llm-classifier
LLM-based zero-shot and few-shot classification for flexible intent detection
Best use case
llm-classifier is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
LLM-based zero-shot and few-shot classification for flexible intent detection
Teams using llm-classifier should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/llm-classifier/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How llm-classifier Compares
| Feature / Agent | llm-classifier | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
LLM-based zero-shot and few-shot classification for flexible intent detection
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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process-builder
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