context-fundamentalslisted

# Context Engineering Fundamentals Context is the complete state available to a language model at inference time: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Context engineering is the discipline of curating the smallest high-signal token set that maximizes the likelihood of desired outcomes. This skill does not own operational work: debugging attention failures belongs to `context-degradation`, and token-efficiency tactics belong to `context-optimization`. ## When to Activate When the work is conceptual: - Explaining what context is and how attention mechanics constrain agent behavior. - Onboarding new contributors who need the mental models before diving into operational skills. - Reasoning about a context-related design decision from first principles (what does this constraint mean, why does this trade-off exist) before picking a specific tactic. - Writing or reviewing documentation that needs to ground operational guidance in the underlying mechanics. Do not activate this skill for operational work. The specialized skills handle the doing: - Diagnosing lost-in-middle, context poisoning, or attention failures: `context-degradation`. - Reducing token cost via masking, partitioning, prefix caching, budgets: `context-optimization`. ## Core Concepts Treat context as a finite attention budget, not a storage bin. Every token added competes for the model's attention and depletes a budget that cannot be refilled mid-infer