The new informational Windows job surfaced 91 failures: four real bugs (fixed in
the previous two commits) and a long tail of POSIX assumptions in the tests
themselves. None of them assert behaviour that differs on Windows, so they are
fixed rather than skipped wherever the behaviour is the same:
* Path spelling: expectations built with `str(tmp_path)` are compared through an
identity helper (`normalize_root_identity`) or the platform's separator, since
the code hands out forward-slashed business paths while `str()` is native.
* Root labels: `_root_display_labels` prefixes the drive on Windows ("C: loras"),
so label assertions compare the final path component or derive the expected
label from the same helper.
* `~` expansion reads USERPROFILE (ntpath), not HOME; timer granularity rounds
0.1s down; a case-only rename counts as "already named" via `os.path.normcase`.
* Genuinely POSIX-only strategies are skipped with a reason instead of failing:
the headless clipboard fallback, dbus file-manager selection, xdg-open, and
the case-fold index test that simulates a case-sensitive filesystem.
The post-processor stored the LLM's confidence as `_llm_confidence`, but
that value could never be read back: `BaseModelMetadata.from_dict()`
deliberately excludes underscore-prefixed keys from `_unknown_fields` and
`to_dict()` strips private fields, so it was erased by the next metadata
write and was invisible to `read_metadata()`. The enrichment evaluation
harness reads this field to score runs, so confidence was always scored
as blank.
Store it as `llm_confidence`, which round-trips as an ordinary unknown
field — the same mechanism `llm_enriched_at` already relies on. Nothing
else consumed the old name, and the harness still accepts it so sidecars
written by earlier versions keep evaluating.
Covered by a metadata load/save round-trip regression test plus
assertions that the post-processor writes the persisted key and no longer
writes the private one.
Refresh after manual .metadata.json deletion rebuilds the payload without
file_name/size/modified, which are required by BaseModelMetadata.from_dict.
The recreated sidecar then fails to parse and the scanner skips the model.
- load_metadata_payload fills missing file facts from os.stat
- hydrate_model_data restores every missing key from the cache snapshot
only when the sidecar is missing entirely (disk stays authoritative
otherwise), preferring the cached import timestamp for modified
- save_metadata fills file facts on write so no write path can produce
an unparseable sidecar
Will Miao