This skill provides a comprehensive framework for mastering advanced prompt engineering within Claude Code, enabling developers to build more reliable, controllable, and high-performing LLM applications. It encompasses essential patterns such as dynamic few-shot learning, chain-of-thought reasoning, and modular template systems that allow for variable interpolation and conditional logic. By applying these industry-standard patterns, developers can systematically refine system prompts, implement robust error recovery mechanisms, and establish automated optimization workflows to ensure consistent, high-quality AI outputs across complex production use cases.
주요 기능
01Systematic optimization workflows for iterative refinement and A/B testing
02Modular prompt template systems with variable interpolation and formatting
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04Advanced few-shot learning with dynamic example selection and semantic similarity
05Built-in error recovery and self-verification strategies for graceful failures
06Structured reasoning elicitation using Chain-of-Thought (CoT) and self-consistency
