pi-planning-with-files

You can maintain task planning context across sessions by persisting complex planning data to disk using markdown files like task_plan.md, findings.md, and progress.md. This external memory approach prevents context loss during tool calls and session breaks.

File-based planning stores task context, findings, and progress in persistent markdown files. You need it for long-running multi-phase tasks where session recovery and context refresh are required after breaks to prevent data loss.

Set up persistent markdown templates by initializing task_plan.md, findings.md, and progress.md in your project root. These templates manage task context and support automatic session recovery for coordinating multi-step workflows with AI agents.

Yes, markdown planning files support automatic session recovery and context refresh after breaks. By persisting complex task planning data to disk, the AI agent can reload progress.md and findings.md to resume long-running multi-step tasks seamlessly.

The best way to coordinate multi-step research tasks is using file-based planning with persistent templates. Storing context in task_plan.md and findings.md provides external memory, ensuring the AI agent maintains task state across tool calls and sessions.

Yes, the file-based planning approach requires scripts to manage the persistent markdown files. These scripts handle the creation, updates, and session recovery logic for task_plan.md, findings.md, and progress.md to maintain external memory.