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genpark-statistical-zscore-iqr-anomaly-detector-skill

genpark-statistical-zscore-iqr-anomaly-detector-skill

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    • A
      license
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      maintenance
      Enables agents to run zero-dependency statistical analysis natively over MCP, including Power Iteration PCA/SVD dimensionality reduction, Holt linear forecasting, modified Z-score and Tukey IQR anomaly detection, Welch's t-test, and multivariate gradient-descent regression on numerical data streams. Projects high-dimensional feature vectors into principal component representations without any external libraries.
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    • A
      license
      Not graded
      quality
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      maintenance
      Enables users to perform multivariate linear regression with gradient descent, Holt linear forecasting, Welch's t-test evaluation, anomaly detection, and PCA dimensionality reduction through MCP-compatible clients using only Python standard library tools.
      7
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP clients such as Claude Desktop, Cursor, and Windsurf to run two-sample Welch's t-tests that compute Satterthwaite degrees of freedom and two-tailed p-values for A/B test comparisons. It also exposes zero-dependency statistical primitives including Holt linear forecasting, Z-score/IQR anomaly detection, gradient-descent regression, and power-iteration PCA, all using only the Python standard library.
      7
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to run statistics and data-science computations over numerical streams using only the Python standard library, including Z-score/IQR anomaly detection, Holt linear forecasting, Welch's t-test hypothesis evaluation, gradient-descent multivariate regression, and power-iteration SVD/PCA dimensionality reduction. Exposes these capabilities over JSON-RPC 2.0 stdio so clients like Claude Desktop, Cursor, and Windsurf can project high-dimensional feature vectors into principal components without any external dependencies.
      7
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to run zero-dependency statistical modeling and data analysis through MCP, including multivariate linear regression via gradient descent, anomaly detection, time-series forecasting, hypothesis testing, and PCA dimensionality reduction.
      7
      MIT
    • A
      license
      Not graded
      quality
      Not graded
      maintenance
      Enables agents to detect outliers in numerical streams using multiple robust statistical strategies and to perform related analytics such as forecasting, regression, hypothesis testing, and dimensionality reduction via MCP.
      7
      MIT