Context Optimization Expert

About

This skill provides advanced context engineering strategies designed to extend the effective capacity of limited context windows in production agent systems. By implementing techniques such as compaction through summarization, observation masking for verbose tool outputs, KV-cache optimization, and context partitioning across sub-agents, it helps developers build more complex, lower-latency, and cost-effective AI applications. It is particularly useful for long-running agent trajectories, large document processing, and multi-agent architectures where managing token usage and maintaining a high signal-to-noise ratio is critical for performance.

Key Features

• Context budget management and trigger-based optimization
• Observation masking for verbose tool outputs
• 5,499 GitHub stars
• KV-cache optimization for improved latency
• Context compaction and high-fidelity summarization
• Strategic context partitioning for multi-agent systems

Use Cases

• Reducing token consumption and costs in production LLM applications
• Extending effective conversation length for long-running AI agents
• Improving response quality by reducing noise in dense context windows