# LLM Classifier

> LLM-based zero-shot and few-shot classification for flexible intent detection

- Skill: `a5c-ai/llm-classifier` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add a5c-ai/llm-classifier`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/llm-classifier/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/llm-classifier

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# 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