agilab-pypi-release-maintenance

PyPI release cleanup removes explicitly identified stale versions while preserving the current protected release. You generate a dry-run cleanup plan first, inspect the targeted packages, then confirm deletion to ensure only intended releases are affected.

Retention pruning is the process of identifying and removing obsolete PyPI releases to reduce clutter and maintenance toil. You need it during post-release audits or after noisy releases to simplify provenance and reduce the package surface area.

Yes, you can dry-run PyPI release deletion to inspect targeted packages without deleting any artifacts. The dry-run mode generates a cleanup plan showing exactly which stale versions will be removed while the current protected release remains untouched.

Safe PyPI deletion requires explicit confirmation, credential handling guidance, and precise version matching. The process applies safety checks to ensure only explicitly identified stale versions are affected, preventing accidental deletions of protected releases.

Automated PyPI release cleanup is limited to precise version matching and requires explicit confirmation before deleting artifacts. It targets specific packages like agilab and agi-core, and enforces safety checks to prevent unintended removal of current protected releases.