Context Engineering Fundamentals
Establishes a foundational framework for managing AI context windows to maximize model performance and optimize token efficiency.
This skill provides deep domain expertise in context engineering, teaching users how to curate high-signal inputs for large language models. It covers the anatomy of context—including system prompts, tool definitions, and retrieval-augmented generation—while emphasizing the 'attention budget' constraint. By applying principles like progressive disclosure and structural clarity using XML or Markdown, developers can build more reliable agentic systems that maintain reasoning precision even as conversation history grows and complexity increases.
Características Principales
01Explains attention budget mechanics and the impact of n² token relationships
02Defines the core anatomy of AI context including system prompts and tool outputs
03Implements the Progressive Disclosure principle for just-in-time data loading
04Offers guidance on structural tagging for improved prompt hierarchy and clarity
0537,718 GitHub stars
06Provides strategies for high-signal token curation over exhaustive input
Casos de Uso
01Onboarding development teams to advanced context-aware prompt engineering practices
02Designing and optimizing complex AI agent architectures and tool-calling systems
03Debugging unexpected model behavior or hallucinations related to context overflow