session-continuity

To preserve AI agent context across context compactions, you can use state persistence to save your current working state, active task, and progress to a SESSION.md file. This allows reliable restoration of your exact working environment when the session continues.

The best way to resume an interrupted workflow is through context restoration protocols that recover session state by reading saved files and checking git status. This ensures your AI assistant picks up exactly where it left off, remembering edited files and next steps.

You save and restore session state for multi-session work by saving current state to memory and feature specifications to disk. This manages detailed specifications for multi-session features in .md files, enabling reliable task resumption across new sessions.

You need state checkpointing for task resumption when your AI assistant is interrupted mid-task, ensuring it can pick up exactly where it left off. It is required for AI agent workflows demanding persistent context and feature tracking across interruptions.

Yes, session state persistence works with git status for context recovery by checking the repository state alongside saved SESSION.md files. This combination allows the context restoration mechanism to accurately reconstruct your active task and progress.

Yes, you can manage multi-session feature specifications using markdown files. The system stores detailed specifications in .md files on disk, allowing you to track complex multi-session features and maintain context continuity across different sessions.