ck:context-engineering

Optimize context usage by implementing strategies like context compaction, masking, caching, and partitioning to minimize token consumption and prevent exceeding LLM token limits. This ensures efficient reasoning without overstepping window boundaries.

Debug context failures such as lost-in-middle or context poisoning by using context monitoring tools to identify issues in context management and trace root causes within your LLM agent architecture.

Yes, this context engineering approach supports building multi-agent coordination systems by providing tools to manage isolated contexts, ensuring each agent operates within its own optimized context window.

The best way to monitor context window limits in Claude Code is to activate context monitoring tools that track usage limits, monitor time remaining, and optimize token consumption dynamically during LLM tasks.

Evaluate agent performance and context optimization strategies using built-in evaluation tools designed to test how well your agents manage context and maintain reasoning quality under token limitations.

Use context partitioning when isolating distinct data segments across multi-agent systems, whereas context compaction is better suited for compressing continuous context to reduce token usage within a single LLM session.