llm-classifier

# LLM Classifier Skill ## Capabilities - Implement zero-shot classification with LLMs - Design few-shot classification prompts - Configure structured output for labels - Implement confidence scoring - Design classification taxonomies - Handle multi-label classification ## Target Processes - intent-classification-system - dialogue-flow-design ## Implementation Details ### Classification Patterns 1. **Zero-Shot**: No examples, description-based 2. **Few-Shot**: Example-based classification 3. **Structured Output**: JSON schema for labels 4. **Chain-of-Thought**: Reasoning before classification 5. **Ensemble**: Multiple prompts/models ### Configuration Options - LLM model selection - Label descriptions - Example selection strategy - Output format specification - Confidence calibration ### Best Practices - Clear label descriptions - Representative examples - Consistent output format - Calibrate confidence scores - Test with edge cases ### Dependencies - langchain-core - LLM provider