Context Compression Strategies

When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request. The correct optimization target is tokens per task: total tokens consumed to complete a task, including re-fetching costs when compression loses critical information.

When to Activate

Activate this skill when:

• Agent sessions exceed context window limits
• Designing conversation summarization strategies
• Evaluating different compression approaches for production systems
• Debugging cases where agents "forget" what files they modified
• Building evaluation frameworks for compression quality
• Optimizing long-running coding or debugging sessions

Core Concepts

Context compression trades token savings against information loss. Three production-ready approaches exist:

1. Anchored Iterative Summarization: Maintain structured, persistent summaries with explicit sections for session intent, file modifications, decisions, and next steps. When compression triggers, summarize only the newly-truncated span and merge with the existing summary. Structure forces preservation by dedicating sections to specific information types.