session-reset

To preserve AI context during a session reset, compact accumulated session knowledge into a bounded, structured context block with a 6-section architecture. This prevents context loss and enables immediate future AI session resumption without re-explaining project state.

Context compaction is the process of compressing accumulated knowledge from extended AI conversations into bounded, structured markdown context blocks. It applies a 5-level compression protocol to ensure context blocks remain parseable and optimized for future sessions.

You can extract reusable architectural knowledge by pulling decisions, patterns, and constraints from long-running sessions into persistent memory files. These files use cross-session reference syntax to avoid re-deriving the same insights across multiple projects.

Yes, structured context blocks support time-tracking automation through a mandatory datetime-stamped activity log. This append-only log provides a full audit trail of work performed during the software development session.

Oversized context block compression is triggered by a 3693-line threshold. When this limit is reached, an impact-aware 5-level compression protocol is applied to preserve the most relevant information for future AI session resumption.

Yes, session-reset operates independently without external dependencies to compact AI development sessions. It enforces a mandatory 6-section context block architecture to ensure context blocks are parseable, auditable, and optimized for cross-session memory.