Context Optimization

What it does

This skill, Context Optimization, helps AI agents make better use of limited context windows. It achieves this through techniques like summarizing information ("compaction"), replacing verbose outputs with references ("observation masking"), reusing cached computations, and splitting work across isolated contexts. The goal is to improve response accuracy and efficiency without needing larger models or longer context lengths – effectively doubling or tripling the usable context capacity.

When to use it

• When complex tasks are limited by context size.
• To reduce costs associated with token usage.
• For long conversations where latency needs to be minimized.
• In production systems requiring handling of large documents or extensive conversation histories.

Key capabilities

• Compaction: Summarizes content when approaching context limits, preserving essential information.
• Observation Masking: Replaces verbose outputs with references to reduce token usage.
• KV-Cache Optimization: Reuses cached computations for efficiency.
• Context Partitioning: Splits work across isolated contexts.

Example prompts

• "Optimize the context window before responding to this user query."
• "Summarize the previous conversation turns and continue from there."
• "Apply compaction strategies to reduce token usage in this interaction."

Tips & gotchas

• Context quality is prioritized over quantity – the skill focuses on preserving important information.
• Compaction typically serves as the first optimization step.
• The skill aims to improve existing context capacity, not magically increase it.