thought-based-reasoning

SKILL.md

Chain-of-Thought (CoT) prompting and its variants encourage LLMs to generate intermediate reasoning steps before arriving at a final answer, significantly improving performance on complex reasoning tasks. These techniques transform how models approach problems by making implicit reasoning explicit.

| Technique | When to Use | Complexity | Accuracy Gain |

| Zero-shot CoT | Quick reasoning, no examples available | Low | +20-60% | | Few-shot CoT | Have good examples, consistent format needed | Medium | +30-70% | | Self-Consistency | High-stakes decisions, need confidence | Medium | +10-20% over CoT | | Tree of Thoughts | Complex problems requiring exploration | High | +50-70% on hard tasks |

Úselo al abordar tareas de razonamiento complejas que requieren lógica paso a paso, aritmética de varios pasos, razonamiento de sentido común, manipulación simbólica o problemas en los que fallan las indicaciones simples: proporciona una guía completa de la cadena de pensamiento y técnicas de indicaciones relacionadas (Zero-shot CoT, autoconsistencia, árbol de pensamientos, de menor a mayor, ReAct, PAL, reflexión) con plantillas, matrices de decisión y patrones respaldados por investigaciones. Fuente: neolabhq/context-engineering-kit.

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