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From classifier
Trains a text classifier by associating a category with a text sample or file pattern. Build labeled datasets with multiple categories.
npx claudepluginhub cardmagic/classifierHow this command is triggered — by the user, by Claude, or both
Slash command
/classifier:trainThe summary Claude sees in its command listing — used to decide when to auto-load this command
# /classifier:train Train a classifier with labeled examples. ## Usage ## Examples ## Instructions Run the classifier train command: After training, inform the user they can: 1. Add more training examples with additional `/classifier:train` commands 2. Classify new text with `classifier "text to classify"` 3. Save the model with `classifier save model-name.json` For best results, recommend balanced training data across all categories.
/text-classificationApplies LLM-based text classification expertise to a provided task, covering codebook design (Halterman & Keith format), learning regime selection, human-LLM hybrid workflows, cross-model validation, and agreement statistics.
/trainTrains machine learning or deep learning models in the project via standard workflow: environment checks, data preparation, configuration, launch with GPU support, and monitoring.
/trainTrains an ML model on specified dataset and parameters using scikit-learn, PyTorch, TensorFlow, or XGBoost. Generates code for data prep, training with CV, metrics, and model saving.
/build-classifierAnalyzes context to generate AI/ML task code with validation, error handling, performance metrics, artifacts, and documentation.
/analyze-sentimentAnalyzes context to generate ML task code with validation, error handling, performance metrics, insights, artifacts, and documentation.
/analyze-textAnalyzes context and requirements to generate AI/ML task code with validation, error handling, performance metrics, insights, artifacts, and documentation.
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Train a classifier with labeled examples.
/classifier:train <category> <text or file pattern>
/classifier:train spam "Buy cheap viagra now"
/classifier:train ham "Meeting scheduled for tomorrow"
/classifier:train positive reviews/good/*.txt
/classifier:train negative reviews/bad/*.txt
Run the classifier train command:
classifier train "$category" "$text_or_pattern"
After training, inform the user they can:
/classifier:train commandsclassifier "text to classify"classifier save model-name.jsonFor best results, recommend balanced training data across all categories.