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From prompt-architecture
Designs LLM context windows: allocates token budgets, orders information for attention, selects relevant data, and applies RAG/summarization strategies.
How this skill is triggered — by the user, by Claude, or both
Slash command
/prompt-architecture:context-engineeringThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
The context window is finite. What goes into it — and in what order — determines the quality of every output. Context engineering is the practice of deliberately designing the information architecture of the context window.
The context window is finite. What goes into it — and in what order — determines the quality of every output. Context engineering is the practice of deliberately designing the information architecture of the context window.
Every context window has a token budget. Allocate it deliberately:
Order matters. The model pays different amounts of attention to different positions:
Not everything should go into the context. Design selection criteria:
How to tell if your context engineering is working:
npx claudepluginhub owl-listener/ai-design-skills --plugin prompt-architectureExplains context engineering fundamentals: attention mechanics, U-shaped curve, context anatomy, and quality-versus-quantity tradeoffs. For conceptual understanding and onboarding.
Explains LLM context fundamentals: system prompts, tool definitions, anatomy, and progressive disclosure. For designing agents, debugging behavior, optimizing tokens, and onboarding.
Diagnoses context stuffing vs. context engineering in AI workflows. Helps fix bloated, brittle, or unreliable AI outputs by designing better context structure.