context-engineering

SKILL.md

Context is the complete state available to a language model at inference time. It includes everything the model can attend to when generating responses: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Understanding context fundamentals is prerequisite to effective context engineering.

Context comprises several distinct components, each with different characteristics and constraints. The attention mechanism creates a finite budget that constrains effective context usage. Progressive disclosure manages this constraint by loading information only as needed. The engineering discipline is curating the smallest high-signal token set that achieves desired outcomes.

Context as Finite Resource Context must be treated as a finite resource with diminishing marginal returns. Like humans with limited working memory, language models have an attention budget drawn on when parsing large volumes of context. Every new token introduced depletes this budget by some amount.

原文を見る