planning-with-files

To preserve context for complex AI tasks, you can organize multi-step work into persistent markdown files like task_plan.md, findings.md, and progress.md. This file-based memory approach prevents lost context and aimless execution by keeping goals, decisions, and findings on disk.

The best way to recover context after a session clear is using context management hooks that detect prior-session unsynced changes. The system prompts you to reconcile previous findings with git diff and existing planning files to restore your working state.

You can manage concurrent research workflows by creating isolated file-based planning directories under .planning/<plan-id>/. This ensures separate concurrent workflows maintain their own task plans, findings, and progress logs without cross-contamination.

You can defend against prompt injection by enabling optional SHA-256 plan attestation. This treats plan contents as structured data rather than instructions, blocking plan injection and warning you if content tampering is detected.

Yes, file-based task tracking is designed specifically for iterative feature development and long-running execution workflows. It uses operational guardrails to read the active plan before decisions and enforces updates to progress files as work advances.