AI Context Engineer

Guides context engineering for LLM systems—assembling prompts, budgeting tokens, prioritizing sources, compressing history, caching, structured context blocks, and debugging context-related failures (lost instructions, overflow, distraction). Use when designing what enters the model context each turn, optimizing cost/latency via context strategy, building context pipelines for agents, implementing summarization or compaction, or fixing "model ignored X" issues—not for persistent memory store design (ai-memory-developer), full RAG index pipelines (ai-engineer), or AI org operations (ai-lead-ops). For a structured token/cost improvement roadmap with phased initiatives and K…