feat: clothing lane, character sources, and DCC bridges

Bulk import of the working lanes that were living untracked on the PC.

Content:
- characters/  Lena/male body lanes, bakes, texture work, run logs
- clothing/    garment pipeline, configs, gates, contract docs
- garments/    MD-authored garment sources (.zprj/.zpac)
- UAL-Lib/     Universal Animation Library 2 source (.blend/.fbx/.glb)
- tools/       blender_bridge, iclone_bridge, md_bridge, tailor, glm_agent
- docs/, plans/, dev/, .agents/plans/

Repo hygiene:
- .gitattributes: LFS now covers .blend, .zprj, .zpac, .obj, .npy and the
  Reallusion .iAvatar/.ccAvatar/.ccRestore containers. Without this the
  ~3.8 GB in this commit would land as raw blobs. .png/.jpg are left out
  on purpose — ~250 are already tracked raw and converting them would
  rewrite every one without shrinking history.
- .gitignore: exclude /accurig/ (~1 GB AccuRig program files, redistributable
  from Reallusion, nothing authored here) and /dev/null/ (git-lfs hook copies
  dropped by a `>/dev/null` redirect on Windows).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

characters/female/lena_nude/hires_claude/dbg_lines.py

@@ -0,0 +1,37 @@
+# Probe: (1) duplicate coincident verts along the stomach/waist lines, (2) where the heal
+# strip actually was vs where the lines are.
+#   blender --background --python dbg_lines.py -- <blend> <masks.npz>
+import bpy, sys
+import numpy as np
+
+argv = sys.argv[sys.argv.index("--") + 1:]
+BLEND, MASKS = argv[0], argv[1]
+bpy.ops.wm.open_mainfile(filepath=BLEND)
+ob = max([o for o in bpy.data.objects if o.type == 'MESH'],
+         key=lambda o: len(o.data.vertices))
+me = ob.data
+n_v = len(me.vertices)
+co = np.empty(n_v * 3)
+me.vertices.foreach_get("co", co)
+co = co.reshape(-1, 3)
+print(f"verts: {n_v}")
+
+# coincident duplicates: exact-position hash
+key = np.round(co * 1e5).astype(np.int64)
+kview = key[:, 0] * 73856093 ^ key[:, 1] * 19349663 ^ key[:, 2] * 83492791
+uniq, counts = np.unique(kview, return_counts=True)
+dup_groups = (counts > 1).sum()
+print(f"coincident position groups: {dup_groups} (verts in dups: {counts[counts>1].sum()})")
+
+# where are the dups? histogram by z in the torso front
+from collections import Counter
+dupset = set(uniq[counts > 1].tolist())
+isdup = np.array([k in dupset for k in kview])
+front = (np.abs(co[:, 0]) < 0.06) & (co[:, 1] < 0)
+print("z-slice | dup verts (front) | hemband verts (front)")
+M = np.load(MASKS)
+hemband = M["hemband"]
+for z0 in np.arange(0.44, 0.68, 0.02):
+    zi = (co[:, 2] >= z0) & (co[:, 2] < z0 + 0.02) & front
+    print(f"  {z0:.2f}-{z0+0.02:.2f}: dup {int((zi & isdup).sum()):6d}   hem {int((zi & hemband).sum()):6d}")
+print("PROBE_DONE")