context-guard

You preserve AI agent context across session boundaries by writing learned state to external safeguard files such as CLAUDE.md and SESSION_LOG.md, which survive crashes, rate limits, and context compaction for seamless recovery.

The best way to recover an AI coding session after a crash is to use command-driven lifecycle management with /start, /save, and /audit commands to validate external state files and resume the project seamlessly.

You create an audit trail for long-running AI coding projects by generating traceability artifacts like DECISIONS.md and TASK_REGISTRY.md, using the /audit and /itemise commands to manage validation and handoffs across different models.

Yes, external state persistence works across different AI models and runtimes because the safeguard files are stored externally, allowing context to survive model changes and enabling full recovery regardless of the underlying agent runtime.

You prevent context loss during AI agent rate limits or compaction by actively persisting learned context to external state files using the /save command, ensuring that session data is not lost when the context window is compacted or limited.

You initialize safeguard files for AI context recovery by running your agent and entering the /start command, which sets up the necessary external files to enable session-wide context protection and recovery.