feat: retire the sketch/doodle WIP title tier, and the review strip asks "Is this a WIP?" (milestone 430)

The soft tier (#1474) tagged `wip` on any post titled sketch, doodle or
scribble: 6,096 of the library's 8,876 wip tags. Its conflict audit flagged
most of them, because finished art scores >= 0.5 on some content head,
which filled the Gallery strip with 2,086 cards. A "sketch" is usually
finished work, so the operator retired the tier. The artist's own
"WIP" / "work in progress" title rule and human wip tags stay.

- Removed: the soft matcher, source and prefilter; the importer and backfill
  soft branches; soft_wip_conflict_audit with its task and beat; the
  wip_soft_title_tagging_enabled setting, API field and toggle; and
  wip_title_soft from _AUTO_SOURCES.
- Migration 0112: a soft tag the operator confirmed, or kept from the strip,
  becomes `manual`. Every other soft tag is deleted, along with the open
  review cards whose tag is gone. The column is dropped.
- Review strip (#4424): each card asks "Is this a WIP?" (or banner, and so on),
  and the buttons read "Is a WIP" / "Is not a WIP". The content tag it also
  scored on is shown as the reason it was flagged.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR

backend/app/services/importer.py

@@ -49,10 +49,8 @@ from .audits import single_color
 from .link_extract import extract_external_links
 from .thumbnailer import Thumbnailer
 from .wip_title import (
-    WIP_TITLE_SOFT_SOURCE,
     WIP_TITLE_SOURCE,
     apply_wip_image_tags,
-    matches_soft_wip_title,
     matches_wip_title,
     resolve_wip_tag_id,
 )
@@ -1040,9 +1038,7 @@ class Importer:
         removal sticks. The existing catalogue is covered separately by the
         operator-triggered backfill sweep. Gated by the settings toggle, and
         best-effort: any failure is logged, never allowed to fail the import."""
-        hard_on = self.settings.wip_title_tagging_enabled
-        soft_on = self.settings.wip_soft_title_tagging_enabled
-        if not (hard_on or soft_on):
+        if not self.settings.wip_title_tagging_enabled:
             return
         if record.primary_post_id is None:
             return
@@ -1050,21 +1046,14 @@ class Importer:
             title = self.session.execute(
                 select(Post.post_title).where(Post.id == record.primary_post_id)
             ).scalar_one_or_none()
-            # HARD tier ("WIP"/"work in progress") wins — higher precision, and it
-            # trains the head; SOFT (sketch/doodle, #1474) is the provisional fallback
-            # that never trains (source wip_title_soft).
-            if hard_on and matches_wip_title(title):
-                source = WIP_TITLE_SOURCE
-            elif soft_on and matches_soft_wip_title(title):
-                source = WIP_TITLE_SOFT_SOURCE
-            else:
+            if not matches_wip_title(title):
                 return
             if self._wip_tag_id is _UNSET:
                 self._wip_tag_id = resolve_wip_tag_id(self.session)
             if self._wip_tag_id is None:
                 return
             apply_wip_image_tags(
-                self.session, [record.id], self._wip_tag_id, source=source
+                self.session, [record.id], self._wip_tag_id, source=WIP_TITLE_SOURCE
             )
         except Exception as exc:  # noqa: BLE001 — a tag must never fail an import
             log.warning(

backend/app/services/ml/heads.py

@@ -97,8 +97,8 @@ def _sigmoid(z, np):
 
 def _conflict_scores(Xn, Wc, bc, np):
     """The presentation conflict signal (#141): per row, the MAX content-head
-    probability and WHICH head produced it. Shared by the system-tag sweep's guard-2
-    and the soft-wip audit — both ask "does this ALSO look like real content?"."""
+    probability and WHICH head produced it — the system-tag sweep's guard-2 asks
+    "does this ALSO look like real content?"."""
     cprobs = _sigmoid(Xn @ Wc.T + bc, np)
     return cprobs.max(axis=1), cprobs.argmax(axis=1)
 
@@ -106,10 +106,9 @@ def _conflict_scores(Xn, Wc, bc, np):
 def _insert_presentation_review(
     session, *, image_record_id, tag_id, conflict_tag_id, conflict_score, mode,
 ):
-    """Single-source the ring-loud PresentationReview row shape so the two writers
-    (system-tag sweep guard-2 + soft-wip audit) can't drift on columns or `mode` —
-    they share the (image_record_id, tag_id) composite PK, so a divergent `mode`
-    would be a silent first-writer-wins bug."""
+    """Single-source the ring-loud PresentationReview row shape, so every writer of
+    the (image_record_id, tag_id) composite PK agrees on columns and `mode` — a
+    divergent `mode` would be a silent first-writer-wins bug."""
     session.execute(
         pg_insert(PresentationReview)
         .values(
@@ -963,68 +962,6 @@ def system_tag_auto_apply_sweep(
     }
 
 
-def soft_wip_conflict_audit(session: Session, dry_run: bool = False) -> dict:
-    """Ring-loud audit for the SOFT WIP-title cohort (#1474). Images auto-tagged
-    `wip` from a low-precision sketch/doodle title (source='wip_title_soft') that ALSO
-    score >= the process conflict threshold on a content head are probably FINISHED
-    art mis-tagged as process — flag them (PresentationReview, mode='process') so the
-    review strip surfaces them ("also looks like <X>", Keep tag / Remove tag). Does
-    NOT remove the tag; the operator decides. No-op when there are no content heads.
-    numpy-only. Returns {n_scanned, n_flagged}."""
-    import numpy as np
-
-    from ..wip_title import WIP_TITLE_SOFT_SOURCE, resolve_wip_tag_id
-
-    settings = _settings(session)
-    ver = settings.embedder_model_version
-    conflict_thr = float(settings.process_conflict_threshold)
-    conf = _conflict_heads(session, ver)
-    wip_id = resolve_wip_tag_id(session)
-    if not conf or wip_id is None:
-        return {"n_scanned": 0, "n_flagged": 0}
-    Wc = np.vstack([np.asarray(r.weights, dtype=np.float32) for r in conf])
-    bc = np.asarray([r.bias for r in conf], dtype=np.float32)
-    conf_tag_ids = [r.tag_id for r in conf]
-
-    soft_ids = [iid for (iid,) in session.execute(
-        select(image_tag.c.image_record_id)
-        .where(image_tag.c.tag_id == wip_id)
-        .where(image_tag.c.source == WIP_TITLE_SOFT_SOURCE)
-    )]
-    # Skip images already flagged for this tag (idempotent re-runs).
-    flagged = {iid for (iid,) in session.execute(
-        select(PresentationReview.image_record_id)
-        .where(PresentationReview.tag_id == wip_id)
-    )}
-    soft_ids = [i for i in soft_ids if i not in flagged]
-
-    n_flagged = 0
-    scanned = 0
-    for start in range(0, len(soft_ids), _AUTO_APPLY_CHUNK):
-        chunk = soft_ids[start:start + _AUTO_APPLY_CHUNK]
-        emb = _load_embeddings(session, chunk)
-        cids = [i for i in chunk if i in emb]
-        if not cids:
-            continue
-        scanned += len(cids)
-        Xn = _l2norm(np.vstack([emb[i] for i in cids]).astype(np.float32), np)
-        max_c, arg_c = _conflict_scores(Xn, Wc, bc, np)
-        for k in range(len(cids)):
-            if float(max_c[k]) >= conflict_thr:
-                n_flagged += 1
-                if not dry_run:
-                    _insert_presentation_review(
-                        session,
-                        image_record_id=cids[k], tag_id=wip_id,
-                        conflict_tag_id=conf_tag_ids[int(arg_c[k])],
-                        conflict_score=float(max_c[k]),
-                        mode="process",
-                    )
-        if not dry_run:
-            session.commit()
-    return {"n_scanned": scanned, "n_flagged": n_flagged}
-
-
 def retract_auto_applied_heads(session: Session) -> int:
     """Soft auto-apply (milestone 139): re-score every standing source='head_auto'
     tag against its CURRENT head and REMOVE the ones now BELOW the head's

backend/app/services/ml/training_data.py

@@ -32,11 +32,8 @@ from ...models.tag import image_tag
 # `process_auto` (#1464): wip/editor screenshot applied by the process sweep are
 # ALSO provisional — the head must learn only from title (`wip_title`) + manual
 # labels, never its own auto-applied output, or it would runaway (operator 2026-07-12).
-# `wip_title_soft` (#1474): the soft title tier (sketch/doodle) is LOW-precision, so
-# it's provisional too — a finished piece titled "sketch" must not train the wip head.
 _AUTO_SOURCES = (
     "head_auto", "ccip_auto", "ml_auto", "presentation_auto", "process_auto",
-    "wip_title_soft",
 )
 
 

backend/app/services/wip_title.py

@@ -27,13 +27,10 @@ from .image_tag_apply import insert_image_tags
 
 # image_tag.source stamped on title-heuristic WIP tags — distinct from the other
 # apply sources so provenance stays legible and a future undo can target only these.
-# HARD tier ("WIP"/"work in progress") is high-precision → trains the wip head.
+# Only the artist's own "WIP"/"work in progress" label counts — high-precision, so it
+# trains the wip head. A sketch/doodle/scribble tier (#1474) was retired in milestone
+# 430: a "sketch" is usually finished art, and its 6k tags flooded the review strip.
 WIP_TITLE_SOURCE = "wip_title"
-# SOFT tier (sketch/doodle/scribble, #1474) is LOWER-precision — a finished "sketch"
-# is often not WIP. This source is PROVISIONAL (in training_data._AUTO_SOURCES) so it
-# NEVER trains the wip head; a soft-tagged image that also looks like real content is
-# surfaced by the ring-loud audit for review.
-WIP_TITLE_SOFT_SOURCE = "wip_title_soft"
 
 # A standalone "WIP" / "W.I.P" token, or the phrase "work in progress"
 # (space/underscore/hyphen separated). The letter-boundary lookarounds are what
@@ -45,20 +42,10 @@ _WIP_RE = re.compile(
     re.IGNORECASE,
 )
 
-# Soft tier: sketch / doodle / scribble (+ plurals), letter-boundary anchored so
-# "sketchbook" / "kadoodle" don't trip it. Deliberately conservative — recall is
-# secondary because the soft source doesn't train the head and the ring-loud audit
-# catches false positives.
-_SOFT_WIP_RE = re.compile(
-    r"(?<![A-Za-z])(?:sketch|sketches|doodle|doodles|scribble|scribbles)(?![A-Za-z])",
-    re.IGNORECASE,
-)
-
-# Coarse SQL prefilters for the backfill sweep — narrow the post scan to rows that
+# Coarse SQL prefilter for the backfill sweep — narrows the post scan to rows that
 # COULD match before the precise regex confirms. Case-insensitive ILIKE patterns.
-# Each MUST stay a SUPERSET of its regex or the sweep would silently miss posts.
+# It MUST stay a SUPERSET of the regex or the sweep would silently miss posts.
 WIP_TITLE_SQL_PREFILTER = ("%wip%", "%work%progress%")
-SOFT_WIP_TITLE_SQL_PREFILTER = ("%sketch%", "%doodle%", "%scribble%")
 
 # Chunk bulk inserts so a large sweep can't blow past psycopg's 65535-parameter
 # ceiling (3 params/row → ~21k rows max; 5k stays comfortably under).
@@ -66,19 +53,12 @@ _INSERT_CHUNK = 5000
 
 
 def matches_wip_title(title: str | None) -> bool:
-    """True when a post title explicitly marks it work-in-progress (HARD tier)."""
+    """True when a post title explicitly marks it work-in-progress."""
     if not title:
         return False
     return _WIP_RE.search(title) is not None
 
 
-def matches_soft_wip_title(title: str | None) -> bool:
-    """True when a title carries a SOFT WIP cue (sketch/doodle/scribble, #1474)."""
-    if not title:
-        return False
-    return _SOFT_WIP_RE.search(title) is not None
-
-
 def resolve_wip_tag_id(session: Session) -> int | None:
     """The seeded ``wip`` system tag's id (migration 0075), or None if absent."""
     return session.execute(