Add describe (first-pass) mode to the judge node

New mode on QwenVLImageJudge: 'describe' looks at the reference alone and returns
a prompt-ready caption + per-axis target spec to seed the very first prompt (the
generator has nothing to reproduce yet). 'compare' is the existing ref-vs-gen
scoring. generated_image is now optional (required only for compare); shared
generation refactored into _generate_from_messages; third output renamed
diff_analysis -> analysis (mode-agnostic). agent_bridge gains --mode (describe
needs no receptor/prompt); added workflow_describe_api.json. Docs updated with the
first-pass bootstrap step. Fixed error-return arity to 5-tuple.

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

docs/CALIBRATION_POLICY.md

@@ -38,6 +38,22 @@ grouped below.
 Coarse axes blur the differences that matter for adult imagery; this set keeps the act /
 interaction cluster granular so the agent gets actionable targets.
 
+## Step 0 — first pass (describe / bootstrap)
+
+The very first iteration has no generated image yet, so the judge runs in **describe
+mode**: it looks at the reference alone and returns a prompt-ready `caption` plus a
+per-axis target spec. That seeds everything:
+
+```bash
+python agent_bridge.py --mode describe --workflow workflow/workflow_describe_api.json \
+  --run-tag seed --analysis-dir <report_dir>
+```
+→ `latest.json` = `{"mode":"describe", "caption":"...", "axes":{axis: "value", ...}}`
+
+The agent takes `caption` as the **initial prompt** and `axes` as the **initial
+axis_state**, then enters the compare loop below. No reference description has to be
+written by hand — the VL provides the target to reproduce.
+
 ## Per-iteration algorithm (greedy per-axis hill-climb)
 
 ```