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skills/model-evaluation-suite/SKILL.md

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+---
+name: evaluating-machine-learning-models
+description: |
+  This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".
+allowed-tools: Read, Write, Edit, Grep, Glob, Bash
+version: 1.0.0
+---
+
+## Overview
+
+This skill empowers Claude to perform thorough evaluations of machine learning models, providing detailed performance insights. It leverages the `model-evaluation-suite` plugin to generate a range of metrics, enabling informed decisions about model selection and optimization.
+
+## How It Works
+
+1. **Analyzing Context**: Claude analyzes the user's request to identify the model to be evaluated and any specific metrics of interest.
+2. **Executing Evaluation**: Claude uses the `/eval-model` command to initiate the model evaluation process within the `model-evaluation-suite` plugin.
+3. **Presenting Results**: Claude presents the generated metrics and insights to the user, highlighting key performance indicators and potential areas for improvement.
+
+## When to Use This Skill
+
+This skill activates when you need to:
+- Assess the performance of a machine learning model.
+- Compare the performance of multiple models.
+- Identify areas where a model can be improved.
+- Validate a model's performance before deployment.
+
+## Examples
+
+### Example 1: Evaluating Model Accuracy
+
+User request: "Evaluate the accuracy of my image classification model."
+
+The skill will:
+1. Invoke the `/eval-model` command.
+2. Analyze the model's performance on a held-out dataset.
+3. Report the accuracy score and other relevant metrics.
+
+### Example 2: Comparing Model Performance
+
+User request: "Compare the F1-score of model A and model B."
+
+The skill will:
+1. Invoke the `/eval-model` command for both models.
+2. Extract the F1-score from the evaluation results.
+3. Present a comparison of the F1-scores for model A and model B.
+
+## Best Practices
+
+- **Specify Metrics**: Clearly define the specific metrics of interest for the evaluation.
+- **Data Validation**: Ensure the data used for evaluation is representative of the real-world data the model will encounter.
+- **Interpret Results**: Provide context and interpretation of the evaluation results to facilitate informed decision-making.
+
+## Integration
+
+This skill integrates seamlessly with the `model-evaluation-suite` plugin, providing a comprehensive solution for model evaluation within the Claude Code environment. It can be combined with other skills to build automated machine learning workflows.

skills/model-evaluation-suite/assets/README.md

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+# Assets
+
+Bundled resources for model-evaluation-suite skill
+
+- [ ] evaluation_template.md: Template for generating evaluation reports with placeholders for metrics and visualizations.
+- [ ] example_dataset.csv: Example dataset for testing the evaluation process.
+- [ ] visualization_script.py: Script to generate visualizations of model performance metrics.

skills/model-evaluation-suite/assets/visualization_script.py

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+#!/usr/bin/env python3
+
+"""
+visualization_script.py
+
+This script generates visualizations of model performance metrics.
+It supports various plot types and data formats.
+
+Example Usage:
+    To generate a scatter plot of predicted vs. actual values:
+    python visualization_script.py --plot_type scatter --actual_values actual.csv --predicted_values predicted.csv --output scatter_plot.png
+
+    To generate a histogram of errors:
+    python visualization_script.py --plot_type histogram --errors errors.csv --output error_histogram.png
+"""
+
+import argparse
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import os
+
+
+def generate_scatter_plot(actual_values_path, predicted_values_path, output_path):
+    """
+    Generates a scatter plot of actual vs. predicted values.
+
+    Args:
+        actual_values_path (str): Path to the CSV file containing actual values.
+        predicted_values_path (str): Path to the CSV file containing predicted values.
+        output_path (str): Path to save the generated plot.
+    """
+    try:
+        actual_values = pd.read_csv(actual_values_path).values.flatten()
+        predicted_values = pd.read_csv(predicted_values_path).values.flatten()
+
+        plt.figure(figsize=(10, 8))
+        sns.scatterplot(x=actual_values, y=predicted_values)
+        plt.xlabel("Actual Values")
+        plt.ylabel("Predicted Values")
+        plt.title("Actual vs. Predicted Values")
+        plt.savefig(output_path)
+        plt.close()
+
+        print(f"Scatter plot saved to {output_path}")
+
+    except FileNotFoundError as e:
+        print(f"Error: File not found: {e}")
+    except Exception as e:
+        print(f"Error generating scatter plot: {e}")
+
+
+def generate_histogram(errors_path, output_path):
+    """
+    Generates a histogram of errors.
+
+    Args:
+        errors_path (str): Path to the CSV file containing errors.
+        output_path (str): Path to save the generated plot.
+    """
+    try:
+        errors = pd.read_csv(errors_path).values.flatten()
+
+        plt.figure(figsize=(10, 8))
+        sns.histplot(errors, kde=True)  # Add kernel density estimate
+        plt.xlabel("Error")
+        plt.ylabel("Frequency")
+        plt.title("Distribution of Errors")
+        plt.savefig(output_path)
+        plt.close()
+
+        print(f"Histogram saved to {output_path}")
+
+    except FileNotFoundError as e:
+        print(f"Error: File not found: {e}")
+    except Exception as e:
+        print(f"Error generating histogram: {e}")
+
+
+def generate_residual_plot(actual_values_path, predicted_values_path, output_path):
+    """
+    Generates a residual plot.
+
+    Args:
+        actual_values_path (str): Path to the CSV file containing actual values.
+        predicted_values_path (str): Path to the CSV file containing predicted values.
+        output_path (str): Path to save the generated plot.
+    """
+    try:
+        actual_values = pd.read_csv(actual_values_path).values.flatten()
+        predicted_values = pd.read_csv(predicted_values_path).values.flatten()
+
+        residuals = actual_values - predicted_values
+
+        plt.figure(figsize=(10, 8))
+        sns.scatterplot(x=predicted_values, y=residuals)
+        plt.xlabel("Predicted Values")
+        plt.ylabel("Residuals")
+        plt.title("Residual Plot")
+        plt.axhline(y=0, color='r', linestyle='--')  # Add a horizontal line at y=0
+        plt.savefig(output_path)
+        plt.close()
+
+        print(f"Residual plot saved to {output_path}")
+
+    except FileNotFoundError as e:
+        print(f"Error: File not found: {e}")
+    except Exception as e:
+        print(f"Error generating residual plot: {e}")
+
+
+def main():
+    """
+    Main function to parse arguments and generate visualizations.
+    """
+    parser = argparse.ArgumentParser(
+        description="Generate visualizations of model performance metrics."
+    )
+    parser.add_argument(
+        "--plot_type",
+        type=str,
+        required=True,
+        choices=["scatter", "histogram", "residual"],
+        help="Type of plot to generate (scatter, histogram, residual).",
+    )
+    parser.add_argument(
+        "--actual_values",
+        type=str,
+        help="Path to the CSV file containing actual values (required for scatter and residual plots).",
+    )
+    parser.add_argument(
+        "--predicted_values",
+        type=str,
+        help="Path to the CSV file containing predicted values (required for scatter and residual plots).",
+    )
+    parser.add_argument(
+        "--errors",
+        type=str,
+        help="Path to the CSV file containing errors (required for histogram).",
+    )
+    parser.add_argument(
+        "--output", type=str, required=True, help="Path to save the generated plot."
+    )
+
+    args = parser.parse_args()
+
+    if args.plot_type == "scatter":
+        if not args.actual_values or not args.predicted_values:
+            print(
+                "Error: --actual_values and --predicted_values are required for scatter plots."
+            )
+            return
+        generate_scatter_plot(args.actual_values, args.predicted_values, args.output)
+    elif args.plot_type == "histogram":
+        if not args.errors:
+            print("Error: --errors is required for histograms.")
+            return
+        generate_histogram(args.errors, args.output)
+    elif args.plot_type == "residual":
+        if not args.actual_values or not args.predicted_values:
+            print(
+                "Error: --actual_values and --predicted_values are required for residual plots."
+            )
+            return
+        generate_residual_plot(args.actual_values, args.predicted_values, args.output)
+
+
+if __name__ == "__main__":
+    main()

skills/model-evaluation-suite/references/README.md

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+# References
+
+Bundled resources for model-evaluation-suite skill
+
+- [ ] metrics_definitions.md: Detailed definitions and explanations of all supported evaluation metrics.
+- [ ] dataset_schemas.md: Schemas for supported datasets, including required fields and data types.
+- [ ] model_api_documentation.md: Documentation for the model API, including input/output formats and authentication details.

skills/model-evaluation-suite/scripts/README.md

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+# Scripts
+
+Bundled resources for model-evaluation-suite skill
+
+- [ ] evaluate_model.py: Script to execute model evaluation using specified metrics and datasets.
+- [ ] data_loader.py: Script to load model and datasets for evaluation.
+- [ ] metrics_calculator.py: Script to calculate evaluation metrics based on model predictions and ground truth.