context-engineeringlisted

# Context Engineering ## Coverage - Core principle: the model is a reasoning engine that reasons over whatever is in its context window — wrong context produces correct reasoning over false premises - The five-layer context stack: system prompt, persistent memory, always-loaded rules, injected skills, agent prompt — what each layer does and how each can fail - The four context failure modes: missing, stale, wrong, overwhelming — diagnostic questions for each, table of symptoms, and prevention strategies - Four context quality metrics: injection precision, injection recall, context utilization, freshness score — definitions, healthy ranges, and how to measure each - Context-compilation levers: selection, structuring, sequencing, compaction, memory integration, retrieval, provenance, and tool-result clearing - Frequent Intentional Compaction (FIC): proactive compaction at task boundaries, target utilization range, and the difference between planned and forced compaction - Subagent delegation pattern: when to delegate context-heavy investigation to a subagent so the main agent receives a summary instead of raw evidence - Debugging decision tree: how to diagnose any agent failure by walking from missing-context through overwhelming-context before blaming the model - The verification checklist: gates a context-engineering review must pass before declaring the pipeline healthy ## Philosophy The model is a reasoning engine that reasons over whatever is in its context window. If