Context Optimization

Context Optimization is a strategic skill for extending effective context capacity through smart techniques rather than requiring larger models. The goal is not to magically increase context windows but to make better use of available capacity—potentially doubling or tripling efficiency through proven optimization strategies.

This skill covers four core optimization strategies that can dramatically improve agent performance. Compaction involves summarizing context when approaching limits, then reinitializing with distilled summaries. This preserves essential information while reducing token consumption by 50-70%. Observation Masking addresses the fact that tool outputs can consume 80%+ of token usage in agent trajectories. This technique replaces verbose outputs with compact references, achieving 60-80% reduction while keeping information retrievable. KV-Cache Optimization reuses cached Key-Value tensors across requests sharing identical prefixes, particularly beneficial for system prompts and stable content across multiple interactions. Context Partitioning distributes work across isolated sub-agents, each operating in clean contexts focused on specific subtasks without accumulated overhead.

Key principles guide effective optimization: prioritize quality over quantity by preserving signal while eliminating noise, monitor triggers at 70%+ utilization to activate optimization proactively, balance token savings against quality preservation, test optimization strategies at production scale, and combine techniques based on what dominates your context usage.

Deploy this skill when context limits constrain task complexity, when reducing operational costs is a priority (fewer tokens = lower expenses), when minimizing latency in extended conversations matters, when supporting long-running agent systems, when processing large documents or conversation histories, or when building production-scale systems. Essential knowledge for anyone seeking to maximize the value extracted from limited context windows.