ralph-loop

To manage implementation loops with git worktrees and Docker workers, you use Python scripts to set up jobs, monitor progress, and enforce stop conditions. This Skill orchestrates Ralph implementation loops by automating PRD batch execution and tracking progress through commits.

A Ralph implementation loop is a structured workflow for planning, executing, and reviewing PRD stories using reusable Docker workers and isolated git worktrees. You need it when executing a series of PRD stories that require automated job setup, progress tracking, and safe loop operations.

Yes, Python is required to run Ralph implementation loops. Python handles the loop management scripts that execute jobs, monitor progress, and enforce stop conditions within the reusable Docker workers and git worktrees.

You track progress of PRD batches in Docker workers by using dedicated progress files and git commits. The implementation loop monitors these commits to ensure tasks are completed properly and workflow steps are executed sequentially.

The best way to safely stop a Ralph implementation loop is by using the built-in stop conditions configured during job setup. The loop management scripts monitor these conditions to safely halt Docker workers and preserve git worktree states.

Yes, you can use built-in templates for job creation in implementation loops. These templates provide explicit operating instructions and standardized setups for Docker workers and git worktrees to streamline PRD story execution.