context-optimization

SKILL.md

Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity. Effective optimization can double or triple effective context capacity without requiring larger models or longer contexts.

Context optimization extends effective capacity through four primary strategies: compaction (summarizing context near limits), observation masking (replacing verbose outputs with references), KV-cache optimization (reusing cached computations), and context partitioning (splitting work across isolated contexts).

The key insight is that context quality matters more than quantity. Optimization preserves signal while reducing noise. The art lies in selecting what to keep versus what to discard, and when to apply each technique.

효과적인 컨텍스트 용량을 확장하기 위해 최적화 기술을 적용합니다. 컨텍스트 제한으로 인해 에이전트 성능이 제한되는 경우, 비용 또는 대기 시간을 최적화하는 경우 또는 장기 실행 에이전트 시스템을 구현할 때 사용합니다. 출처: shipshitdev/library.

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