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skills/classification-model-builder/SKILL.md

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+---
+name: building-classification-models
+description: |
+  This skill enables Claude to construct and evaluate classification models using provided datasets or specifications. It leverages the classification-model-builder plugin to automate model creation, optimization, and reporting. Use this skill when the user requests to "build a classifier", "create a classification model", "train a classification model", or needs help with supervised learning tasks involving labeled data. The skill ensures best practices are followed, including data validation, error handling, and performance metric reporting.
+allowed-tools: Read, Write, Edit, Grep, Glob, Bash
+version: 1.0.0
+---
+
+## Overview
+
+This skill empowers Claude to efficiently build and deploy classification models. It automates the process of model selection, training, and evaluation, providing users with a robust and reliable classification solution. The skill also provides insights into model performance and suggests potential improvements.
+
+## How It Works
+
+1. **Context Analysis**: Claude analyzes the user's request, identifying the dataset, target variable, and any specific requirements for the classification model.
+2. **Model Generation**: The skill utilizes the classification-model-builder plugin to generate code for training a classification model based on the identified dataset and requirements. This includes data preprocessing, feature selection, model selection, and hyperparameter tuning.
+3. **Evaluation and Reporting**: The generated model is trained and evaluated using appropriate metrics (e.g., accuracy, precision, recall, F1-score). Performance metrics and insights are then provided to the user.
+
+## When to Use This Skill
+
+This skill activates when you need to:
+- Build a classification model from a given dataset.
+- Train a classifier to predict categorical outcomes.
+- Evaluate the performance of a classification model.
+
+## Examples
+
+### Example 1: Building a Spam Classifier
+
+User request: "Build a classifier to detect spam emails using this dataset."
+
+The skill will:
+1. Analyze the provided email dataset to identify features and the target variable (spam/not spam).
+2. Generate Python code using the classification-model-builder plugin to train a spam classification model, including data cleaning, feature extraction, and model selection.
+
+### Example 2: Predicting Customer Churn
+
+User request: "Create a classification model to predict customer churn using customer data."
+
+The skill will:
+1. Analyze the customer data to identify relevant features and the churn status.
+2. Generate code to build a classification model for churn prediction, including data validation, model training, and performance reporting.
+
+## Best Practices
+
+- **Data Quality**: Ensure the input data is clean and preprocessed before training the model.
+- **Model Selection**: Choose the appropriate classification algorithm based on the characteristics of the data and the specific requirements of the task.
+- **Hyperparameter Tuning**: Optimize the model's hyperparameters to achieve the best possible performance.
+
+## Integration
+
+This skill integrates with the classification-model-builder plugin to automate the model building process. It can also be used in conjunction with other plugins for data analysis and visualization.

skills/classification-model-builder/assets/README.md

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+# Assets
+
+Bundled resources for classification-model-builder skill
+
+- [ ] model_config_template.json: A template JSON file for specifying model configurations, including hyperparameters and training parameters.
+- [ ] example_dataset.csv: A sample CSV dataset that can be used for testing the classification model builder.
+- [ ] report_template.html: An HTML template for generating the model performance report.

skills/classification-model-builder/assets/model_config_template.json

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+{
+  "_comment": "Model configuration template for the classification model builder plugin.",
+  "model_name": "ExampleClassifier",
+  "_comment": "A descriptive name for your model.",
+  "model_type": "RandomForestClassifier",
+  "_comment": "The type of classification model to use (e.g., RandomForestClassifier, LogisticRegression, SVM).",
+  "data_path": "data/training_data.csv",
+  "_comment": "Path to the CSV file containing the training data.",
+  "target_column": "target",
+  "_comment": "The name of the column containing the target variable.",
+  "features": [
+    "feature1",
+    "feature2",
+    "feature3",
+    "feature4"
+  ],
+  "_comment": "List of column names to use as features. If empty, all columns except the target_column will be used.",
+  "hyperparameters": {
+    "_comment": "Hyperparameters specific to the chosen model type.",
+    "n_estimators": 100,
+    "_comment": "Number of trees in the random forest (example for RandomForestClassifier).",
+    "max_depth": 10,
+    "_comment": "Maximum depth of the trees (example for RandomForestClassifier).",
+    "learning_rate": 0.1
+    "_comment": "Learning rate for gradient boosting models (example for GradientBoostingClassifier)."
+  },
+  "training_parameters": {
+    "_comment": "Parameters related to the training process.",
+    "test_size": 0.2,
+    "_comment": "The proportion of the data to use for testing.",
+    "random_state": 42,
+    "_comment": "A random seed for reproducibility.",
+    "stratify": true
+    "_comment": "Whether to stratify the target variable during train/test split."
+  },
+  "evaluation_metrics": [
+    "accuracy",
+    "precision",
+    "recall",
+    "f1-score",
+    "roc_auc"
+  ],
+  "_comment": "List of evaluation metrics to compute on the test set.",
+  "model_save_path": "models/example_classifier.pkl",
+  "_comment": "Path to save the trained model.",
+  "feature_importance": true,
+  "_comment": "Boolean value to toggle feature importance calculation.",
+  "preprocessing": {
+    "_comment": "Configuration for data preprocessing steps.",
+    "handle_missing_values": "impute",
+    "_comment": "How to handle missing values (e.g., 'impute', 'remove', 'none').",
+    "missing_value_strategy": "mean",
+    "_comment": "Strategy for imputation (e.g., 'mean', 'median', 'most_frequent').",
+    "scale_features": true,
+    "_comment": "Whether to scale numerical features using StandardScaler or similar.",
+    "feature_scaling_method": "standard"
+    "_comment": "Method to use for feature scaling ('standard' or 'minmax')."
+  }
+}

skills/classification-model-builder/references/README.md

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+# References
+
+Bundled resources for classification-model-builder skill
+
+- [ ] model_evaluation_metrics.md: Detailed explanations of various classification model evaluation metrics (accuracy, precision, recall, F1-score, AUC-ROC) and their interpretation.
+- [ ] data_preprocessing_guide.md: Best practices for data preprocessing, including handling missing values, feature scaling, and encoding categorical variables.
+- [ ] model_selection_guide.md: Guidelines for selecting the appropriate classification model based on the characteristics of the dataset and the problem being solved.
+- [ ] hyperparameter_tuning.md: Techniques for hyperparameter tuning to optimize model performance.

skills/classification-model-builder/scripts/README.md

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+# Scripts
+
+Bundled resources for classification-model-builder skill
+
+- [ ] model_builder.py: Automates the process of building, training, and evaluating classification models. Takes dataset path and model configuration as input.
+- [ ] data_validator.py: Validates the input dataset for common issues like missing values, incorrect data types, and imbalanced classes.
+- [ ] report_generator.py: Generates a comprehensive report of the model's performance, including metrics like accuracy, precision, recall, and F1-score.