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 |

ステップバイステップのロジック、複数ステップの算術、常識的推論、記号操作、または単純なプロンプトが失敗する問題を必要とする複雑な推論タスクに取り組むときに使用します。思考連鎖および関連するプロンプト手法に関する包括的なガイドを提供します。 ソース: zpankz/mcp-skillset。

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