llm-classifier
LLM-based zero-shot and few-shot classification for flexible intent detection
npx skills add majiayu000/claude-skill-registry --skill llm-classifier --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Capabilities
- Target Processes
- Implementation Details
- Classification Patterns
- Configuration Options
- Best Practices
- Dependencies
What does the llm-classifier skill do?
LLM-based zero-shot and few-shot classification for flexible intent detection
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill llm-classifier --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.