SKILL.md
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 Use
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
Core Concepts
Context compression trades token savings against information loss. Three approaches to evaluate for the current workload are:
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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 encourages preservation by dedicating sections to specific information types; omissions still require explicit checks.
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Opaque Compression: Produce compressed representations optimized for reconstruction fidelity. May reduce text size substantially but sacrifices interpretability; no general reconstruction or compression ratio guarantee is established here.