Provides strategies for designing AI products around context window limits, token budgets, memory persistence, and conversation UX flows.
How this skill is triggered — by the user, by Claude, or both
Slash command
/model-interaction-design:context-window-designThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Every AI model has a finite context window. Designing within this constraint — and designing the user experience around it — is a core skill for AI product design.
Every AI model has a finite context window. Designing within this constraint — and designing the user experience around it — is a core skill for AI product design.
The context window is not just a technical limitation. It's a design material:
Users expect AI to remember. Design for different memory horizons:
npx claudepluginhub owl-listener/ai-design-skills --plugin model-interaction-designDesigns LLM context windows: allocates token budgets, orders information for attention, selects relevant data, and applies RAG/summarization strategies.
Explains context engineering fundamentals: attention mechanics, U-shaped curve, context anatomy, and quality-versus-quantity tradeoffs. For conceptual understanding and onboarding.
Explains the components of LLM context (system prompts, tool definitions, message history) and principles like attention budgets and progressive disclosure. Useful when designing agent systems, debugging unexpected behavior, or reducing token costs.