milestone-runner

Multi-phase AI development workflows can be tracked by recording execution state in a markdown file, allowing interrupted runs to resume from the correct checkpoint. This approach validates existing files before continuing to ensure progress is maintained.

Enforcing user approval checkpoints during milestone planning requires a controlled handoff workflow that guides users to wait for approval before moving to the next phase. This prevents skipping reviews across content design and balance tuning steps.

Yes, roadmap milestone execution supports resume mode by validating existing generated artifacts against planned outputs. The state tracking mechanism maps files to planned outputs and checks them before continuing from the correct checkpoint.

Yes, milestone execution supports optional phase skips for content design, balance tuning, data generation, and specification writing. The workflow coordinates these phases while maintaining a markdown state file to track which steps are skipped or completed.

Generated artifacts are mapped to planned outputs by coordinating milestone execution state across all phases. The workflow maintains a markdown state file that tracks file generation, enforces approval checkpoints, and validates outputs before phase transitions.

Markdown state files for workflow orchestration rely on manual checkpoint validation and user approval gates, which means they cannot auto-recover from corrupted states or bypass required approval steps without explicit user interaction during milestone execution.