fix: 3 audit findings — truthy skip-reason bug, 500 match consistency, CHANGELOG note

1. if saved: → if isinstance(saved, tuple): so string skip-reasons from
   process_face_mode no longer register as successes and create phantom
   asset_map entries with no JPEG on disk. Dead reason/fallback code in
   the else branch now correctly handles str and None returns.

2. "could not process" permanent-rejection check now uses error_detail
   (json message field, falling back to body[:100]) instead of full_body,
   keeping the match consistent with what is displayed to the user.

3. CHANGELOG [Unreleased] breaking-change note for MAX_AUTO_IMAGES 20→5
   so upgrading users know to set the env var if they want the old cap.

CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Changed
+
+- **`MAX_AUTO_IMAGES` default lowered from 20 to 5** — existing users who have not set this variable and already have more than 5 winnow-managed images in Frigate will find themselves at cap on the next run. With `QUALITY_REPLACEMENT=true` (the default), winnow will attempt to swap weaker images rather than uploading new ones. Set `MAX_AUTO_IMAGES=20` to restore the previous behaviour.
+
 ## [0.6.5] - 2026-06-17
 
 ### Added

winnow/executor.py

@@ -186,12 +186,11 @@ def execute_jobs(jobs: list[dict]) -> None:
                             saved = process_face_mode(
                                 img, asset, person, person_dir, count, insightface_app=insightface_app
                             )
-                            if saved:
+                            if isinstance(saved, tuple):
                                 filename = f"{count}.jpg"
                                 asset_map[filename] = asset["id"]
                                 score_map[filename] = asset.get("quality_score")
-                                if isinstance(saved, tuple):
-                                    dims_map[filename] = saved
+                                dims_map[filename] = saved
                                 # Time-spread path: compute blur score from the downloaded
                                 # image. Capped at 1440px via blur_score_from_image() so the
                                 # scale matches the preview thumbnails the embedding path uses
@@ -574,7 +573,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                                 _is_permanent = (
                                     (resp.status_code == 400 and "face" in full_body.lower())
                                     or resp.status_code == 422
-                                    or (resp.status_code == 500 and "could not process" in full_body.lower())
+                                    or (resp.status_code == 500 and "could not process" in error_detail.lower())
                                 )
                                 if _is_permanent:
                                     asset_id = asset_map.get(fname)