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.

最適化手法を適用して、有効なコンテキスト容量を拡張します。コンテキスト制限によってエージェントのパフォーマンスが制約される場合、コストや遅延を最適化する場合、または長時間実行されるエージェント システムを実装する場合に使用します。 ソース: shipshitdev/library。

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