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 is the conceptual foundation that every other skill in the collection builds on. It explains what context is, how attention mechanics work, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. It does not own operational work: debugging attention failures belongs to context-degradation, and token-efficiency tactics belong to context-optimization.

When to Activate

Activate this skill 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.