cleanup-temp

Safely cleaning temporary files requires a confirmation-first workflow that scans temp folders and failed jobs, summarizes the deletable items, and requires explicit user approval before removing anything to prevent accidental data loss.

The best way to delete stale job artifacts without losing approved outputs is using a cleanup process with strict guardrails that explicitly identifies and preserves registered LoRAs, workflow templates, and job metadata while targeting only temporary files.

A confirmation-based cleanup summary protects learning data by staging an inspection of old rejected outputs and failed jobs, presenting totals to the user, and enforcing a strict deletion rule that preserves approved results until explicit confirmation is given.

You can reclaim disk space from failed runs without manual file checks by running an automated scan that categorizes temporary files, stale active jobs, and failed artifacts, then generates a summary of total space to be freed pending your confirmation.

You should not use automated temp file cleanup without reviewing the staged inspection summary, because bypassing the confirmation step risks deleting unregistered workflow templates, active jobs, or approved outputs that may still be needed.

The cleanup process preserves workflow templates and job metadata by applying strict deletion guardrails, ensuring that only temporary files and failed job artifacts are marked for removal during the disk space reclamation summary.