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