WorkBuddy Workspace Cleanup
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Please follow https://skillhub.cn/install/skillhub.md to install @user_f225d7a4/workbuddy-cleanup.
About this skill
Problem
Long-running WorkBuddy workspaces accumulate conversation folders, automation artifacts, browser profiles, caches, and generated files under platform-specific directories. Manual deletion is risky because important project files, memory notes, active sessions, or automation dependencies can be mixed into the same folder tree. This skill turns cleanup into a reviewable file-maintenance workflow instead of a blind delete operation.
How It Works
The core capabilities are workspace scanning, content-type classification, protected-folder checks, cleanup preview, recycle-bin execution, and cleanup history. Key steps include:
- Platform detection: configure WorkBuddy and recycle-bin paths for Windows, macOS, or Linux.
- Scan and classify: list folders by size, file count, and creation time, then classify them as Web, Python, Java, documentation, configuration, temporary files, or empty directories.
- Safe confirmation: show the exact folders, likely purpose, and impact before execution, while allowing cancellation or protected-list updates.
- Recycle-bin execution: move selected items to the system recycle bin rather than permanently deleting them, then record the operation in cleanup-history.md.
Boundaries
This is useful for empty folders, caches, and stale WorkBuddy outputs. It does not replace backups: important projects should be protected or explicitly verified before cleanup. Browser cache cleanup preserves login-state-related directories, but later automation tasks may run slightly slower on first startup. Files in the recycle bin remain recoverable until the user clears them manually.
Use Cases
- Review accumulated empty folders and temp files in a WorkBuddy directory, preview the scan, and move selected items to the recycle bin.
- Clear browser caches and regenerable artifacts such as node_modules monthly while preserving login-state directories.
- Tidy conversation workspaces after a project ends, add obsolete outputs to the cleanup list, and record the action in cleanup history.
- Protect key project folders in MEMORY.md so source files, docs, and automation tasks are not accidentally removed.
Best For
- AI-assistant maintainers who run WorkBuddy workspaces long-term and need periodic disk-space cleanup.
- Engineers managing local automation task folders who need cache cleanup without deleting running configuration.
- Operations staff maintaining shared WorkBuddy environments who need auditable cleanup records.
- Web, Python, or documentation project engineers who need to assess folder risk before deletion.
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