2 skills found
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project dev
This skill should be used when the user asks to optimize context, reduce token costs, improve context efficiency, implement KV-cache optimization, partition context, or mentions context limits, observation masking, context budgeting, or extending effective context capacity.
