wrapup

You can automatically capture end-of-session summaries by invoking a command that records facts, decisions, and lessons learned into a structured JSONL format. This creates easily searchable records while reducing manual note-taking effort.

Storing session wrap-ups in a git-friendly JSONL format enables structured logging and cross-project aggregation. Each line represents a discrete record, making version control diffs clean and programmatic data extraction straightforward.

Yes, you can track optional session timing and AI/user evaluation data across multiple projects. The structured JSONL logs support cross-project aggregation, allowing you to analyze session metrics and learning progress comprehensively.

Session wrap-up logging supports multilingual content alongside optional auto-memory and todo integrations. This allows you to capture lessons and summaries in your preferred language while automatically syncing actionable items.

A session wrap-up for AI learning evaluation includes a two-layer structure: a session summary capturing facts, Q&A, decisions, and actions, plus a lesson-learned layer detailing both user and AI learning outcomes.

You export session notes, decisions with rationale, and work done by triggering the end-of-session command. This automatically structures the data into JSONL format specifically designed for Claude Code consumption and cross-project logging.