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From classifier
Classifies text using pre-trained models for spam detection, sentiment analysis, and emotion detection. Supports custom training and model management.
npx claudepluginhub cardmagic/classifierHow this skill is triggered — by the user, by Claude, or both
Slash command
/classifier:classifierThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use when: User asks to classify text, detect spam, analyze sentiment, detect emotions, or use pre-trained ML models.
Builds and evaluates supervised classification models from labeled data using generated Python code. For spam detection, churn prediction, or similar tasks.
Analyzes text sentiment, classifying as positive, negative, or neutral with confidence scores for reviews, social media, and surveys.
Guides LLM text classification for survey data: codebook design, zero/few-shot/fine-tuning selection, model choice, human-LLM hybrids, validation, reproducibility.
Share bugs, ideas, or general feedback.
Use when: User asks to classify text, detect spam, analyze sentiment, detect emotions, or use pre-trained ML models.
Run classifier models to see all available models. Common ones:
| Model | Command | Use Case |
|---|---|---|
sms-spam-filter | classifier -r sms-spam-filter "text" | Spam detection |
imdb-sentiment | classifier -r imdb-sentiment "text" | Sentiment analysis |
emotion-detection | classifier -r emotion-detection "text" | Emotion classification |
# Classify with a pre-trained model
classifier -r <model-name> "text to classify"
# Example: detect spam
classifier -r sms-spam-filter "You won a free iPhone! Click here now!"
# Example: sentiment analysis
classifier -r imdb-sentiment "This movie was absolutely terrible"
# Example: emotion detection
classifier -r emotion-detection "I am so happy today"
# Train from text
classifier train positive "Great product, love it"
classifier train negative "Terrible quality, waste of money"
# Train from files
classifier train positive reviews/good/*.txt
classifier train negative reviews/bad/*.txt
# Classify after training
classifier "This product exceeded my expectations"
# List all available models
classifier models
# Show model details
classifier info <model-name>
# Save trained model
classifier save my-model.json
# Load saved model
classifier load my-model.json
classifier models to discover available pre-trained models