What problem does it solve? Writing effective prompts for large language models is often trial-and-error, producing inconsistent outputs, wasted tokens, and unreliable agent behavior. This Skill provides structured prompt engineering patterns and best practices to make LLM interactions predictable and controllable. ## Core Features & Use Cases - Few-Shot Learning: Teach models through input-output examples to enforce consistent formatting and reasoning patterns. - Chain-of-Thought Prompting: Request step-by-step reasoning to improve accuracy on complex analytical and multi-step tasks. - Prompt Optimization & Templates: Systematically iterate on prompts with A/B testing and build reusable template systems with variables and conditional sections. - Use Case: Imagine your support ticket classifier returns inconsistent JSON. Use this Skill to restructure the prompt with few-shot examples and explicit output schemas, then iterate until classification is stable. ## Quick Start Use the prompt-engineering skill to rewrite my customer support classification prompt with few-shot examples and a defined output format.
