Context Optimization

What it does

This skill, Context Optimization, helps AI agents manage and refine their context to improve response accuracy and relevance while maximizing the amount of information they can process. It achieves this through techniques like compaction (summarization), observation masking, KV-cache optimization, and context partitioning. These strategies can effectively increase usable context capacity by 2-3x without needing larger language models. The skill focuses on intelligently reducing token usage within the agent's context window.

When to use it

• When approaching context limits: If your AI agent is consistently hitting its token limit, this skill can help extend its effective capacity.
• With workloads dominated by tool outputs: Observation masking is particularly useful when tools are generating verbose responses that consume a large portion of the available tokens.
• For complex multi-task scenarios: Context partitioning allows breaking down large tasks into smaller, more manageable sub-tasks with isolated contexts.
• When experiencing quality degradation: If response quality declines due to context overload, this skill can help identify and address the root cause.

Key capabilities

• Compaction: Summarizes message history, tool outputs, and retrieved documents while prioritizing key information.
• Observation Masking: Replaces lengthy tool outputs with references and summaries.
• KV-Cache Optimization: Caches frequently used context elements (system prompts, tool definitions) for faster retrieval.
• Context Partitioning: Splits complex tasks into sub-agents each with their own focused context.
• Budget Management: Allows setting token budgets for different context components to control usage and trigger optimization strategies.

Example prompts

• "Agent, you're approaching your context limit. Please optimize the current conversation."
• "Agent, summarize the key findings from the tool outputs in the last turn."
• "Agent, can you partition this research task into smaller sub-tasks?"

Tips & gotchas

• Prioritize stability: When using KV-Cache Optimization, ensure consistent formatting and stable structure across sessions to maintain cache effectiveness.
• Avoid compressing system prompts: The system prompt should never be compressed as it contains critical instructions.
• Monitor utilization: Pay attention to context token usage; start monitoring around 70% and apply compaction strategies when exceeding 80%.