context-engineering-collectionlisted

# Agent Skills for Context Engineering This collection provides structured guidance for building production-grade AI agent systems through effective context engineering. ## When to Activate Activate these skills when: - Building new agent systems from scratch - Optimizing existing agent performance - Debugging context-related failures - Designing multi-agent architectures - Creating or evaluating tools for agents - Implementing memory and persistence layers ## Skill Map ### Foundational Context Engineering **Understanding Context Fundamentals** Context is not just prompt text—it is the complete state available to the language model at inference time, including system instructions, tool definitions, retrieved documents, message history, and tool outputs. Effective context engineering means understanding what information truly matters for the task at hand and curating that information for maximum signal-to-noise ratio. **Recognizing Context Degradation** Language models exhibit predictable degradation patterns as context grows: the "lost-in-middle" phenomenon where information in the center of context receives less attention; U-shaped attention curves that prioritize beginning and end; context poisoning when errors compound; and context distraction when irrelevant information overwhelms relevant content. ### Architectural Patterns **Multi-Agent Coordination** Production multi-agent systems converge on three dominant patterns: supervisor/orchestrator architectures with