context-compressionlisted

# 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 - Codebases exceed context windows (5M+ token systems) - Designing conversation summarization strategies - Debugging cases where agents "forget" what files they modified - Building evaluation frameworks for compression quality - Creating durable handoff summaries that preserve decisions, files, risks, and next actions Do not activate this skill for adjacent work owned by other skills: - General token-efficiency tactics such as masking, prefix caching, or partitioning: `context-optimization`. - Diagnosing why a long context is failing before choosing a mitigation: `context-degradation`. - Writing raw outputs, logs, or plans to files without summarizing them: `filesystem-context`. - Designing long-term semantic memory across sessions: `memory-systems`. ## Core Concepts Context compression trades token savings against information loss. Select from three production-ready approaches based on session characteristics: 1. **Anchored Iterative Summarization**: Implement this for long-running sessions where file tr