Context Engineering Fundamentals

소개

This skill serves as a comprehensive guide for developers building AI agents, focusing on the critical discipline of context engineering. It teaches how to manage the finite 'attention budget' of language models by using techniques like progressive disclosure, selective tool output retention, and structured system prompting. Whether you are debugging unexpected agent behavior or optimizing for token cost and performance, this skill helps you curate high-signal inputs that maximize model reliability and efficiency across complex, long-horizon tasks.

주요 기능

• Context anatomy mapping for system prompts, tool definitions, and message history
• Practical strategies for file-system-based context access and observation masking
• Attention budget management to prevent model performance degradation
• Structured prompting guidelines using XML tagging and Markdown headers
• Progressive disclosure patterns to load information only as needed
• 7,140 GitHub stars

사용 사례

• Designing high-performance multi-agent architectures with efficient memory management
• Optimizing production token usage to reduce inference costs and latency
• Debugging agent hallucinations or reasoning errors caused by context saturation