's Two-Sample T-Test Evaluator"]
    ModelingBranch -->|Dimensionality Reduction| PCAReducer["Power Iteration SVD/PCA Dimension Reducer"]
    
    HoltForecaster --> Synthesis["Agent Synthesis & Statistical Report"]
    LinearRegression --> Synthesis
    WelchTest --> Synthesis
    PCAReducer --> Synthesis

from client import StatisticalZscoreIqrAnomalyDetector

# Initialize engine
engine = StatisticalZscoreIqrAnomalyDetector()

# Execute self-testing benchmark suite
result = engine.run_benchmark_anomaly_detector()
print("Execution Result:", result)

{
  "mcpServers": {
    "genpark-statistical-zscore-iqr-anomaly-detector-skill": {
      "command": "python",
      "args": ["-u", "/path/to/genpark-statistical-zscore-iqr-anomaly-detector-skill/mcp_server.py"]
    }
  }
}

pip install git+https://github.com/alphaparkinc/genpark-statistical-zscore-iqr-anomaly-detector-skill.git