pi-planning-with-files

Maintain planning memory for long-running tasks by storing goals, progress, and discoveries in persistent Markdown files like task_plan.md, findings.md, and progress.md. This preserves context across sessions, reduces rework, and ensures continuity for multi-phase software projects.

The best way to track multi-phase task progress in Markdown files is to use a template-driven workflow that initializes task_plan.md, findings.md, and progress.md. This enforces a disciplined pattern to log errors, track phase completion, and store project discoveries.

Recover task context after a session break or context reset by running a dedicated session-catchup.py script. This disaster recovery utility reads your persistent planning files to restore your exact task state, log errors, and resume multi-step workflows without losing progress.

No, this Markdown planning workflow requires zero external dependencies. It relies entirely on standard Markdown files and bundled scripts, making it easy to initialize persistent planning memory in any project directory without installing additional packages.

Yes, you can use these Markdown planning templates for both research tasks and software projects. The persistent files adapt to any long-running workflow by storing task plans, logging findings, and tracking phase completion across multiple complex phases.

Context decays between sessions in multi-step planning workflows because temporary session memory is lost when work pauses. Storing goals and progress in persistent Markdown files solves this by providing a durable memory layer that survives context resets and breaks.