Classify text sentiment and spam using pre-trained CLI models.
npx skills add https://github.com/cardmagic/ai-marketplace --skill classifier-cardmagic
Please help me install this Agent Skill. Skill: classifier Source: https://github.com/cardmagic/ai-marketplace/tree/main/plugins/classifier/skills/classifier Command: npx skills add https://github.com/cardmagic/ai-marketplace --skill classifier-cardmagic
This Skill enables automated text classification and emotion/sentiment detection for text data using pre-trained models or custom-trained classifiers.
Use the classifier CLI to quickly classify text with a pre-trained model: classifier -r sms-spam-filter "You won a free iPhone! Click here now!" For sentiment analysis: classifier -r imdb-sentiment "This product exceeded my expectations"
High-intent search queries and answers about installing and using this skill.
Text classification for spam detection uses the sms-spam-filter pre-trained model to automatically flag unwanted messages. You can run classifier -r sms-spam-filter "your text" to instantly categorize chat, email, or social media inputs.
Custom text classifier training allows you to build domain-specific sentiment models using the classifier train command. You can train, evaluate, save, and load custom models to accurately analyze customer feedback for your specific industry vocabulary.
Pre-trained models for emotion detection and sentiment analysis include sms-spam-filter, imdb-sentiment, and emotion-detection. These models enable quick text analytics across short and long inputs without requiring custom training.
Text classification supports both short and long text inputs for sentiment and emotion detection. The pre-trained and custom-trained models process varying text lengths to deliver accurate classification across customer feedback and chat data.
Automated text classification for content moderation uses pre-trained models to identify spam and detect emotions across social media inputs. The classifier CLI instantly evaluates text to filter inappropriate content and analyze customer feedback.
The classifier CLI operates without external dependencies to perform text classification and emotion detection. You can immediately use commands like classifier -r <model> 'text' and classifier models to manage pre-trained and custom models.
zjykzkIngest raw text data into an Obsidian wiki as searchable knowledge.
DTMC-marketplaceAnalyze text polarity and subjectivity using the TextBlob library.
pradyumnasagarAutomate inter-skill data transfer and provenance capture via JSON Material Passports.
SignmanalTrain custom BPE, WordPiece, or Unigram tokenizers for Hugging Face Transformers.
