Add synthetic visible-mark example gallery with canary tests

One committed example per registered mark: 12 PNG (image registry) and 6 MP4
clips (video registry), generated by scripts/render_visible_examples.py from
the committed silhouettes and detector templates -- never from user uploads.
The generator self-verifies (exit 1 when a mark misses its own example) and
tests/test_visible_examples.py holds both sides to it: registry completeness
both ways, per-engine detection on the canonical example, and the shipped
temporal selection accepting each clip.

Second tranche of measured-but-unregistered candidates parked under
scripts/assets/visible-mark-candidates/ with a README recording why none
ships yet (positives do not separate from clean negatives): samsung_en,
gemini_text, notebooklm, dola, mindvideo, higgsfield, jianying, capcut, zsky,
chromastudio, digenai, gendo.

data/README.md

@@ -7,6 +7,8 @@ data/
   fixtures/
     provenance/   Real format and provenance fixtures used by tests
                   (source records live in fixtures/README.md)
+    visible/      Synthetic per-mark example gallery (one committed example per
+                  registered visible mark; see fixtures/visible/README.md)
   calibration/
     <vendor>/     Minimal controlled inputs needed to rebuild detector assets
   synthid/

data/fixtures/visible/README.md

@@ -0,0 +1,41 @@
+# Visible-mark example gallery
+
+One committed example per registered visible mark, so the repository carries a
+working sample of everything it supports. `tests/test_visible_examples.py` holds
+both sides to it: a mark registered without an example fails the suite, and so
+does an engine that stops detecting its own example.
+
+## What these files are
+
+Every example is SYNTHETIC: `scripts/render_visible_examples.py` composites the
+mark's committed silhouette (the same font-rendered asset the detector matches)
+onto a deterministic generated base photo at the engine's measured geometry.
+No user upload and no vendor asset enters the repository: corpus files under
+`data/spaces/` are user content and stay out of git by policy, and the
+silhouettes themselves are our own renders (`scripts/render_vendor_silhouettes.py`).
+
+The examples demonstrate DETECTION geometry and house style, not vendor raster
+fidelity; real-world variants (fonts, opacities, sizes) are covered by the
+engines' calibration cohorts, which are local-only.
+
+## Regeneration
+
+    uv run python scripts/render_visible_examples.py
+
+The generator self-verifies: it fails (exit 1) if any registered mark does not
+detect on its own example, so regeneration is the fix point for drift.
+
+## Layout
+
+    <mark-key>/example.png   1536x2048..2048x2048 PNG, one per image mark
+    <mark-key>/example.mp4   960x540 90-frame clip, one per video mark
+                            (kling carries both: it is registered in both registries)
+
+Special cases: `gemini` composites the sparkle alpha map at the provider's
+configured position; `jimeng_pill` is the capture-less pill at the measured
+3:4 portrait geometry; `microsoft` is the opaque white pill with dark text
+holes (the discriminator its detector keys on). Video examples composite the
+detector's own synthetic template on every frame; where two marks share a
+shape family the example carries the discriminative variant (`veo` the legacy
+text form, `kling` the logo-plus-wordmark pair flush to the edge), because the
+temporal selection resolves cross-template ties by table order.