fix: enforce current physique range in funnel

docs/data-flow.md

@@ -36,7 +36,7 @@ A screening worker leases one discovered candidate. `MonitorService.screenCandid
 3. Seller evidence from the post, profile/about (including structured social links when exposed and narrow NSFW link-funnel language), and recent activity.
 4. Source-specific preparation in `ProcessorService`.
 
-For image posts, preparation downloads and validates the image, detects target gender, and performs model analysis. The analysis includes a separate 1–10 visible-physique score only when enough of the body is visible. If physique targeting is enabled, an unknown score or a score outside the configured inclusive range becomes an explicit rejection before strategy selection and opener generation. Passing image posts then select an image strategy and generate an opener. Chat posts do not use physique targeting; they select a chat strategy and generate from the post text. Both paths apply outbound safety checks and return either a prepared immutable result or an explicit skip reason.
+For image posts, preparation downloads and validates the image, detects target gender, and performs model analysis. The analysis includes a separate 1–10 visible-physique score only when enough of the body is visible. Its strict anchors place a broadly average visible physique at 5, reserve 8–10 for rare athletic or model-like physiques, and explicitly forbid using 6 as a safe default; nudity, breast size, face, pose, and photographic quality do not affect it. If physique targeting is enabled, an unknown score or a score outside the configured inclusive range becomes an explicit rejection before strategy selection and opener generation. Passing image posts then select an image strategy and generate an opener. Chat posts do not use physique targeting; they select a chat strategy and generate from the post text. Both paths apply outbound safety checks and return either a prepared immutable result or an explicit skip reason.
 
 Terminal results become rejected candidates. Rate limits and transient failures become retryable candidates. Prepared candidates become ready; the persisted snapshot includes the opener, grounded image analysis, attractiveness and physique scores, the physique range used, source-image URL, and experiment/model/tone metadata. This lets a different account deliver without repeating AI work and lets the authenticated operator inspect the exact preparation context. Saving a physique-range change transactionally re-evaluates prepared image candidates in ready and delivery-retry stages, rejects those outside the new range, and refreshes the displayed range snapshot for retained candidates. A candidate already leased for sending is left unchanged until that send resolves.