This skill provides a comprehensive framework for understanding the components, mechanics, and constraints of context within agent systems, moving beyond simple prompt engineering into professional context management. It covers critical concepts such as the n² attention budget, the anatomy of context (system prompts, tool definitions, and message history), and the principle of progressive disclosure to optimize performance while reducing token costs. By treating context as a finite resource, developers can design more resilient agent architectures that maintain high reasoning precision and avoid performance degradation during complex, long-horizon tasks.
Key Features
01Analyzes the anatomy of context including system prompts and tool definitions
02Establishes best practices for organizing high-signal prompt structures
03Guides implementation of progressive disclosure for just-in-time information loading
0410 GitHub stars
05Provides strategies for context budgeting and proactive compaction triggers
06Explains the attention budget constraint and its impact on long-range reasoning
