context-fundamentals

Context engineering manages system prompts, tool definitions, retrieved documents, and message history to optimize token budgets. It ensures reliable agent architectures by controlling what information the AI processes during multi-turn conversations.

Optimize token budgets by applying progressive disclosure to load content on demand. This approach manages context windows by strategically retrieving documents and tools only when needed, reducing unnecessary token consumption during real-time execution.

Debug context issues by examining the core components: system prompts, tool definitions, retrieved documents, and message history. Outline the scenario to identify where attention budgets fail and apply progressive disclosure to isolate problematic context loading.

Implement progressive disclosure by loading content on demand within file-system-based workflows. Structure agent architectures to retrieve documents and tool definitions dynamically, managing context windows by exposing information only when required for the task.

Yes, context budgeting applies directly to file-system-based workflows. The skill provides practical guidance for implementing progressive disclosure and on-demand loading, allowing you to manage token usage effectively across system prompts and retrieved documents.