Redesign judge output for calibration: per-axis {score, ref, gen}, drop local fix suggestions

The local VLM now only observes and scores; correction is left to the stronger
external agent. Each axis reports the target value (ref), the current value (gen)
and the closeness (score) — the target/current/distance an agent needs to
calibrate. Expanded to ~20 granular axes (identity/body/wardrobe/action/affect/
camera/render) so the action cluster stays discriminative for explicit content.
swap_eval now inverts ref/gen of the swapped pass; diff summary sorts worst-first;
default max_new_tokens 1024. Docs aligned.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

agent_bridge.py

@@ -19,7 +19,7 @@ Stdlib only — no third-party deps, so any agent can shell out to it.
 Loop, from the agent's side:
     1. build a prompt (calibrate from the previous analysis)
     2. run this script -> capture stdout (the analysis JSON)
-    3. read overall_score + per-axis diffs + fix_suggestions
+    3. read overall_score + per-axis {score, ref, gen}
     4. adjust the prompt and go to 1, until overall_score >= target
 """