context-compression

Context compression reduces token usage by summarizing conversation history into structured sections while preserving essential decisions, file changes, and next steps. Apply anchored iterative summarization to merge new content incrementally, avoiding full regeneration and maintaining artifact trails across extended sessions.

Structured context compression maintains memory through persistent, sectioned summaries that capture intent, decisions, and file state. Incremental merging preserves continuity without re-reading full history, enabling seamless task resumption when conversation logs exceed available context.

Anchored iterative summarization balances compression fidelity by choosing between opaque high-compression or regenerative full-summary approaches. The skill extracts prioritized signals—decisions, changes, and next steps—while reducing payload, so you retain what matters for task continuity.

Trigger compression when context utilization approaches your context window limit. Merge the new summary with existing state to maintain a concise, actionable snapshot. This prevents token waste and memory loss in debugging, analysis, or multi-turn problem-solving scenarios.

Incremental merging appends new conversation content to existing sectioned summaries instead of regenerating them entirely. This approach preserves decision trails and reduces computational cost while maintaining fidelity, making it efficient for sustained agent sessions.