LLM Intent Classifier
Classifies text and detects intents using zero-shot and few-shot LLM techniques with structured output support.
The LLM Classifier skill empowers Claude to perform advanced text categorization and intent detection by leveraging sophisticated classification patterns such as zero-shot, few-shot, and chain-of-thought reasoning. It provides a robust framework for designing complex classification taxonomies, implementing confidence scoring, and ensuring deterministic structured outputs via JSON. This skill is ideal for developers building agentic workforces or dialogue systems that require high-precision intent identification to drive automated workflows and self-orchestration without hallucinations.
주요 기능
01Structured JSON output for label enforcement
02Integrated confidence scoring and calibration
03Chain-of-thought reasoning for complex taxonomies
04Support for multi-label classification scenarios
05Zero-shot and few-shot classification implementation
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사용 사례
01Automating support ticket or document categorization workflows
02Designing deterministic routing logic for multi-agent systems
03Building intent detection systems for conversational AI and agents