What problem does it solve?
This Skill helps clean up an AI project workspace after work accumulates across multiple change artifacts, session records, and legacy folders. It reduces context bloat, preserves useful summaries, and prevents completed work from making future sessions slower and harder to manage.
Core Features & Use Cases
- Artifact Inventory and Cleanup Planning: Scans workspace artifacts, estimates token footprint, and classifies items such as active work, completed changes, unindexed directories, and legacy patterns.
- Safe Archiving with Confirmation: Summarizes completed changes into a summary file, archives old artifact directories, truncates stale history, and always asks before destructive actions.
- Workflow Integrity Checks: Warns when completed changes have not yet been synced, protects active changes from accidental cleanup, and handles epic-child archive relationships carefully.
- Use Case: After several completed features and reviews have piled up in the shared workspace, use this Skill to reclaim tokens, archive finished change records, and keep only the most relevant active context available.
Quick Start
Ask the AI to run mvt-cleanup to review workspace artifacts, show a cleanup plan, and archive completed changes after confirmation.