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--- name: dataset_maintenance description: Diagnose and maintain dataset Qdrant indexes and parsed caches. --- # Dataset Maintenance This administrative skill owns destructive and diagnostic operations for dataset indexes. Business skills must not contain collection pruning, storage-layout migration, or repair logic. ## Usage ```bash python -m skills.dataset_maintenance diagnose python -m skills.dataset_maintenance prune python -m skills.dataset_maintenance prune --apply python -m skills.dataset_maintenance delete --dataset avientus python -m skills.dataset_maintenance activate --dataset avientus python -m skills.dataset_maintenance archive --dataset avientus python -m skills.dataset_maintenance create "Example Startup" python -m skills.dataset_maintenance delete --embeddings nomic-embed-text python -m skills.dataset_maintenance from-insight --insight person_profile python -m skills.dataset_maintenance from-insight --insight investor_profile --source-dataset sictic-members python -m skills.dataset_maintenance migrate-startup-dossiers python -m skills.dataset_maintenance migrate-insight-manifests python -m skills.dataset_maintenance migrate-insight-manifests --apply ``` `prune` is dry-run by default. Pass `--apply` to delete orphaned collections. `create` initializes a startup dossier under the configured storage layout. It creates raw and parsed startup dataset folders with `data-room`, `linkedin`, `dealum`, `snippets`, and `post-deal` subfolders, then marks the startup active. The startup-dossier migration is also dry-run by default and writes a JSON manifest before any optional `--apply`.