Context Optimization

What it does

This skill, Context Optimization, improves the performance of AI agents working within limited context windows. It achieves this by strategically compressing, masking, caching, and partitioning conversation history. Instead of increasing context window size, it focuses on making better use of existing capacity, potentially doubling or tripling effective context length without requiring larger models.

When to use it

• When context limits restrict the complexity of tasks you can assign to the agent.
• To reduce operational costs by minimizing token usage.
• For long-running agent systems where latency is a concern.
• When handling large documents or extended conversations.
• When building production AI agent systems at scale.

Key capabilities

• Compaction: Summarizes context contents when approaching limits, distilling information while preserving key points. Prioritizes summarizing tool outputs, old conversation turns, and retrieved documents (but never the system prompt).
• Observation Masking: Replaces verbose tool outputs with compact references to reduce token usage.
• KV-cache Optimization: Reuses cached computations for efficiency.
• Context Partitioning: Splits work across isolated contexts.

Example prompts

While this skill operates automatically, here are examples of situations where it would be beneficial:

• "Analyze this 10,000-word document and provide a summary." (Handles large documents)
• "Continue our conversation about project X; we've been discussing it for over an hour." (Reduces latency in long conversations)
• "Perform these tasks sequentially, keeping track of the results." (Supports long-running agent systems)

Tips & gotchas

• Context Optimization prioritizes preserving signal while reducing noise. The skill automatically determines which information to keep and discard based on message type.
• The primary goal is not to increase context window size but to improve how existing capacity is used.