session-checkpoint

You can save AI session state by writing a structured checkpoint to a local `.ai-session/STATE.md` file, ensuring task tracking continuity without polluting git history. This local memory file captures high-signal context for seamless session handoffs.

A task checkpoint is a structured markdown file capturing session state like completed work and next actions. You should create a checkpoint during pause points, before long-running operations, or at session end to ensure seamless handoffs.

To resume an AI automation task, read the deterministic markdown checkpoint stored in local memory and execute the defined NEXT ACTION. This format captures high-signal context like decisions and current blockers for seamless continuation.

Yes, the session memory checkpoint works by writing to a local markdown file at `.ai-session/STATE.md`. It uses a deterministic format with specific sections like TASK, DECISIONS, and NEXT ACTION to store context without requiring external databases.

The best way to track workflow context is generating a structured checkpoint at pause points and persisting it locally in a deterministic markdown format. This approach stores high-signal state like files, key variables, and next actions for resumption.

A structured checkpoint ensures deterministic formatting with specific sections like CURRENT BLOCKER and NEXT ACTION, preventing context loss. Unlike manual logging, it captures high-signal session state automatically at critical pause points for reliable handoffs.