strategic-compact

To prevent losing context during AI context window compaction, trigger manual compaction at logical task boundaries like finishing research or planning. This preserves vital project state and instructions while clearing stale exploration data instead of cutting off mid-task reasoning.

You should manually compact the context window at natural task milestones, such as after finalizing an implementation plan but before writing code. This avoids arbitrary auto-compaction that disrupts multi-phase workflows and drops critical reasoning.

Shell-based tool call tracking triggers context window optimization reminders by monitoring development session activity against configurable thresholds. Once a session threshold is met, it suggests a manual cleanup to manage token efficiency.

Yes, you can use logical boundary compaction for multi-phase workflows. It targets long-running development sessions by suggesting context cleanup after completing research or feature phases, ensuring vital instructions are maintained before proceeding.

Relying on arbitrary intervals for context window management causes mid-task auto-compaction, which leads to the loss of critical reasoning and project state. It disrupts complex development sessions by clearing context before a logical milestone is reached.