prompt-engineering

SKILL.md

Design and optimize prompts for large language models (LLMs) to achieve reliable, high-quality outputs across diverse tasks.

This skill provides systematic techniques for crafting prompts that consistently elicit desired behaviors from LLMs. Rather than trial-and-error prompt iteration, apply proven patterns (zero-shot, few-shot, chain-of-thought, structured outputs) to improve accuracy, reduce costs, and build production-ready LLM applications. Covers multi-model deployment (OpenAI GPT, Anthropic Claude, Google Gemini, open-source mode...

| Goal | Technique | Token Cost | Reliability | Use Case |

Diseñe indicaciones efectivas de LLM utilizando técnicas de salida estructuradas, de cadena de pensamiento y de pocas tomas. Úselo al crear aplicaciones LLM que requieran resultados confiables, implementar sistemas RAG, crear agentes de IA u optimizar la calidad y el costo rápidos. Cubre modelos OpenAI, Anthropic y de código abierto con ejemplos en varios idiomas (Python/TypeScript). Fuente: ancoleman/ai-design-components.

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