feat(agent): survive + auto-recover when curator is unreachable

For redeploying curator while away with nobody to restart the agent:

- _process now distinguishes a TRANSPORT error (curator down/redeploying, 5xx,
  401/403/408/409/429, or our lease reclaimed mid-flight) from a genuine job
  fault. On a transport error it hands the job back (best effort) and signals
  the loop to back off — instead of calling fail(), which would burn the job's
  server-side attempt budget (MAX_ATTEMPTS=3) and permanently error good jobs
  across a redeploy. Job-specific 4xx (404 image gone) still fail so they don't
  re-lease forever.
- lease loop retries with capped exponential backoff (poll_idle → 60s) and
  resets on the first successful lease, so a long outage is gentle and recovery
  is automatic within ≤60s of curator returning. Sleeps are interruptible so
  Stop / pool-shrink stays responsive.
- AUTO_START env (default on in compose) resumes the worker on container start,
  so a host reboot / crash-restart (restart: unless-stopped) self-heals with
  nobody at the desktop.
- control UI shows a "waited out" counter + an "curator unreachable, holding
  work" banner so the recovering state reads as recovery, not failure.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

.forgejo/workflows/build.yml

@@ -329,3 +329,41 @@ jobs:
           file: Dockerfile.ml
           push: true
           tags: ${{ steps.tag.outputs.tags }}
+
+  # The desktop GPU agent (#114) — published so the operator pulls + runs it on
+  # the GPU machine instead of building locally. Independent of web/ml (its own
+  # CUDA + onnxruntime-gpu image, context = agent/). Same tag cadence.
+  build-agent:
+    runs-on: python-ci
+    container:
+      image: git.fabledsword.com/bvandeusen/ci-python:3.14
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Determine tag
+        id: tag
+        run: |
+          SHORT_SHA=$(printf '%s' "$GITHUB_SHA" | cut -c1-7)
+          if [ "${GITHUB_REF#refs/tags/}" != "${GITHUB_REF}" ]; then
+            TAG_NAME="${GITHUB_REF#refs/tags/}"
+            echo "tags=git.fabledsword.com/bvandeusen/fabledcurator-agent:${TAG_NAME}" >> "$GITHUB_OUTPUT"
+          elif [ "${GITHUB_REF##*/}" = "main" ]; then
+            echo "tags=git.fabledsword.com/bvandeusen/fabledcurator-agent:main,git.fabledsword.com/bvandeusen/fabledcurator-agent:latest,git.fabledsword.com/bvandeusen/fabledcurator-agent:c-${SHORT_SHA}" >> "$GITHUB_OUTPUT"
+          else
+            echo "tags=git.fabledsword.com/bvandeusen/fabledcurator-agent:dev" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Login to Forgejo registry
+        uses: docker/login-action@v3
+        with:
+          registry: git.fabledsword.com
+          username: ${{ github.actor }}
+          password: ${{ secrets.RELEASE_TOKEN }}
+
+      - name: Build and push agent image
+        uses: docker/build-push-action@v5
+        with:
+          context: agent
+          file: agent/Dockerfile
+          push: true
+          tags: ${{ steps.tag.outputs.tags }}

.forgejo/workflows/ci.yml

@@ -92,28 +92,25 @@ jobs:
       - run: npm run test:unit
       - run: npm run build
 
-  # Integration suite split into THREE parallel shards (2026-05-25, runner
-  # capacity bumped 2→6). Each shard gets its own Postgres + Redis service
-  # set and runs alembic + a disjoint subset of integration tests. Shards
-  # share no DB state, so the autouse TRUNCATE fixture in tests/conftest.py
-  # stays single-threaded per shard but multiple shards run in parallel
-  # wall-clock. Approximate split — rebalance once --durations=15 output
-  # reveals which shard is the long pole.
+  # Single integration job — collapsed from a 3-way shard split on 2026-06-04.
+  # The shards existed to parallelize ~8.5min of integration tests; once the
+  # throwaway Postgres runs with fsync OFF (the durability step below) the whole
+  # suite runs in ~45s, so the split only triplicated the ~2min fixed overhead
+  # (container + `uv pip install` + `alembic upgrade head`) and burned 3 of 6
+  # runner slots for no wall-clock gain. One job now: spin up once, install
+  # once, migrate once, run every integration test.
   #
-  # Each shard's docker-ps filter uses its own unique job name to scope
-  # service-container resolution. act_runner appears to strip underscores
-  # from job names when building container labels — `int_api` yielded
-  # zero matches on 2026-05-25 — so shards use no-separator names
-  # (`intapi`, `intimp`, `intcore`) instead. Each step prints
-  # `docker ps -a` first so a future naming-convention shift surfaces in
-  # the log without another guess-and-push cycle.
+  # The docker-ps filter scopes to THIS job's own Postgres/Redis service
+  # containers by job name. act_runner strips underscores from job names when
+  # labelling containers (`int_api` matched nothing on 2026-05-25), so the name
+  # stays separator-free (`integration`). The step prints `docker ps -a` first
+  # so a future naming-convention shift surfaces in the log without a
+  # guess-and-push cycle.
   #
-  # Pre-baking requirements.txt into ci-python:3.14 is intentionally NOT
-  # done — per ci-requirements.md, FC is the only Python consumer of that
-  # image and the CI-Runner project's "add deps to image when used by >1
-  # project" rule keeps the install per-job.
-
-  intapi:
+  # Pre-baking requirements.txt into ci-python:3.14 is intentionally NOT done —
+  # per ci-requirements.md, FC is the only Python consumer of that image and the
+  # CI-Runner "add deps to image when used by >1 project" rule keeps it per-job.
+  integration:
     runs-on: python-ci
     container:
       image: git.fabledsword.com/bvandeusen/ci-python:3.14
@@ -144,14 +141,14 @@ jobs:
           --health-retries 10
     steps:
       - uses: actions/checkout@v4
-      - name: API integration shard (resolve service IPs, migrate, test)
+      - name: Integration suite (resolve service IPs, migrate, test)
         run: |
           set -eux
           echo "=== container landscape (diagnostic for filter scoping) ==="
           docker ps -a --format '{{.ID}} {{.Image}} -> {{.Names}}'
           echo "=== end landscape ==="
-          PG=$(docker ps --filter "name=intapi" --filter "ancestor=pgvector/pgvector:pg16" -q | head -n1)
-          RD=$(docker ps --filter "name=intapi" --filter "ancestor=redis:7-alpine" -q | head -n1)
+          PG=$(docker ps --filter "name=integration" --filter "ancestor=pgvector/pgvector:pg16" -q | head -n1)
+          RD=$(docker ps --filter "name=integration" --filter "ancestor=redis:7-alpine" -q | head -n1)
           test -n "$PG" && test -n "$RD"
           PG_IP=$(docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' "$PG")
           RD_IP=$(docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' "$RD")
@@ -168,130 +165,14 @@ jobs:
           else
             pip install -r requirements.txt pytest pytest-asyncio
           fi
+          # Relax durability on the throwaway CI Postgres so the per-test
+          # TRUNCATE's commit-fsync — the integration teardown's dominant cost
+          # (~1.5-2s/test, which collapsed the suite from ~13min to ~45s) — is
+          # skipped. fsync/full_page_writes are sighup GUCs and synchronous_commit
+          # is user-context, so ALTER SYSTEM + pg_reload_conf() applies them with
+          # NO restart. Ephemeral DB ⇒ fsync-off is safe. Non-fatal so a perms
+          # surprise can't red the job; fabledcurator is the postgres image's
+          # bootstrap superuser.
+          python -c "import os,psycopg; c=psycopg.connect(host=os.environ['DB_HOST'],port=5432,user=os.environ['DB_USER'],password=os.environ['DB_PASSWORD'],dbname=os.environ['DB_NAME'],autocommit=True); [c.execute(q) for q in ('ALTER SYSTEM SET fsync=off','ALTER SYSTEM SET synchronous_commit=off','ALTER SYSTEM SET full_page_writes=off','SELECT pg_reload_conf()')]; c.close()" || echo 'WARN: durability GUC relax failed (continuing)'
           alembic upgrade head
-          pytest tests/test_api_*.py -v -m integration --durations=15
-
-  intimp:
-    runs-on: python-ci
-    container:
-      image: git.fabledsword.com/bvandeusen/ci-python:3.14
-    env:
-      DB_USER: fabledcurator
-      DB_PASSWORD: ci_integration
-      DB_PORT: "5432"
-      DB_NAME: fabledcurator_test
-      SECRET_KEY: ci_integration_placeholder
-    services:
-      postgres:
-        image: pgvector/pgvector:pg16
-        env:
-          POSTGRES_USER: fabledcurator
-          POSTGRES_PASSWORD: ci_integration
-          POSTGRES_DB: fabledcurator_test
-        options: >-
-          --health-cmd "pg_isready -U fabledcurator"
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 10
-      redis:
-        image: redis:7-alpine
-        options: >-
-          --health-cmd "redis-cli ping"
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 10
-    steps:
-      - uses: actions/checkout@v4
-      - name: Importer integration shard (resolve service IPs, migrate, test)
-        run: |
-          set -eux
-          echo "=== container landscape (diagnostic for filter scoping) ==="
-          docker ps -a --format '{{.ID}} {{.Image}} -> {{.Names}}'
-          echo "=== end landscape ==="
-          PG=$(docker ps --filter "name=intimp" --filter "ancestor=pgvector/pgvector:pg16" -q | head -n1)
-          RD=$(docker ps --filter "name=intimp" --filter "ancestor=redis:7-alpine" -q | head -n1)
-          test -n "$PG" && test -n "$RD"
-          PG_IP=$(docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' "$PG")
-          RD_IP=$(docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' "$RD")
-          test -n "$PG_IP" && test -n "$RD_IP"
-          export DB_HOST="$PG_IP"
-          export CELERY_BROKER_URL="redis://$RD_IP:6379/0"
-          export CELERY_RESULT_BACKEND="redis://$RD_IP:6379/0"
-          for i in $(seq 1 60); do
-            (echo > "/dev/tcp/$PG_IP/5432") >/dev/null 2>&1 && break
-            sleep 2
-          done
-          if command -v uv >/dev/null 2>&1; then
-            uv pip install --system -r requirements.txt pytest pytest-asyncio
-          else
-            pip install -r requirements.txt pytest pytest-asyncio
-          fi
-          alembic upgrade head
-          pytest tests/test_importer*.py tests/test_import_*.py tests/test_migration_*.py tests/test_phash_*.py tests/test_sidecar_*.py tests/test_scan_*.py tests/test_archive_extractor.py tests/test_backfill_phash.py -v -m integration --durations=15
-
-  intcore:
-    runs-on: python-ci
-    container:
-      image: git.fabledsword.com/bvandeusen/ci-python:3.14
-    env:
-      DB_USER: fabledcurator
-      DB_PASSWORD: ci_integration
-      DB_PORT: "5432"
-      DB_NAME: fabledcurator_test
-      SECRET_KEY: ci_integration_placeholder
-    services:
-      postgres:
-        image: pgvector/pgvector:pg16
-        env:
-          POSTGRES_USER: fabledcurator
-          POSTGRES_PASSWORD: ci_integration
-          POSTGRES_DB: fabledcurator_test
-        options: >-
-          --health-cmd "pg_isready -U fabledcurator"
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 10
-      redis:
-        image: redis:7-alpine
-        options: >-
-          --health-cmd "redis-cli ping"
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 10
-    steps:
-      - uses: actions/checkout@v4
-      - name: Core integration shard (everything not api / importer / migration / phash / sidecar / scan / archive / backfill)
-        run: |
-          set -eux
-          echo "=== container landscape (diagnostic for filter scoping) ==="
-          docker ps -a --format '{{.ID}} {{.Image}} -> {{.Names}}'
-          echo "=== end landscape ==="
-          PG=$(docker ps --filter "name=intcore" --filter "ancestor=pgvector/pgvector:pg16" -q | head -n1)
-          RD=$(docker ps --filter "name=intcore" --filter "ancestor=redis:7-alpine" -q | head -n1)
-          test -n "$PG" && test -n "$RD"
-          PG_IP=$(docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' "$PG")
-          RD_IP=$(docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' "$RD")
-          test -n "$PG_IP" && test -n "$RD_IP"
-          export DB_HOST="$PG_IP"
-          export CELERY_BROKER_URL="redis://$RD_IP:6379/0"
-          export CELERY_RESULT_BACKEND="redis://$RD_IP:6379/0"
-          for i in $(seq 1 60); do
-            (echo > "/dev/tcp/$PG_IP/5432") >/dev/null 2>&1 && break
-            sleep 2
-          done
-          if command -v uv >/dev/null 2>&1; then
-            uv pip install --system -r requirements.txt pytest pytest-asyncio
-          else
-            pip install -r requirements.txt pytest pytest-asyncio
-          fi
-          alembic upgrade head
-          pytest tests/ -v -m integration --durations=15 \
-            --ignore-glob='tests/test_api_*.py' \
-            --ignore-glob='tests/test_importer*.py' \
-            --ignore-glob='tests/test_import_*.py' \
-            --ignore-glob='tests/test_migration_*.py' \
-            --ignore-glob='tests/test_phash_*.py' \
-            --ignore-glob='tests/test_sidecar_*.py' \
-            --ignore-glob='tests/test_scan_*.py' \
-            --ignore-glob='tests/test_archive_extractor.py' \
-            --ignore-glob='tests/test_backfill_phash.py'
+          pytest tests/ -v -m integration --durations=15

.gitignore

@@ -61,8 +61,12 @@ Thumbs.db
 
 # Claude Code per-user local overrides (shared .claude/settings.json is OK to commit)
 .claude/settings.local.json
+# Transient scheduler lock/state (committed by accident in 3f30327)
+.claude/scheduled_tasks.lock
+.claude/scheduled_tasks*.json
 
 # Alembic / DB scratch
 alembic/versions/__pycache__/
 *.sqlite
 *.sqlite-journal
+.superpowers/

Dockerfile

@@ -18,13 +18,16 @@ ENV PYTHONUNBUFFERED=1 \
 
 # System deps: ffmpeg (transcode + thumbnails, FC-2), unar (archives, FC-2),
 # libpq for psycopg, postgresql-client + zstd for FC-5 backup/restore
-# (pg_dump + tar --zstd), image libs.
+# (pg_dump + tar --zstd), image libs, megatools (mega.nz public-link downloads
+# for off-platform file-host links, #830 — `megatools dl`; Debian-native, no
+# external MEGA apt repo needed).
 RUN apt-get update && apt-get install -y --no-install-recommends \
         ffmpeg \
         unar \
         libpq5 \
         postgresql-client \
         zstd \
+        megatools \
         libjpeg62-turbo \
         libwebp7 \
         libpng16-16 \

agent/Dockerfile

@@ -0,0 +1,27 @@
+# FabledCurator GPU agent — runs on the desktop with the GPU.
+# CUDA + cuDNN runtime so onnxruntime-gpu can use the card (it needs cuDNN 9 —
+# the plain -runtime image lacks it: "libcudnn.so.9: cannot open shared object
+# file"); ffmpeg for video frames.
+FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive PYTHONUNBUFFERED=1
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends python3 python3-pip ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+# torch from the CUDA-12.4 wheel index (matches the base image); its wheels
+# bundle their own CUDA + cuDNN and coexist with onnxruntime-gpu. Installed
+# first + separately so the GPU build of torch is deterministic and layer-cached.
+RUN pip3 install --no-cache-dir torch==2.6.0 --index-url https://download.pytorch.org/whl/cu124
+COPY requirements.txt .
+RUN pip3 install --no-cache-dir -r requirements.txt
+COPY fc_agent ./fc_agent
+
+# imgutils ONNX models + the transformers SigLIP weights both cache here; mount
+# a volume to persist them across restarts (the SigLIP download is ~3.5 GB once).
+ENV HF_HOME=/models
+EXPOSE 8770
+
+# The control UI; the worker is started from it (or POST /start).
+CMD ["uvicorn", "fc_agent.app:app", "--host", "0.0.0.0", "--port", "8770"]

agent/README.md

@@ -0,0 +1,71 @@
+# FabledCurator GPU agent
+
+A desktop-GPU worker that embeds characters (CCIP) + figure crops for
+FabledCurator. It talks to FC **only over HTTP** — it leases jobs, fetches image
+pixels, runs the models on your GPU, and posts results back. Your FC database and
+Redis stay private; the agent never touches them.
+
+You run it when you want a burst and stop it to reclaim the card.
+
+## 0. Host prerequisite — NVIDIA Container Toolkit
+Docker needs the toolkit to hand the GPU to a container (else: *"could not select
+device driver nvidia with capabilities [[gpu]]"*). On Arch/CachyOS:
+```sh
+sudo pacman -S nvidia-container-toolkit
+sudo nvidia-ctk runtime configure --runtime=docker
+sudo systemctl restart docker
+# verify:
+docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi
+```
+
+## 1. Get a token
+In FC: **Settings → Tagging → GPU agent → Generate token** (or Rotate). Copy it.
+
+## 2. Pull (CI publishes it alongside the web/ml images)
+```sh
+docker pull git.fabledsword.com/bvandeusen/fabledcurator-agent:latest
+```
+> Local build for development instead: `docker build -t fc-gpu-agent agent/`
+
+## 3. Run (on the machine with the GPU)
+```sh
+docker run --rm --gpus all -p 8770:8770 \
+  -e FC_URL=http://curator.traefik.internal \
+  -e FC_TOKEN=<paste-the-token> \
+  -v fc-agent-models:/models \
+  git.fabledsword.com/bvandeusen/fabledcurator-agent:latest
+```
+Then open <http://localhost:8770> — the control page. Click **Start** to begin
+draining the queue; **Pause**/**Stop** to yield the GPU. The `-v fc-agent-models`
+volume caches the downloaded ONNX models so restarts are fast.
+
+Kick off a backfill from FC (**GPU agent card → Queue character embedding**), then
+watch the queue counts on the control page (or FC's card) drain.
+
+## Config (env)
+| var | default | meaning |
+|---|---|---|
+| `FC_URL` | `http://localhost:8000` | FC base URL |
+| `FC_TOKEN` | — | the bearer token (required) |
+| `AGENT_ID` | `desktop-agent` | identifies this agent's leases |
+| `BATCH_SIZE` | `4` | jobs leased per round (still processed one at a time) |
+| `CCIP_MODEL` | imgutils default | CCIP model name |
+| `DETECTOR_LEVEL` | `m` | person-detector size: `n` < `s` < `m` < `x` |
+| `POLL_IDLE_SECONDS` | `10` | wait between empty leases |
+
+## ⚠️ Verify on first run
+This part can't be CI-tested (no GPU/models in CI), so confirm against your
+installed `dghs-imgutils` (`pip show dghs-imgutils`) — see `fc_agent/models.py`:
+- `imgutils.detect.detect_person(image, level=...)` returns
+  `[((x0,y0,x1,y1), label, score), ...]`.
+- `imgutils.metrics.ccip_extract_feature(image, model=...)` returns a vector
+  (768-d for caformer). If you want the F1-0.94 variant, set
+  `CCIP_MODEL=ccip-caformer_b36-24` (verify the exact string in imgutils).
+
+If FC's matcher under/over-fires, tune the cosine threshold in
+`backend/app/services/ml/ccip.py` (`DEFAULT_SIM_THRESHOLD`) and use
+`GET /api/ccip/overview` + `/api/ccip/images/<id>` to spot-check.
+
+## CPU fallback
+Swap `onnxruntime-gpu` → `onnxruntime` in `requirements.txt` and drop `--gpus all`
+to grind it slowly on the server instead. Same agent, no card.

agent/docker-compose.yml

@@ -0,0 +1,53 @@
+# FabledCurator GPU agent — desktop run via docker compose.
+#
+# Usage:
+#   1. Generate a token: FC → Settings → Tagging → GPU agent → Generate token.
+#   2. Create a .env next to this file:
+#        FC_URL=http://curator.traefik.internal
+#        FC_TOKEN=<paste-the-token>
+#        # optional: CCIP_MODEL=ccip-caformer_b36-24   (the F1-0.94 variant)
+#   3. docker compose up -d     (pulls the published image)
+#   4. Open http://localhost:8770 → Start. Pause/Stop hands the GPU back.
+#      docker compose down       to stop the container entirely.
+#
+# Surviving a curator redeploy (you're away, can't touch the agent):
+#   - A running agent rides out curator being unreachable on its own — it retries
+#     leasing with capped backoff and resumes when the server is back. In-flight
+#     work is handed back (not failed), so a redeploy never poisons good jobs.
+#   - AUTO_START=1 (below) also resumes the worker if the AGENT container itself
+#     restarts (host reboot / crash via `restart: unless-stopped`) — no click.
+#
+# Needs the NVIDIA Container Toolkit installed on the host for --gpus.
+
+services:
+  fc-gpu-agent:
+    image: git.fabledsword.com/bvandeusen/fabledcurator-agent:latest
+    pull_policy: always
+    ports:
+      - "8770:8770"
+    environment:
+      FC_URL: ${FC_URL:-http://curator.traefik.internal}
+      FC_TOKEN: ${FC_TOKEN:?set FC_TOKEN in .env (FC → GPU agent → Generate token)}
+      CCIP_MODEL: ${CCIP_MODEL:-}
+      DETECTOR_LEVEL: ${DETECTOR_LEVEL:-m}
+      BATCH_SIZE: ${BATCH_SIZE:-4}
+      # Resume the worker automatically on container start (survive a reboot /
+      # crash-restart while you're away). Set to 0 to require a manual Start.
+      AUTO_START: ${AUTO_START:-1}
+      # Crop embedder (SigLIP concept bag): float16 keeps VRAM low on a shared
+      # desktop GPU; the model itself is announced by the server.
+      SIGLIP_DTYPE: ${SIGLIP_DTYPE:-float16}
+    volumes:
+      # Persist the downloaded ONNX models so restarts are fast.
+      - fc-agent-models:/models
+    restart: unless-stopped
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+
+volumes:
+  fc-agent-models:

agent/fc_agent/app.py

@@ -0,0 +1,134 @@
+"""FastAPI control surface for the agent (served on localhost).
+
+Start / stop the worker pool, tune the worker count live (trades desktop
+responsiveness for throughput), and watch GPU load + progress + the server-side
+queue. Config is env-seeded; the worker count is adjustable here on the fly.
+"""
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse, JSONResponse
+
+from .config import Config
+from .gpu import read_gpu
+from .worker import Worker
+
+cfg = Config.from_env()
+worker = Worker(cfg)
+app = FastAPI(title="FabledCurator GPU agent")
+
+
+@app.on_event("startup")
+def _maybe_autostart() -> None:
+    # With AUTO_START set, a container restart (host reboot, or `restart:
+    # unless-stopped` after a crash) resumes the worker on its own — the slots
+    # then ride out a still-down curator via lease backoff. Lets the agent
+    # survive a redeploy with nobody at the desktop to click Start.
+    if cfg.auto_start and cfg.token:
+        worker.start()
+
+
+@app.get("/", response_class=HTMLResponse)
+def index() -> str:
+    return _PAGE
+
+
+@app.post("/start")
+def start():
+    worker.start()
+    return JSONResponse(worker.status())
+
+
+@app.post("/stop")
+def stop():
+    worker.stop()
+    return JSONResponse(worker.status())
+
+
+@app.post("/concurrency")
+async def concurrency(request: Request):
+    body = await request.json()
+    worker.set_concurrency(int(body.get("value", 1)))
+    return JSONResponse(worker.status())
+
+
+@app.get("/status")
+def status():
+    s = worker.status()
+    s["fc_url"] = cfg.fc_url
+    s["configured"] = bool(cfg.token)
+    s["gpu"] = read_gpu()
+    try:
+        s["queue"] = worker.client.queue_status()
+    except Exception:
+        s["queue"] = None
+    return JSONResponse(s)
+
+
+_PAGE = """<!doctype html><html><head><meta charset=utf-8>
+<title>FabledCurator GPU agent</title>
+<style>
+ body{font:14px system-ui;margin:2rem;max-width:680px;background:#14171a;color:#e8e8e8}
+ h1{font-size:18px} button{font:14px system-ui;padding:.5rem 1rem;border:0;border-radius:6px;
+ margin-right:.5rem;cursor:pointer;color:#fff} .start{background:#2e7d32}.stop{background:#b3261e}
+ .step{background:#33373b;padding:.4rem .7rem;font-weight:700}
+ .stat{display:inline-block;margin-right:1.5rem;vertical-align:top}
+ .n{font-size:22px;font-weight:700} code{background:#222;padding:2px 6px;border-radius:4px}
+ .q,.gpu{margin-top:1rem;color:#9aa} .bar{height:8px;border-radius:4px;background:#222;overflow:hidden;
+ max-width:320px;margin-top:4px} .bar>i{display:block;height:100%;background:#3f7d3f}
+ .row{margin:.8rem 0}
+</style></head><body>
+<h1>FabledCurator GPU agent</h1>
+<p>FC: <code id=fc>—</code> · token <code id=cfg>—</code></p>
+<div class=row>
+ <button class=start onclick=act('start')>Start</button>
+ <button class=stop onclick=act('stop')>Stop</button>
+</div>
+<div class=row>
+ workers
+ <button class=step onclick=setc(-1)>−</button>
+ <input id=conc type=number min=1 value=1
+  style="width:3.5rem;font:700 16px system-ui;text-align:center;background:#222;color:#e8e8e8;border:1px solid #444;border-radius:6px;padding:.3rem"
+  onchange="setv(this.value)">
+ <button class=step onclick=setc(1)>+</button>
+ <span class=cap style=color:#9aa>(more = overlap I/O, fill the GPU) max <b id=capn>8</b></span>
+</div>
+<div class=row>
+ <span class=stat><span class=n id=state>stopped</span><br>state</span>
+ <span class=stat><span class=n id=active>0</span><br>active now</span>
+ <span class=stat><span class=n id=done>0</span><br>processed</span>
+ <span class=stat><span class=n id=err>0</span><br>errors</span>
+ <span class=stat><span class=n id=wait>0</span><br>waited out</span>
+</div>
+<div id=banner style="display:none;margin:.6rem 0;padding:.5rem .8rem;border-radius:6px;background:#5a4a17;color:#ffe28a">
+ curator unreachable — holding work + retrying, will resume on its own (no restart needed)
+</div>
+<div class=gpu id=gpu>GPU — …</div>
+<div class=bar><i id=gpubar style=width:0%></i></div>
+<div class=q id=queue></div>
+<script>
+ let CAP=8
+ async function act(p){await fetch('/'+p,{method:'POST'});refresh()}
+ function setc(d){ setv((parseInt(conc.value||'1'))+d) }
+ async function setv(v){
+  v=Math.max(1,Math.min(CAP,parseInt(v)||1)); conc.value=v
+  await fetch('/concurrency',{method:'POST',headers:{'Content-Type':'application/json'},
+   body:JSON.stringify({value:v})});refresh()
+ }
+ async function refresh(){
+  const s=await (await fetch('/status')).json()
+  CAP=s.max_concurrency||8; capn.textContent=CAP
+  state.textContent=s.state; active.textContent=s.active; done.textContent=s.processed
+  err.textContent=s.errors; fc.textContent=s.fc_url; wait.textContent=s.transient||0
+  // Running but the queue read failed → curator is unreachable; show we're
+  // riding it out rather than erroring.
+  banner.style.display=(s.state==='running' && !s.queue)?'block':'none'
+  if(document.activeElement!==conc) conc.value=s.concurrency
+  conc.max=CAP
+  cfg.textContent=s.configured?'set':'MISSING'
+  if(s.gpu){
+   gpu.textContent=`GPU — ${s.gpu.util_pct}% util · VRAM ${s.gpu.mem_used_mb}/${s.gpu.mem_total_mb} MB · ${s.gpu.temp_c}°C`
+   gpubar.style.width=Math.round(100*s.gpu.mem_used_mb/s.gpu.mem_total_mb)+'%'
+  } else { gpu.textContent='GPU — n/a (CPU fallback?)'; gpubar.style.width='0%' }
+  queue.textContent=s.queue?`queue — pending ${s.queue.pending} · in flight ${s.queue.leased} · done ${s.queue.done} · errored ${s.queue.error}`:'queue — unreachable'
+ }
+ refresh(); setInterval(refresh,3000)
+</script></body></html>"""

agent/fc_agent/client.py

@@ -0,0 +1,85 @@
+"""HTTP client for the FabledCurator GPU-job API.
+
+The agent's ONLY contact with FC — lease/submit/heartbeat/fail + fetch image
+bytes, all over HTTP with the bearer token. No DB/Redis.
+"""
+import requests
+from requests.adapters import HTTPAdapter
+
+
+class FcClient:
+    def __init__(self, base_url: str, token: str, agent_id: str):
+        self.base = base_url.rstrip("/")
+        self.agent_id = agent_id
+        self.s = requests.Session()
+        self.s.headers["Authorization"] = f"Bearer {token}"
+        # Many worker threads share this Session; the default pool (10) would
+        # throttle them + spam "connection pool is full". Size it for the cap.
+        adapter = HTTPAdapter(pool_connections=64, pool_maxsize=64)
+        self.s.mount("http://", adapter)
+        self.s.mount("https://", adapter)
+
+    def lease(self, batch_size: int) -> list[dict]:
+        r = self.s.post(
+            f"{self.base}/api/gpu/jobs/lease",
+            json={"agent_id": self.agent_id, "batch_size": batch_size},
+            timeout=30,
+        )
+        r.raise_for_status()
+        return r.json().get("jobs", [])
+
+    def submit(self, job_id: int, regions: list[dict], replace_kinds: list[str]) -> dict:
+        r = self.s.post(
+            f"{self.base}/api/gpu/jobs/submit",
+            json={
+                "agent_id": self.agent_id, "job_id": job_id,
+                "regions": regions, "replace_kinds": replace_kinds,
+            },
+            timeout=120,
+        )
+        r.raise_for_status()
+        return r.json()
+
+    def heartbeat(self, job_ids: list[int]) -> None:
+        try:
+            self.s.post(
+                f"{self.base}/api/gpu/jobs/heartbeat",
+                json={"agent_id": self.agent_id, "job_ids": job_ids},
+                timeout=30,
+            )
+        except requests.RequestException:
+            pass
+
+    def fail(self, job_id: int, error: str) -> None:
+        try:
+            self.s.post(
+                f"{self.base}/api/gpu/jobs/fail",
+                json={"agent_id": self.agent_id, "job_id": job_id, "error": error},
+                timeout=30,
+            )
+        except requests.RequestException:
+            pass
+
+    def release(self, job_ids: list[int]) -> None:
+        # Graceful hand-back on stop so orphaned work is re-leased at once.
+        if not job_ids:
+            return
+        try:
+            self.s.post(
+                f"{self.base}/api/gpu/jobs/release",
+                json={"agent_id": self.agent_id, "job_ids": job_ids},
+                timeout=30,
+            )
+        except requests.RequestException:
+            pass
+
+    def fetch_image(self, image_url: str) -> bytes:
+        # image_url is a server-relative path ("/images/...").
+        r = self.s.get(f"{self.base}{image_url}", timeout=180)
+        r.raise_for_status()
+        return r.content
+
+    def queue_status(self) -> dict:
+        r = self.s.get(f"{self.base}/api/gpu/status", timeout=15)
+        r.raise_for_status()
+        return r.json()

agent/fc_agent/config.py

@@ -0,0 +1,36 @@
+"""Agent config, all from env (the control container is configured at run)."""
+import os
+from dataclasses import dataclass
+
+
+@dataclass
+class Config:
+    fc_url: str          # base URL of the FabledCurator web service
+    token: str           # the bearer token from Settings → Tagging → GPU agent
+    agent_id: str        # identifies this agent's leases
+    batch_size: int      # jobs a worker leases per round
+    concurrency: int     # INITIAL parallel workers (tunable live from the UI)
+    ccip_model: str      # imgutils CCIP model name ("" → imgutils default)
+    detector_level: str  # imgutils person-detector level: n|s|m|x
+    poll_idle_seconds: float   # wait between empty leases
+    embed_dtype: str     # torch dtype for the crop embedder: float16|float32
+    embed_model_override: str  # force a SigLIP-family model ("" → use the one
+    #                            the server announces in the lease)
+    auto_start: bool     # start the worker pool on boot (so a container restart
+    #                      resumes processing without anyone clicking Start)
+
+    @classmethod
+    def from_env(cls) -> "Config":
+        return cls(
+            fc_url=os.environ.get("FC_URL", "http://localhost:8000").rstrip("/"),
+            token=os.environ.get("FC_TOKEN", ""),
+            agent_id=os.environ.get("AGENT_ID", "desktop-agent"),
+            batch_size=int(os.environ.get("BATCH_SIZE", "4")),
+            concurrency=int(os.environ.get("CONCURRENCY", "1")),
+            ccip_model=os.environ.get("CCIP_MODEL", ""),
+            detector_level=os.environ.get("DETECTOR_LEVEL", "m"),
+            poll_idle_seconds=float(os.environ.get("POLL_IDLE_SECONDS", "10")),
+            embed_dtype=os.environ.get("SIGLIP_DTYPE", "float16"),
+            embed_model_override=os.environ.get("EMBED_MODEL_NAME", ""),
+            auto_start=os.environ.get("AUTO_START", "").lower() in ("1", "true", "yes"),
+        )

agent/fc_agent/crops.py

@@ -0,0 +1,36 @@
+"""Crop primitive — vendored from backend/app/services/ml/crops.py so the agent
+is self-contained. Keep in sync if the floor logic changes."""
+from PIL import Image
+
+MIN_CROP_FRACTION = 0.10
+MIN_CROP_PX = 64
+
+
+def crop_region(
+    img: Image.Image,
+    bbox: tuple[float, float, float, float],
+    *,
+    pad: float = 0.0,
+    min_fraction: float = MIN_CROP_FRACTION,
+    min_px: int = MIN_CROP_PX,
+) -> Image.Image | None:
+    """Crop a NORMALIZED bbox (x, y, w, h in [0,1]); None if below the size
+    floor (max of a fraction-of-short-side and an absolute pixel floor)."""
+    iw, ih = img.size
+    x, y, w, h = bbox
+    px, py, pw, ph = x * iw, y * ih, w * iw, h * ih
+    if pad:
+        px -= pw * pad / 2.0
+        py -= ph * pad / 2.0
+        pw *= (1.0 + pad)
+        ph *= (1.0 + pad)
+    left = max(0, int(round(px)))
+    top = max(0, int(round(py)))
+    right = min(iw, int(round(px + pw)))
+    bottom = min(ih, int(round(py + ph)))
+    if right <= left or bottom <= top:
+        return None
+    floor = max(min_px, int(min_fraction * min(iw, ih)))
+    if min(right - left, bottom - top) < floor:
+        return None
+    return img.crop((left, top, right, bottom)).convert("RGB")

agent/fc_agent/embedder.py

@@ -0,0 +1,69 @@
+"""Crop EMBEDDER for the concept bag — model-agnostic (CLIP/SigLIP-family).
+
+The server trains its per-concept heads in the embedding space of whatever model
+its `embedder_model_version` names; a crop must be embedded with the SAME model
+or its vector lands in a different coordinate system and every head misfires. So
+the model identity (HF name + version) is ANNOUNCED BY THE SERVER in the lease —
+nothing here is hardcoded to SigLIP. Whatever name the server sends is loaded via
+transformers `get_image_features` (the CLIP/SigLIP-family image-tower call); a
+non-CLIP backbone (e.g. a DINO encoder) would need its own pooling adapter.
+
+torch on CUDA, fp16 by default to keep VRAM low on a shared desktop GPU — the
+tiny fp16-vs-fp32 difference is negligible for the linear heads (cosine ~0.999).
+A single inference lock serializes the forward pass: the pipeline is I/O-bound,
+so the GPU isn't the bottleneck, and one model shared across worker threads is
+safest behind a lock.
+"""
+import threading
+
+import numpy as np
+from PIL import Image
+
+
+class CropEmbedder:
+    def __init__(self, model_name: str, dtype: str = "float16"):
+        self._name = model_name
+        self._dtype_name = dtype
+        self._model = None
+        self._processor = None
+        self._torch = None
+        self._device = None
+        self._dt = None
+        self._load_lock = threading.Lock()
+        self._infer_lock = threading.Lock()
+
+    @property
+    def model_name(self) -> str:
+        return self._name
+
+    def load(self) -> None:
+        if self._model is not None:
+            return
+        with self._load_lock:
+            if self._model is not None:
+                return
+            import torch
+            from transformers import AutoImageProcessor, AutoModel
+
+            self._torch = torch
+            self._device = "cuda" if torch.cuda.is_available() else "cpu"
+            dt = getattr(torch, self._dtype_name, torch.float16)
+            if self._device == "cpu":
+                dt = torch.float32   # fp16 matmul is unsupported/slow on CPU
+            self._dt = dt
+            self._processor = AutoImageProcessor.from_pretrained(self._name)
+            model = AutoModel.from_pretrained(self._name, torch_dtype=dt)
+            model.eval().to(self._device)
+            self._model = model
+
+    def embed(self, image: Image.Image) -> list[float]:
+        """A crop → its embedding as a plain float list, ready to POST."""
+        self.load()
+        torch = self._torch
+        enc = self._processor(images=image, return_tensors="pt")
+        pixel_values = enc["pixel_values"].to(self._device, self._dt)
+        with self._infer_lock, torch.no_grad():
+            out = self._model.get_image_features(pixel_values=pixel_values)
+        pooled = out.pooler_output if hasattr(out, "pooler_output") else out
+        vec = pooled[0].float().cpu().numpy().astype(np.float32).reshape(-1)
+        return vec.tolist()

agent/fc_agent/gpu.py

@@ -0,0 +1,30 @@
+"""GPU load readout via nvidia-smi (present in the container thanks to the
+NVIDIA Container Toolkit's `utility` capability). Returns None if unavailable —
+the UI just shows n/a (e.g. CPU-fallback run)."""
+import subprocess
+
+
+def read_gpu() -> dict | None:
+    try:
+        out = subprocess.run(
+            [
+                "nvidia-smi",
+                "--query-gpu=utilization.gpu,memory.used,memory.total,temperature.gpu",
+                "--format=csv,noheader,nounits",
+            ],
+            capture_output=True, text=True, timeout=5, check=True,
+        ).stdout.strip().splitlines()
+    except (OSError, subprocess.SubprocessError):
+        return None
+    if not out:
+        return None
+    parts = [p.strip() for p in out[0].split(",")]
+    try:
+        return {
+            "util_pct": int(float(parts[0])),
+            "mem_used_mb": int(float(parts[1])),
+            "mem_total_mb": int(float(parts[2])),
+            "temp_c": int(float(parts[3])),
+        }
+    except (ValueError, IndexError):
+        return None

agent/fc_agent/media.py

@@ -0,0 +1,63 @@
+"""Image + video handling. Stills load directly; videos are sampled into frames
+(ffmpeg) at the cadence FC sends — so a video becomes a bag of per-frame
+instances, each with a timestamp."""
+import io
+import os
+import subprocess
+import tempfile
+
+from PIL import Image
+
+
+def is_video(mime: str) -> bool:
+    return bool(mime) and (mime.startswith("video/") or mime in {"image/gif"})
+
+
+def to_rgb(img: Image.Image) -> Image.Image:
+    """RGB, flattening any transparency onto white first. A naive convert('RGB')
+    on a palette-with-transparency image (common for character PNGs on a clear
+    background) lets PIL guess the transparent pixels — usually black artifacts
+    that bleed into the crop + the embedding (and the "should be converted to
+    RGBA" warning). Compositing over white gives a clean, consistent background."""
+    if img.mode in ("RGBA", "LA", "PA") or (
+        img.mode == "P" and "transparency" in img.info
+    ):
+        img = img.convert("RGBA")
+        bg = Image.new("RGBA", img.size, (255, 255, 255, 255))
+        return Image.alpha_composite(bg, img).convert("RGB")
+    return img.convert("RGB")
+
+
+def load_image(data: bytes) -> Image.Image:
+    return to_rgb(Image.open(io.BytesIO(data)))
+
+
+def sample_frames(
+    data: bytes, interval_seconds: float, max_frames: int
+) -> list[tuple[float, Image.Image]]:
+    """Extract up to max_frames frames at one-every-interval_seconds via ffmpeg.
+    Returns [(timestamp_seconds, frame)]. Empty on failure (caller falls back)."""
+    interval = max(0.5, float(interval_seconds or 4.0))
+    cap = max(1, int(max_frames or 64))
+    with tempfile.TemporaryDirectory() as tmp:
+        src = os.path.join(tmp, "in")
+        with open(src, "wb") as fh:
+            fh.write(data)
+        pattern = os.path.join(tmp, "f_%05d.jpg")
+        try:
+            subprocess.run(
+                [
+                    "ffmpeg", "-nostdin", "-loglevel", "error", "-i", src,
+                    "-vf", f"fps=1/{interval}", "-frames:v", str(cap),
+                    "-q:v", "3", pattern,
+                ],
+                check=True, timeout=600,
+            )
+        except (subprocess.SubprocessError, FileNotFoundError):
+            return []
+        out: list[tuple[float, Image.Image]] = []
+        names = sorted(n for n in os.listdir(tmp) if n.startswith("f_"))
+        for i, name in enumerate(names[:cap]):
+            with Image.open(os.path.join(tmp, name)) as im:
+                out.append((round(i * interval, 2), to_rgb(im)))
+        return out

agent/fc_agent/models.py

@@ -0,0 +1,39 @@
+"""imgutils model wrappers — the figure DETECTOR + the CCIP EMBEDDER.
+
+⚠️ VERIFY ON FIRST RUN: the exact imgutils function names/signatures + the CCIP
+model string can drift between dghs-imgutils releases. These are the two seams to
+check against your installed version (`pip show dghs-imgutils`):
+  - detect_person(image, level=...) -> [((x0,y0,x1,y1), label, score), ...]
+  - ccip_extract_feature(image, model=...) -> a vector (768-d for caformer)
+imgutils auto-downloads the ONNX models from HuggingFace on first use; GPU is
+used when onnxruntime-gpu is installed.
+"""
+import numpy as np
+from PIL import Image
+
+
+def detect_figures(image: Image.Image, level: str = "m") -> list[tuple[tuple, float | None]]:
+    """Person/figure bounding boxes, NORMALIZED (x, y, w, h in [0,1]) + score.
+    Returns [] if detection finds nothing (caller falls back to whole-image)."""
+    from imgutils.detect import detect_person
+
+    iw, ih = image.size
+    out = []
+    for (x0, y0, x1, y1), _label, score in detect_person(image, level=level):
+        out.append((
+            (x0 / iw, y0 / ih, (x1 - x0) / iw, (y1 - y0) / ih),
+            float(score),
+        ))
+    return out
+
+
+def ccip_vector(image: Image.Image, model: str | None = None) -> list[float]:
+    """The CCIP identity embedding of a (cropped) character image, as a plain
+    float list ready to POST."""
+    from imgutils.metrics import ccip_extract_feature
+
+    feat = (
+        ccip_extract_feature(image, model=model)
+        if model else ccip_extract_feature(image)
+    )
+    return np.asarray(feat, dtype=np.float32).reshape(-1).tolist()

agent/fc_agent/worker.py

@@ -0,0 +1,274 @@
+"""The lease → fetch → detect+embed → submit loop, run by a pool of worker
+slots whose count is tunable live from the UI.
+
+Each slot is an independent loop (its own leases; the server's SKIP-LOCKED lease
+keeps them from colliding). More slots = more GPU load + throughput; the model is
+loaded once and shared, so slots add concurrent inference, not N× model VRAM.
+That's the dial the operator turns to trade desktop responsiveness for speed.
+
+Stop (or shrinking the pool) RELEASES a slot's still-leased jobs immediately so
+orphaned work is re-picked at once rather than waiting out the lease.
+"""
+import threading
+
+import requests
+
+from . import media, models
+from .client import FcClient
+from .config import Config
+from .crops import crop_region
+
+# Cap on the lease-retry backoff: when curator is unreachable (e.g. you redeploy
+# it while away), each slot retries leasing with exponential backoff up to this
+# many seconds, then resumes within this window once the server is back — no
+# restart needed.
+MAX_BACKOFF_SECONDS = 60.0
+
+
+def _is_transient(exc: "requests.RequestException") -> bool:
+    """A server/transport problem (wait it out) vs a job-specific fault (fail it).
+    No response → connection refused/timeout → curator is down → transient. With
+    a response: 5xx, auth (401/403, e.g. a token blip on redeploy), 408/409/429
+    (timeout / our lease reclaimed / rate-limited) are all 'not this job's fault'.
+    A specific 4xx like 404 (image gone) / 400 IS the job's fault → fail it."""
+    resp = getattr(exc, "response", None)
+    if resp is None:
+        return True
+    return resp.status_code >= 500 or resp.status_code in (401, 403, 408, 409, 429)
+
+# Generous cap: the pipeline is usually I/O-bound (downloading + decoding images
+# over HTTP), so the GPU stays underused until many workers overlap that I/O.
+# Push it up while watching the GPU util + VRAM in the UI.
+MAX_CONCURRENCY = 32
+
+# Fallbacks only — the server ANNOUNCES the embedding model (name + version) in
+# the lease so the agent stays model-agnostic and in lock-step with the space
+# the heads were trained in. These cover an older server that doesn't send them.
+DEFAULT_EMBED_MODEL = "google/siglip-so400m-patch14-384"
+DEFAULT_EMBED_VERSION = "siglip-so400m-patch14-384"
+
+
+class _Slot:
+    """One worker loop. `inflight` = jobs leased but not yet processed, so a
+    graceful stop can hand them back."""
+    __slots__ = ("stop", "inflight")
+
+    def __init__(self):
+        self.stop = threading.Event()
+        self.inflight: list[int] = []
+
+
+class Worker:
+    def __init__(self, cfg: Config):
+        self.cfg = cfg
+        self.client = FcClient(cfg.fc_url, cfg.token, cfg.agent_id)
+        self._lock = threading.Lock()
+        self._running = False
+        self._target = max(1, min(MAX_CONCURRENCY, cfg.concurrency))
+        self._slots: list[_Slot] = []
+        self.processed = 0
+        self.errors = 0
+        self.transient = 0        # jobs handed back due to a server outage (NOT
+        #                           failed) — the "waiting out curator" counter
+        self._active = 0          # slots currently mid-image
+        # The crop embedder (SigLIP-family) is built lazily on the first job that
+        # needs it, from the model the server announces — one shared instance.
+        self._embedder = None
+        self._embedder_lock = threading.Lock()
+
+    # --- control -----------------------------------------------------------
+    def start(self):
+        with self._lock:
+            self._running = True
+            self._reconcile_locked()
+
+    def stop(self):
+        with self._lock:
+            self._running = False
+            slots, self._slots = self._slots, []
+        for s in slots:
+            s.stop.set()          # each slot releases its inflight on exit
+
+    def set_concurrency(self, n: int):
+        with self._lock:
+            self._target = max(1, min(MAX_CONCURRENCY, int(n)))
+            if self._running:
+                self._reconcile_locked()
+
+    def _reconcile_locked(self):
+        while len(self._slots) < self._target:
+            slot = _Slot()
+            self._slots.append(slot)
+            threading.Thread(target=self._loop, args=(slot,), daemon=True).start()
+        while len(self._slots) > self._target:
+            self._slots.pop().stop.set()
+
+    def status(self) -> dict:
+        with self._lock:
+            return {
+                "state": "running" if self._running else "stopped",
+                "concurrency": self._target,
+                "max_concurrency": MAX_CONCURRENCY,
+                "workers": len(self._slots),
+                "active": self._active,
+                "processed": self.processed,
+                "errors": self.errors,
+                "transient": self.transient,
+            }
+
+    def _bump(self, *, processed=0, errors=0, active=0, transient=0):
+        with self._lock:
+            self.processed += processed
+            self.errors += errors
+            self.transient += transient
+            self._active += active
+
+    # --- per-slot loop -----------------------------------------------------
+    def _loop(self, slot: _Slot):
+        backoff = self.cfg.poll_idle_seconds
+        while not slot.stop.is_set() and self._running:
+            try:
+                jobs = self.client.lease(self.cfg.batch_size)
+                backoff = self.cfg.poll_idle_seconds      # server answered → reset
+            except Exception:
+                # curator unreachable (redeploy, network drop): wait it out with
+                # exponential backoff, capped — resume on our own when it returns.
+                self._interruptible_sleep(slot, backoff)
+                backoff = min(backoff * 2, MAX_BACKOFF_SECONDS)
+                continue
+            if not jobs:
+                self._interruptible_sleep(slot, self.cfg.poll_idle_seconds)
+                continue
+            slot.inflight = [j["job_id"] for j in jobs]
+            for job in jobs:
+                if slot.stop.is_set() or not self._running:
+                    break
+                ok = self._process(job)
+                slot.inflight = [i for i in slot.inflight if i != job["job_id"]]
+                if not ok:
+                    # Server went away mid-batch: hand the rest back (best effort)
+                    # and back off instead of hammering a recovering server or
+                    # burning the jobs' attempt budgets on fail().
+                    if slot.inflight:
+                        self.client.release(slot.inflight)
+                        slot.inflight = []
+                    self._interruptible_sleep(slot, backoff)
+                    backoff = min(backoff * 2, MAX_BACKOFF_SECONDS)
+                    break
+                if slot.inflight:
+                    self.client.heartbeat(slot.inflight)
+        # Graceful hand-back of anything leased but not processed.
+        if slot.inflight:
+            self.client.release(slot.inflight)
+            slot.inflight = []
+
+    def _interruptible_sleep(self, slot: _Slot, seconds: float):
+        """Sleep, but wake immediately if the slot is told to stop — so a Stop or
+        a pool-shrink doesn't hang for a full backoff window."""
+        slot.stop.wait(timeout=seconds)
+
+    def _ensure_embedder(self, model_name: str):
+        if self._embedder is not None:
+            return self._embedder
+        with self._embedder_lock:
+            if self._embedder is None:
+                from .embedder import CropEmbedder
+                self._embedder = CropEmbedder(model_name, self.cfg.embed_dtype)
+        return self._embedder
+
+    def _process(self, job: dict) -> bool:
+        """Process one job. Returns True when handled (completed, or hard-failed
+        because the job itself is bad) and False on a TRANSPORT error (curator
+        unreachable / 5xx / our lease was reclaimed mid-flight) — which is not
+        the job's fault, so the caller backs off and the job is left to be
+        re-leased rather than fail()ed into its attempt budget."""
+        self._bump(active=1)
+        try:
+            data = self.client.fetch_image(job["image_url"])
+            if media.is_video(job.get("mime", "")):
+                frames = media.sample_frames(
+                    data, job.get("frame_interval_seconds", 4.0),
+                    job.get("max_frames", 64),
+                ) or [(None, media.load_image(data))]
+            else:
+                frames = [(None, media.load_image(data))]
+
+            # task picks what to produce per crop:
+            #   'siglip' (backfill existing images) → concept (SigLIP) regions
+            #     ONLY, so it never churns their figure/CCIP regions or the
+            #     character-reference cache.
+            #   'ccip' / 'both' (a new image's first pass) → figure (CCIP) AND
+            #     concept (SigLIP) in one go, off the same crop.
+            task = job.get("task") or "ccip"
+            want_ccip = task in ("ccip", "both")
+            want_siglip = task in ("ccip", "siglip", "both")
+            replace_kinds = (
+                ["concept"] if task == "siglip" else ["figure", "face", "concept"]
+            )
+
+            embed_version = job.get("embed_version") or DEFAULT_EMBED_VERSION
+            embedder = None
+            if want_siglip:
+                model_name = (
+                    self.cfg.embed_model_override
+                    or job.get("embed_model_name")
+                    or DEFAULT_EMBED_MODEL
+                )
+                embedder = self._ensure_embedder(model_name)
+
+            regions = []
+            ccip_ev = self.cfg.ccip_model or "ccip-default"
+            dv = f"person-{self.cfg.detector_level}"
+            for t, frame in frames:
+                figs = models.detect_figures(frame, self.cfg.detector_level)
+                if not figs:
+                    figs = [((0.0, 0.0, 1.0, 1.0), None)]   # whole-frame fallback
+                for bbox, score in figs:
+                    crop = crop_region(frame, bbox)
+                    if crop is None:
+                        continue
+                    if want_ccip:
+                        regions.append({
+                            "kind": "figure",
+                            "bbox": list(bbox),
+                            "frame_time": t,
+                            "score": score,
+                            "ccip_embedding": models.ccip_vector(
+                                crop, self.cfg.ccip_model or None
+                            ),
+                            "embedding_version": ccip_ev,
+                            "detector_version": dv,
+                        })
+                    if want_siglip:
+                        regions.append({
+                            "kind": "concept",
+                            "bbox": list(bbox),
+                            "frame_time": t,
+                            "score": score,
+                            "siglip_embedding": embedder.embed(crop),
+                            "embedding_version": embed_version,
+                            "detector_version": dv,
+                        })
+            self.client.submit(job["job_id"], regions, replace_kinds)
+            self._bump(processed=1)
+            return True
+        except requests.RequestException as exc:
+            if _is_transient(exc):
+                # curator down/redeploying, a 5xx, or our lease was reclaimed
+                # while we worked. NOT the job's fault — hand it back (best
+                # effort; no-ops if the server is still down, then the server's
+                # orphan-recovery reclaims it) and signal the loop to wait.
+                self._bump(transient=1)
+                self.client.release([job["job_id"]])
+                return False
+            # A job-specific HTTP fault (404 image gone, 400) → fail it so it
+            # doesn't re-lease forever.
+            self._bump(errors=1)
+            self.client.fail(job["job_id"], str(exc)[:500])
+            return True
+        except Exception as exc:   # noqa: BLE001 — a genuine job fault: report it
+            self._bump(errors=1)
+            self.client.fail(job["job_id"], str(exc)[:500])
+            return True
+        finally:
+            self._bump(active=-1)

agent/requirements.txt

@@ -0,0 +1,15 @@
+# CCIP + figure detection (ONNX models, auto-downloaded from HuggingFace).
+dghs-imgutils>=0.4
+# GPU inference for the ONNX models. Swap to onnxruntime (CPU) for a slow
+# server-side fallback run.
+onnxruntime-gpu
+# The crop EMBEDDER (concept bag). torch is installed separately in the
+# Dockerfile from the CUDA-12.4 wheel index so the GPU build is deterministic;
+# transformers loads whatever SigLIP-family model the server announces.
+transformers>=4.45
+# Control surface + HTTP.
+fastapi
+uvicorn[standard]
+requests
+pillow
+numpy

alembic/env.py

@@ -1,13 +1,28 @@
 """Alembic environment — reads DATABASE_URL from app config."""
 
+import os
+import re
 from logging.config import fileConfig
 
-from sqlalchemy import engine_from_config, pool
+from sqlalchemy import engine_from_config, pool, text
 
 from alembic import context
 from backend.app.config import get_config
 from backend.app.models import Base
 
+# Fail a blocked migration FAST instead of hanging forever. Migrations run
+# against the live DB while workers hold locks; 0040's `ALTER series_page` queued
+# behind a tag-merge that held a series_page lock for minutes (the merge runs an
+# unindexed full scan over image_record while repointing series_page) and hung
+# with no timeout — silent, indefinite (operator-flagged 2026-06-07). With a
+# lock_timeout a blocked DDL errors ("canceling statement due to lock timeout")
+# and the entrypoint's `alembic upgrade head` exits non-zero, so the deploy
+# retries / surfaces loudly rather than wedging. Override via env when a known
+# slow-lock window is expected.
+_MIGRATION_LOCK_TIMEOUT = os.environ.get("MIGRATION_LOCK_TIMEOUT", "30s")
+if not re.fullmatch(r"\d+\s*(ms|s|min)?", _MIGRATION_LOCK_TIMEOUT.strip()):
+    _MIGRATION_LOCK_TIMEOUT = "30s"  # ignore a malformed override
+
 config = context.config
 
 if config.config_file_name is not None:
@@ -38,6 +53,15 @@ def run_migrations_online() -> None:
         poolclass=pool.NullPool,
     )
     with connectable.connect() as connection:
+        # Session-level lock_timeout for every DDL statement in this run. Set
+        # (and commit) before alembic opens its own transaction so the GUC
+        # persists on this connection regardless of how alembic structures its
+        # transactions. Value is from our own env, so f-string interpolation is
+        # safe (and it's been pattern-validated above); SET takes no bind params.
+        connection.execute(
+            text(f"SET lock_timeout = '{_MIGRATION_LOCK_TIMEOUT}'")
+        )
+        connection.commit()
         context.configure(
             connection=connection,
             target_metadata=target_metadata,

alembic/versions/0034_artist_visit.py

@@ -0,0 +1,53 @@
+"""artist_visit: per-artist last-viewed timestamp for the "+N new" badge
+
+Revision ID: 0034
+Revises: 0033
+Create Date: 2026-06-03
+
+Powers the artists-directory "+N new since last visit" badge + ArtistView
+banner. Single row per artist (no user_id yet — rule #47 multi-user ACL
+is aspirational; widens to (user_id, artist_id) PK when User lands).
+
+Seed every existing artist with `last_viewed_at = NOW()` so the badge
+starts at 0 across the board — no noisy "you have 5000 unseen images"
+on first deploy. New artists auto-get a row via
+`ArtistService.find_or_create`.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0034"
+down_revision: Union[str, None] = "0033"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "artist_visit",
+        sa.Column(
+            "artist_id",
+            sa.Integer,
+            sa.ForeignKey("artist.id", ondelete="CASCADE"),
+            primary_key=True,
+        ),
+        sa.Column(
+            "last_viewed_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("NOW()"),
+        ),
+    )
+    # Seed: every existing artist starts "fully caught up". Without this,
+    # every operator with N artists would see N badges (worth of every
+    # image ever imported) on first deploy.
+    op.execute(
+        "INSERT INTO artist_visit (artist_id, last_viewed_at) "
+        "SELECT id, NOW() FROM artist"
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("artist_visit")

alembic/versions/0035_image_record_effective_date.py

@@ -0,0 +1,70 @@
+"""image_record.effective_date: materialized gallery sort key + index
+
+Revision ID: 0035
+Revises: 0034
+Create Date: 2026-06-04
+
+The gallery ordered/cursored on COALESCE(post.post_date,
+image_record.created_at) across the Post outer join. That expression spans
+two tables, so no index can serve it — every /scroll sorted a large slice
+of the library, and the frontend fired ten of them serially per initial
+load. Materialize the value into image_record.effective_date and index
+(effective_date DESC, id DESC) so the cursor scroll is an index range scan.
+
+Backfill = COALESCE(primary post's post_date, created_at) so existing rows
+keep their exact ordering. New rows get the created_at-equivalent server
+default; services/importer.py overrides it with the post's date when a
+primary post with a date is linked.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0035"
+down_revision: Union[str, None] = "0034"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Add nullable first so the backfill can populate before NOT NULL.
+    op.add_column(
+        "image_record",
+        sa.Column("effective_date", sa.DateTime(timezone=True), nullable=True),
+    )
+    # Pure set-based UPDATEs (no per-row params) — immune to the 65535
+    # bind-parameter ceiling regardless of library size.
+    op.execute(
+        """
+        UPDATE image_record AS ir
+        SET effective_date = COALESCE(p.post_date, ir.created_at)
+        FROM post AS p
+        WHERE ir.primary_post_id = p.id
+        """
+    )
+    op.execute(
+        """
+        UPDATE image_record
+        SET effective_date = created_at
+        WHERE effective_date IS NULL
+        """
+    )
+    op.alter_column(
+        "image_record",
+        "effective_date",
+        nullable=False,
+        server_default=sa.text("now()"),
+    )
+    # DESC/DESC matches the gallery's ORDER BY effective_date DESC, id DESC
+    # so the scroll is a forward index scan; raw SQL because alembic's
+    # column list doesn't express per-column DESC cleanly.
+    op.execute(
+        "CREATE INDEX ix_image_record_effective_date "
+        "ON image_record (effective_date DESC, id DESC)"
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_image_record_effective_date", table_name="image_record")
+    op.drop_column("image_record", "effective_date")

alembic/versions/0036_siglip_embedding_hnsw_index.py

@@ -0,0 +1,41 @@
+"""image_record.siglip_embedding: HNSW cosine index for "more like this"
+
+Revision ID: 0036
+Revises: 0035
+Create Date: 2026-06-04
+
+Gallery Phase 3 (visual similarity search) ranks images by
+`siglip_embedding.cosine_distance(source_embedding)`. Without an index that's
+a sequential scan computing a 1152-dim distance for every row — fine at small
+scale, but it grows linearly with the library. Add an HNSW index with
+`vector_cosine_ops` so the top-N nearest search is sub-50ms ANN.
+
+1152 dims is under pgvector's 2000-dim HNSW limit, so HNSW (no training,
+better recall than IVFFlat) is the right choice. ONE-TIME COST: building the
+index over the existing embeddings (~57k vectors on the operator's library)
+locks image_record for ~30-60s during this migration on deploy — acceptable
+for a single-operator homelab. NULL embeddings (videos / not-yet-embedded
+rows) are simply not indexed.
+"""
+from typing import Sequence, Union
+
+from alembic import op
+
+revision: str = "0036"
+down_revision: Union[str, None] = "0035"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Raw SQL: alembic's create_index doesn't express the `USING hnsw (...
+    # vector_cosine_ops)` access-method + opclass cleanly. Must match the
+    # query's cosine_distance operator class to be usable by the planner.
+    op.execute(
+        "CREATE INDEX ix_image_record_siglip_hnsw "
+        "ON image_record USING hnsw (siglip_embedding vector_cosine_ops)"
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_image_record_siglip_hnsw", table_name="image_record")

alembic/versions/0037_patreon_seen_media.py

@@ -0,0 +1,53 @@
+"""patreon_seen_media: per-source ledger of already-ingested Patreon media
+
+Revision ID: 0037
+Revises: 0036
+Create Date: 2026-06-05
+
+Native Patreon ingester (build step 2a). Replaces gallery-dl's
+archive.sqlite3 with our own queryable table. The downloader upserts one
+row per (source, media) so routine walks skip media we've already
+processed; a future "recovery" mode bypasses the ledger to re-walk.
+
+`filehash` is a 32-hex Patreon CDN MD5, OR a video sentinel of the form
+``video:<post_id>:<media_id>`` — hence String(128). The unique
+constraint on (source_id, filehash) is the dedup upsert key.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0037"
+down_revision: Union[str, None] = "0036"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "patreon_seen_media",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column(
+            "source_id",
+            sa.Integer,
+            sa.ForeignKey("source.id", ondelete="CASCADE"),
+            nullable=False,
+            index=True,
+        ),
+        sa.Column("filehash", sa.String(128), nullable=False),
+        sa.Column("post_id", sa.String(64), nullable=True),
+        sa.Column(
+            "seen_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("NOW()"),
+        ),
+        sa.UniqueConstraint(
+            "source_id", "filehash", name="uq_patreon_seen_media_source_id"
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("patreon_seen_media")

alembic/versions/0038_patreon_failed_media.py

@@ -0,0 +1,58 @@
+"""patreon_failed_media: per-source dead-letter ledger for failing Patreon media
+
+Revision ID: 0038
+Revises: 0037
+Create Date: 2026-06-06
+
+Plan #705 (#7). Media that keeps failing to download/validate (404'd CDN,
+deleted post, geo-blocked Mux, persistently-corrupt bytes) gets recorded here
+with an attempt counter; once it crosses the dead-letter threshold the ingester
+skips it on routine walks (recovery still re-attempts). A clean download clears
+the row. UNIQUE (source_id, filehash) is the upsert key (same media key the
+seen-ledger uses).
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0038"
+down_revision: Union[str, None] = "0037"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "patreon_failed_media",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column(
+            "source_id",
+            sa.Integer,
+            sa.ForeignKey("source.id", ondelete="CASCADE"),
+            nullable=False,
+            index=True,
+        ),
+        sa.Column("filehash", sa.String(128), nullable=False),
+        sa.Column("attempts", sa.Integer, nullable=False, server_default="1"),
+        sa.Column("last_error", sa.Text, nullable=True),
+        sa.Column(
+            "first_failed_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("NOW()"),
+        ),
+        sa.Column(
+            "last_failed_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("NOW()"),
+        ),
+        sa.UniqueConstraint(
+            "source_id", "filehash", name="uq_patreon_failed_media_source_id"
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("patreon_failed_media")

alembic/versions/0039_library_audit_resume.py

@@ -0,0 +1,40 @@
+"""library_audit_run: resume cursor + progress timestamp for chunked scans
+
+Revision ID: 0039
+Revises: 0038
+Create Date: 2026-06-07
+
+scan_library_for_rule used to run one 2h pass that timed out on large libraries
+and monopolized the concurrency-1 maintenance queue (operator-flagged). It now
+runs short time-boxed chunks that re-enqueue: `resume_after_id` persists the
+keyset cursor so the next chunk continues where it left off, and
+`last_progress_at` lets the recovery sweep tell a progressing multi-chunk audit
+from a genuinely stuck one.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0039"
+down_revision: Union[str, None] = "0038"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "library_audit_run",
+        sa.Column(
+            "resume_after_id", sa.Integer, nullable=False, server_default="0"
+        ),
+    )
+    op.add_column(
+        "library_audit_run",
+        sa.Column("last_progress_at", sa.DateTime(timezone=True), nullable=True),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("library_audit_run", "last_progress_at")
+    op.drop_column("library_audit_run", "resume_after_id")

alembic/versions/0040_series_chapters.py

@@ -0,0 +1,108 @@
+"""series chapters: chapter layer over series_page (FC-6.1)
+
+Revision ID: 0040
+Revises: 0039
+Create Date: 2026-06-07
+
+A series (Tag kind='series') gains an ordered chapter layer. Reading order
+becomes (series_chapter.chapter_number, series_page.page_number). Every existing
+series is backfilled into a single auto-chapter (chapter_number=1) holding its
+current flat pages, so no data is lost and the old flat ordering is preserved.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0040"
+down_revision: Union[str, None] = "0039"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "series_chapter",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column(
+            "series_tag_id",
+            sa.Integer,
+            sa.ForeignKey("tag.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("chapter_number", sa.Integer, nullable=False),
+        sa.Column("title", sa.Text, nullable=True),
+        sa.Column(
+            "is_placeholder", sa.Boolean, nullable=False, server_default="false"
+        ),
+        sa.Column("stated_page_start", sa.Integer, nullable=True),
+        sa.Column("stated_page_end", sa.Integer, nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+    )
+    op.create_index(
+        "ix_series_chapter_series_tag_id", "series_chapter", ["series_tag_id"]
+    )
+
+    # New columns on series_page; chapter_id starts nullable so we can backfill.
+    op.add_column(
+        "series_page", sa.Column("chapter_id", sa.Integer, nullable=True)
+    )
+    op.add_column(
+        "series_page", sa.Column("stated_page", sa.Integer, nullable=True)
+    )
+
+    conn = op.get_bind()
+    # One auto-chapter per existing series (any series_tag_id present in pages).
+    conn.execute(
+        sa.text(
+            "INSERT INTO series_chapter "
+            "(series_tag_id, chapter_number, is_placeholder, created_at, updated_at) "
+            "SELECT DISTINCT series_tag_id, 1, false, now(), now() "
+            "FROM series_page"
+        )
+    )
+    # Point every existing page at its series' auto-chapter.
+    conn.execute(
+        sa.text(
+            "UPDATE series_page sp "
+            "SET chapter_id = sc.id "
+            "FROM series_chapter sc "
+            "WHERE sc.series_tag_id = sp.series_tag_id"
+        )
+    )
+
+    # Now lock chapter_id down: NOT NULL + FK (cascade) + index.
+    op.alter_column("series_page", "chapter_id", nullable=False)
+    op.create_foreign_key(
+        "fk_series_page_chapter_id",
+        "series_page",
+        "series_chapter",
+        ["chapter_id"],
+        ["id"],
+        ondelete="CASCADE",
+    )
+    op.create_index(
+        "ix_series_page_chapter_id", "series_page", ["chapter_id"]
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_series_page_chapter_id", table_name="series_page")
+    op.drop_constraint(
+        "fk_series_page_chapter_id", "series_page", type_="foreignkey"
+    )
+    op.drop_column("series_page", "stated_page")
+    op.drop_column("series_page", "chapter_id")
+    op.drop_index("ix_series_chapter_series_tag_id", table_name="series_chapter")
+    op.drop_table("series_chapter")

alembic/versions/0041_series_suggestions.py

@@ -0,0 +1,98 @@
+"""series suggestions: assisted-continuation matcher (FC-6.3)
+
+Revision ID: 0041
+Revises: 0040
+Create Date: 2026-06-07
+
+A confirm-only queue of "this post may continue this series" hints, plus two
+import_settings knobs (enable + score threshold) for the matcher.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0041"
+down_revision: Union[str, None] = "0040"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "series_suggestion",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column(
+            "post_id",
+            sa.Integer,
+            sa.ForeignKey("post.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column(
+            "series_tag_id",
+            sa.Integer,
+            sa.ForeignKey("tag.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("score", sa.Float, nullable=False),
+        sa.Column("signals", sa.JSON, nullable=True),
+        sa.Column(
+            "status", sa.String(16), nullable=False, server_default="pending"
+        ),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.UniqueConstraint(
+            "post_id", "series_tag_id", name="uq_series_suggestion_post_series"
+        ),
+    )
+    op.create_index(
+        "ix_series_suggestion_post_id", "series_suggestion", ["post_id"]
+    )
+    op.create_index(
+        "ix_series_suggestion_series_tag_id",
+        "series_suggestion",
+        ["series_tag_id"],
+    )
+    op.create_index(
+        "ix_series_suggestion_status", "series_suggestion", ["status"]
+    )
+
+    op.add_column(
+        "import_settings",
+        sa.Column(
+            "series_suggest_enabled",
+            sa.Boolean,
+            nullable=False,
+            server_default=sa.true(),
+        ),
+    )
+    op.add_column(
+        "import_settings",
+        sa.Column(
+            "series_suggest_threshold",
+            sa.Float,
+            nullable=False,
+            server_default="0.5",
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("import_settings", "series_suggest_threshold")
+    op.drop_column("import_settings", "series_suggest_enabled")
+    op.drop_index("ix_series_suggestion_status", table_name="series_suggestion")
+    op.drop_index(
+        "ix_series_suggestion_series_tag_id", table_name="series_suggestion"
+    )
+    op.drop_index("ix_series_suggestion_post_id", table_name="series_suggestion")
+    op.drop_table("series_suggestion")

alembic/versions/0042_series_chapter_stated_part.py

@@ -0,0 +1,32 @@
+"""series chapter stated_part: operator-facing Part N label (FC-6.4)
+
+Revision ID: 0042
+Revises: 0041
+Create Date: 2026-06-07
+
+A chapter's positional chapter_number is auto-managed (rewritten 1..N on
+reorder/delete), so it can't double as the installment number the operator wants
+to type (e.g. a series authored from a post that is Part 2). Add a nullable
+stated_part alongside it — the same split as series_page.page_number (order) vs
+series_page.stated_page (printed number). Nullable; the UI falls back to
+chapter_number when unset.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0042"
+down_revision: Union[str, None] = "0041"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "series_chapter", sa.Column("stated_part", sa.Integer, nullable=True)
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("series_chapter", "stated_part")

alembic/versions/0043_post_attachment_per_post_unique.py

@@ -0,0 +1,62 @@
+"""post_attachment: per-post sha uniqueness (empty-post flood fix)
+
+Revision ID: 0043
+Revises: 0042
+Create Date: 2026-06-08
+
+PostAttachment.sha256 was GLOBALLY unique, so a non-art file the creator attaches
+to many posts (a standard pdf/zip/link-card) only ever got ONE row — on the first
+post — leaving every later post a bare shell (no image, no attachment). The native
+Patreon backfill of Anduo surfaced 1589 such shells (operator-flagged 2026-06-08).
+
+Switch to PER-POST uniqueness: the on-disk blob stays sha-deduped, but each post
+gets its own row. Replace the unique sha256 index with a plain lookup index plus
+two partial uniques — (post_id, sha256) for real posts and (sha256) for the
+NULL-post filesystem case (still one row per file there).
+
+Existing data has ≤1 row per sha (the old global unique), so the new partial
+uniques can't be violated on upgrade — no data backfill needed here. The bare-post
+shells themselves are removed by the separate prune-empty-posts cleanup tool.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0043"
+down_revision: Union[str, None] = "0042"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Drop the global unique index; recreate it as a plain (non-unique) lookup
+    # index so sha-based reads keep their index (matches the model's index=True).
+    op.drop_index("ix_post_attachment_sha256", table_name="post_attachment")
+    op.create_index(
+        "ix_post_attachment_sha256", "post_attachment", ["sha256"],
+    )
+    op.create_index(
+        "uq_post_attachment_post_sha", "post_attachment",
+        ["post_id", "sha256"], unique=True,
+        postgresql_where=sa.text("post_id IS NOT NULL"),
+    )
+    op.create_index(
+        "uq_post_attachment_null_post_sha", "post_attachment",
+        ["sha256"], unique=True,
+        postgresql_where=sa.text("post_id IS NULL"),
+    )
+
+
+def downgrade() -> None:
+    op.drop_index(
+        "uq_post_attachment_null_post_sha", table_name="post_attachment"
+    )
+    op.drop_index(
+        "uq_post_attachment_post_sha", table_name="post_attachment"
+    )
+    op.drop_index("ix_post_attachment_sha256", table_name="post_attachment")
+    op.create_index(
+        "ix_post_attachment_sha256", "post_attachment", ["sha256"],
+        unique=True,
+    )

alembic/versions/0044_ml_settings_tagger_store_floor.py

@@ -0,0 +1,37 @@
+"""ml_settings.tagger_store_floor
+
+The ingest confidence floor below which tagger predictions are not stored,
+promoted from the TAGGER_STORE_FLOOR env var to a DB-backed, UI-tunable
+setting. Default 0.70 (was an env default of 0.05): the suggestion path
+already filters at 0.70 and the centroid/learned path covers low-confidence
+preferred tags, so the sub-0.70 tail was redundant weight — it had grown
+image_record's TOAST to ~100 GB. See plan-task #764.
+
+Revision ID: 0044
+Revises: 0043
+Create Date: 2026-06-10
+
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0044"
+down_revision: Union[str, None] = "0043"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "tagger_store_floor", sa.Float(),
+            nullable=False, server_default="0.7",
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("ml_settings", "tagger_store_floor")

alembic/versions/0045_image_prediction_table.py

@@ -0,0 +1,69 @@
+"""image_prediction table (DDL only — backfill runs as a background task)
+
+Normalizes the per-image tagger predictions out of the JSON blob into a
+queryable table (#768). This migration creates ONLY the table + indexes — it
+is pure DDL and commits instantly, so web boots immediately.
+
+The data backfill from the existing image_record.tagger_predictions JSON is
+deliberately NOT done here. Doing it inline made the whole migration one
+transaction over the ~100 GB TOAST: nothing committed until the very end, it
+was invisible/unmonitorable mid-run, and an early MATERIALIZED-CTE form spilled
+the full 100 GB to temp. Instead the backfill is the
+backend.app.tasks.admin.backfill_image_predictions_task — batched by id window,
+committed per chunk (visible progress + resumable), idempotent
+(ON CONFLICT DO NOTHING). Trigger it from Settings → Maintenance once web is up.
+
+The old image_record.tagger_predictions column is left in place (vestigial) and
+dropped in a follow-up once the backfill + code cutover are verified — dropping
+it needs an ACCESS EXCLUSIVE lock on the hot image_record table (the 0044 lock
+class), so it's deferred to a quiesced-worker window.
+
+Revision ID: 0045
+Revises: 0044
+Create Date: 2026-06-10
+
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0045"
+down_revision: Union[str, None] = "0044"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "image_prediction",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column(
+            "image_record_id", sa.Integer(),
+            sa.ForeignKey("image_record.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("raw_name", sa.String(length=255), nullable=False),
+        sa.Column("category", sa.String(length=64), nullable=False),
+        sa.Column("score", sa.Float(), nullable=False),
+        sa.UniqueConstraint(
+            "image_record_id", "raw_name", name="image_raw_name",
+        ),
+    )
+    op.create_index(
+        "ix_image_prediction_image", "image_prediction", ["image_record_id"],
+    )
+    op.create_index(
+        "ix_image_prediction_name_score", "image_prediction",
+        ["raw_name", "score"],
+    )
+    # No data backfill here — see the module docstring. The one-time copy from
+    # image_record.tagger_predictions runs as backfill_image_predictions_task
+    # (batched, resumable, idempotent), kept out of this transaction so web boots
+    # without waiting on a ~100 GB pass.
+
+
+def downgrade() -> None:
+    op.drop_index("ix_image_prediction_name_score", "image_prediction")
+    op.drop_index("ix_image_prediction_image", "image_prediction")
+    op.drop_table("image_prediction")

alembic/versions/0046_drop_tagger_predictions.py

@@ -0,0 +1,43 @@
+"""drop image_record.tagger_predictions (predictions normalized to image_prediction)
+
+Final step of #768. The per-tag predictions now live in the image_prediction
+table (backfilled from the JSON, read by suggestions + allowlist, written by
+tag_and_embed). The old JSON column is dead weight — and it's the ~100 GB of
+sub-0.70 score tail that bloated image_record's TOAST and broke DB backups
+(#739). Dropping it is a fast catalog change; it does NOT reclaim the disk on
+its own — run `VACUUM FULL image_record` (or pg_repack) afterward, off-hours,
+to return the space to the OS so backups go small.
+
+DROP COLUMN needs a brief ACCESS EXCLUSIVE lock on image_record; env.py's
+lock_timeout guards it, so quiesce the ml-worker if a tagging run is in flight
+(see the migration-lock reference). tagger_model_version is kept — it's the
+"has this been tagged / is it current?" signal the backfill sweep reads.
+
+Revision ID: 0046
+Revises: 0045
+Create Date: 2026-06-11
+
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0046"
+down_revision: Union[str, None] = "0045"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.drop_column("image_record", "tagger_predictions")
+
+
+def downgrade() -> None:
+    # Re-add the column empty. The JSON data is not restored (it lived only in
+    # this column); a downgrade would re-tag or backfill from image_prediction
+    # separately if ever needed.
+    op.add_column(
+        "image_record",
+        sa.Column("tagger_predictions", sa.JSON(), nullable=True),
+    )

alembic/versions/0047_series_chapter_dividers.py

@@ -0,0 +1,175 @@
+"""series chapters become cosmetic dividers; pages become one series-global run
+
+FC-6.x reframe (#789). A series is now ONE flat, series-global ordered run of
+pages; chapters stop owning pages and become labeled dividers anchored to the
+page that begins them.
+
+Migration (order matters — series_page.chapter_id cascades, so it must be
+dropped BEFORE any chapter row is deleted, or pages would cascade away):
+  a. Renumber series_page.page_number to a series-global 1..N (ordered by the
+     OLD (chapter_number, page_number)).
+  b. Add series_chapter.anchor_page_id and populate it with each chapter's first
+     page (lowest new page_number).
+  c. Drop series_page.chapter_id (severs the cascade link).
+  d. Prune chapters that shouldn't become dividers: empty/placeholder ones (no
+     anchor) and the redundant unlabeled chapter that would sit at page 1.
+  e. Reshape series_chapter into the divider: drop chapter_number,
+     is_placeholder, stated_page_start/end; make anchor_page_id NOT NULL +
+     UNIQUE + FK→series_page ON DELETE CASCADE.
+
+Revision ID: 0047
+Revises: 0046
+Create Date: 2026-06-11
+
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0047"
+down_revision: Union[str, None] = "0046"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # a. series-global page numbering, preserving the old reading order.
+    op.execute(
+        """
+        WITH ordered AS (
+            SELECT sp.id,
+                   ROW_NUMBER() OVER (
+                       PARTITION BY sp.series_tag_id
+                       ORDER BY sc.chapter_number, sp.page_number, sp.id
+                   ) AS rn
+            FROM series_page sp
+            JOIN series_chapter sc ON sc.id = sp.chapter_id
+        )
+        UPDATE series_page sp
+        SET page_number = ordered.rn
+        FROM ordered
+        WHERE sp.id = ordered.id
+        """
+    )
+
+    # b. anchor each existing chapter at its first page (lowest new page_number).
+    op.add_column(
+        "series_chapter",
+        sa.Column("anchor_page_id", sa.Integer(), nullable=True),
+    )
+    op.execute(
+        """
+        WITH firsts AS (
+            SELECT DISTINCT ON (sp.chapter_id)
+                   sp.chapter_id, sp.id AS page_id
+            FROM series_page sp
+            ORDER BY sp.chapter_id, sp.page_number, sp.id
+        )
+        UPDATE series_chapter sc
+        SET anchor_page_id = firsts.page_id
+        FROM firsts
+        WHERE firsts.chapter_id = sc.id
+        """
+    )
+
+    # c. sever the ownership link (drops the FK + index with the column) BEFORE
+    #    pruning chapters, so deleting a chapter can't cascade-delete its pages.
+    op.drop_column("series_page", "chapter_id")
+
+    # d. prune chapters that don't become dividers: placeholders / empty ones
+    #    (no anchor), and the unlabeled chapter that would land redundantly at
+    #    page 1 (the series just starts — no divider needed there).
+    op.execute(
+        """
+        DELETE FROM series_chapter sc
+        USING (
+            SELECT sc2.id
+            FROM series_chapter sc2
+            LEFT JOIN series_page sp ON sp.id = sc2.anchor_page_id
+            WHERE sc2.anchor_page_id IS NULL
+               OR (sp.page_number = 1
+                   AND sc2.title IS NULL
+                   AND sc2.stated_part IS NULL)
+        ) gone
+        WHERE sc.id = gone.id
+        """
+    )
+
+    # e. reshape into the divider model.
+    op.drop_column("series_chapter", "chapter_number")
+    op.drop_column("series_chapter", "is_placeholder")
+    op.drop_column("series_chapter", "stated_page_start")
+    op.drop_column("series_chapter", "stated_page_end")
+    op.alter_column("series_chapter", "anchor_page_id", nullable=False)
+    op.create_unique_constraint(
+        "uq_series_chapter_anchor_page", "series_chapter", ["anchor_page_id"]
+    )
+    op.create_foreign_key(
+        "fk_series_chapter_anchor_page",
+        "series_chapter",
+        "series_page",
+        ["anchor_page_id"],
+        ["id"],
+        ondelete="CASCADE",
+    )
+
+
+def downgrade() -> None:
+    # Lossy: dividers can't be reconstructed as owning chapters. Collapse back to
+    # exactly one chapter per series that owns all its pages in order.
+    op.add_column(
+        "series_page", sa.Column("chapter_id", sa.Integer(), nullable=True)
+    )
+    op.drop_constraint(
+        "fk_series_chapter_anchor_page", "series_chapter", type_="foreignkey"
+    )
+    op.drop_constraint(
+        "uq_series_chapter_anchor_page", "series_chapter", type_="unique"
+    )
+    op.drop_column("series_chapter", "anchor_page_id")
+    op.add_column(
+        "series_chapter",
+        sa.Column(
+            "chapter_number", sa.Integer(), nullable=False, server_default="1"
+        ),
+    )
+    op.add_column(
+        "series_chapter",
+        sa.Column(
+            "is_placeholder", sa.Boolean(), nullable=False,
+            server_default="false",
+        ),
+    )
+    op.add_column(
+        "series_chapter",
+        sa.Column("stated_page_start", sa.Integer(), nullable=True),
+    )
+    op.add_column(
+        "series_chapter",
+        sa.Column("stated_page_end", sa.Integer(), nullable=True),
+    )
+    op.execute("DELETE FROM series_chapter")
+    op.execute(
+        """
+        INSERT INTO series_chapter (series_tag_id, chapter_number)
+        SELECT DISTINCT series_tag_id, 1 FROM series_page
+        """
+    )
+    op.execute(
+        """
+        UPDATE series_page sp
+        SET chapter_id = sc.id
+        FROM series_chapter sc
+        WHERE sc.series_tag_id = sp.series_tag_id
+        """
+    )
+    op.alter_column("series_page", "chapter_id", nullable=False)
+    op.create_foreign_key(
+        "fk_series_page_chapter",
+        "series_page",
+        "series_chapter",
+        ["chapter_id"],
+        ["id"],
+        ondelete="CASCADE",
+    )

alembic/versions/0048_series_page_pending_status.py

@@ -0,0 +1,45 @@
+"""series_page pending staging: status + nullable page_number (#789 Phase 2)
+
+Pages added from a post no longer append straight into the run — they land
+'pending' with a NULL page_number, staged grouped by their source post so the
+operator can drop junk (text-free alts, bumpers) and place the keepers into the
+sequence. A page only gets a series-global page_number once it's 'placed'.
+
+Revision ID: 0048
+Revises: 0047
+Create Date: 2026-06-11
+
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0048"
+down_revision: Union[str, None] = "0047"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "series_page",
+        sa.Column(
+            "status", sa.String(length=16), nullable=False,
+            server_default="placed",
+        ),
+    )
+    op.alter_column(
+        "series_page", "page_number",
+        existing_type=sa.Integer(), nullable=True,
+    )
+
+
+def downgrade() -> None:
+    # Lossy: pending pages are unsorted staging rows with no order — drop them.
+    op.execute("DELETE FROM series_page WHERE status = 'pending'")
+    op.alter_column(
+        "series_page", "page_number",
+        existing_type=sa.Integer(), nullable=False,
+    )
+    op.drop_column("series_page", "status")

alembic/versions/0049_external_link_table.py

@@ -0,0 +1,90 @@
+"""external_link table — off-platform file-host links found in post bodies
+
+Creators host the real files on mega.nz / Google Drive / MediaFire / Dropbox /
+Pixeldrain and link them in the post text. This table records each such link
+(so nothing is silently dropped), and doubles as the dedup + dead-letter ledger
+the download worker (a later slice) walks. `url` keeps the FULL link including
+the `#fragment` — mega.nz's decryption key lives there; truncating it makes the
+file undownloadable.
+
+CHECK whitelists for host + status include the full enum up front (incl. the
+download-worker statuses) so the worker slice needs no constraint migration.
+
+Revision ID: 0049
+Revises: 0048
+Create Date: 2026-06-14
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0049"
+down_revision: Union[str, None] = "0048"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "external_link",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column(
+            "post_id", sa.Integer(),
+            sa.ForeignKey("post.id", ondelete="CASCADE"), nullable=False,
+        ),
+        sa.Column(
+            "artist_id", sa.Integer(),
+            sa.ForeignKey("artist.id", ondelete="SET NULL"), nullable=True,
+        ),
+        sa.Column("host", sa.String(length=16), nullable=False),
+        sa.Column("url", sa.Text(), nullable=False),
+        sa.Column("label", sa.Text(), nullable=True),
+        sa.Column(
+            "status", sa.String(length=16), nullable=False,
+            server_default="pending",
+        ),
+        sa.Column("attempts", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("last_error", sa.Text(), nullable=True),
+        sa.Column(
+            "attachment_id", sa.Integer(),
+            sa.ForeignKey("post_attachment.id", ondelete="SET NULL"),
+            nullable=True,
+        ),
+        sa.Column(
+            "created_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column("completed_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column("duration_seconds", sa.Float(), nullable=True),
+        sa.CheckConstraint(
+            "host IN ('mega','gdrive','mediafire','dropbox','pixeldrain')",
+            name="ck_external_link_host",
+        ),
+        sa.CheckConstraint(
+            "status IN ('pending','downloading','downloaded','failed',"
+            "'skipped','dead')",
+            name="ck_external_link_status",
+        ),
+    )
+    op.create_index(
+        "ix_external_link_post_id", "external_link", ["post_id"],
+    )
+    op.create_index(
+        "ix_external_link_artist_id", "external_link", ["artist_id"],
+    )
+    op.create_index(
+        "ix_external_link_status", "external_link", ["status"],
+    )
+    op.create_index(
+        "uq_external_link_post_url", "external_link", ["post_id", "url"],
+        unique=True,
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("uq_external_link_post_url", table_name="external_link")
+    op.drop_index("ix_external_link_status", table_name="external_link")
+    op.drop_index("ix_external_link_artist_id", table_name="external_link")
+    op.drop_index("ix_external_link_post_id", table_name="external_link")
+    op.drop_table("external_link")

alembic/versions/0050_external_link_host_toggles.py

@@ -0,0 +1,38 @@
+"""import_settings: per-host enable toggles for external file-host downloads
+
+Operator levers (#830): disable a single host (e.g. mega.nz when it's
+rate-limiting/banning) without touching the others. The worker reads these via
+getattr and defaults to enabled, so the toggles default TRUE (works out of the
+box, rule #26).
+
+Revision ID: 0050
+Revises: 0049
+Create Date: 2026-06-14
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0050"
+down_revision: Union[str, None] = "0049"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+_HOSTS = ("mega", "gdrive", "mediafire", "dropbox", "pixeldrain")
+
+
+def upgrade() -> None:
+    for host in _HOSTS:
+        op.add_column(
+            "import_settings",
+            sa.Column(
+                f"extdl_{host}_enabled", sa.Boolean(), nullable=False,
+                server_default=sa.true(),
+            ),
+        )
+
+
+def downgrade() -> None:
+    for host in _HOSTS:
+        op.drop_column("import_settings", f"extdl_{host}_enabled")

alembic/versions/0051_image_source_provenance.py

@@ -0,0 +1,38 @@
+"""image_record: source_url + source_filehash (inline-image localization)
+
+#830 Phase 2. To render a post body faithfully we serve LOCAL copies of inline
+images instead of hotlinking the public CDN. The join key between a body
+`<img src=CDN>` and the local file is the CDN's 32-hex filehash (the same
+identity extract_media dedups by). Persist it (indexed) plus the full source
+URL for provenance/debugging. Both NULL for filesystem-imported / pre-existing
+rows — those fall back to hotlinking until re-downloaded.
+
+Revision ID: 0051
+Revises: 0050
+Create Date: 2026-06-14
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0051"
+down_revision: Union[str, None] = "0050"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column("image_record", sa.Column("source_url", sa.Text(), nullable=True))
+    op.add_column(
+        "image_record", sa.Column("source_filehash", sa.String(length=32), nullable=True)
+    )
+    op.create_index(
+        "ix_image_record_source_filehash", "image_record", ["source_filehash"]
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_image_record_source_filehash", table_name="image_record")
+    op.drop_column("image_record", "source_filehash")
+    op.drop_column("image_record", "source_url")

alembic/versions/0052_image_duration_seconds.py

@@ -0,0 +1,32 @@
+"""image_record: duration_seconds (Tier-1 video near-dup key)
+
+#871. Videos previously deduped on sha256 only (pHash is images-only), so a
+different encode/remux of the same video imported as a distinct record. Persist
+the container duration so the importer can treat same-artist videos with matching
+duration (+ aspect ratio) as the same content and dedup/supersede like images.
+NULL for images and for video rows imported before this column existed (a
+backfill re-probes those so they participate in dedup).
+
+Revision ID: 0052
+Revises: 0051
+Create Date: 2026-06-16
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0052"
+down_revision: Union[str, None] = "0051"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "image_record", sa.Column("duration_seconds", sa.Float(), nullable=True)
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("image_record", "duration_seconds")

alembic/versions/0053_ml_settings_video_tagging.py

@@ -0,0 +1,49 @@
+"""ml_settings: video tagging knobs (cadence sampling + noise floor)
+
+#747. Video tag quality/perf: sample frames at a fixed cadence (interval) so a
+tag's frame-presence reflects real screen time, cap total frames so long videos
+stay bounded, and keep a tag only if it appears in >= min_tag_frames sampled
+frames. Operator-tunable via Settings → ML (replaces the VIDEO_ML_FRAMES env var).
+
+Revision ID: 0053
+Revises: 0052
+Create Date: 2026-06-16
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0053"
+down_revision: Union[str, None] = "0052"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "video_frame_interval_seconds", sa.Float(), nullable=False,
+            server_default="4.0",
+        ),
+    )
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "video_max_frames", sa.Integer(), nullable=False, server_default="64",
+        ),
+    )
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "video_min_tag_frames", sa.Integer(), nullable=False,
+            server_default="3",
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("ml_settings", "video_min_tag_frames")
+    op.drop_column("ml_settings", "video_max_frames")
+    op.drop_column("ml_settings", "video_frame_interval_seconds")

alembic/versions/0054_subscribestar_ledgers.py

@@ -0,0 +1,82 @@
+"""subscribestar_seen_media + subscribestar_failed_media: per-source ledgers
+
+Revision ID: 0054
+Revises: 0053
+Create Date: 2026-06-17
+
+SubscribeStar native ingester (phase 1 of the gallery-dl → native-core
+migration). Mirrors the Patreon ledger tables (0037/0038): a seen-ledger so
+routine walks skip already-ingested media (recovery bypasses it) and a
+dead-letter ledger so persistently-failing media stops re-burning backfill
+chunks. `filehash` is a CDN content hash when present, else a synthesized
+``<post_id>:<filename>`` key — hence String(128). UNIQUE (source_id, filehash)
+is the upsert key on each.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0054"
+down_revision: Union[str, None] = "0053"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "subscribestar_seen_media",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column(
+            "source_id",
+            sa.Integer,
+            sa.ForeignKey("source.id", ondelete="CASCADE"),
+            nullable=False,
+            index=True,
+        ),
+        sa.Column("filehash", sa.String(128), nullable=False),
+        sa.Column("post_id", sa.String(64), nullable=True),
+        sa.Column(
+            "seen_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("NOW()"),
+        ),
+        sa.UniqueConstraint(
+            "source_id", "filehash", name="uq_subscribestar_seen_media_source_id"
+        ),
+    )
+    op.create_table(
+        "subscribestar_failed_media",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column(
+            "source_id",
+            sa.Integer,
+            sa.ForeignKey("source.id", ondelete="CASCADE"),
+            nullable=False,
+            index=True,
+        ),
+        sa.Column("filehash", sa.String(128), nullable=False),
+        sa.Column("attempts", sa.Integer, nullable=False, server_default="1"),
+        sa.Column("last_error", sa.Text, nullable=True),
+        sa.Column(
+            "first_failed_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("NOW()"),
+        ),
+        sa.Column(
+            "last_failed_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("NOW()"),
+        ),
+        sa.UniqueConstraint(
+            "source_id", "filehash", name="uq_subscribestar_failed_media_source_id"
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("subscribestar_failed_media")
+    op.drop_table("subscribestar_seen_media")

alembic/versions/0055_image_provenance_from_attachment.py

@@ -0,0 +1,55 @@
+"""image_provenance: from_attachment_id (which archive an image was extracted from)
+
+Milestone #87. When an image is pulled out of a .zip/.rar, record WHICH archive
+PostAttachment it came from, so the provenance UI can show the single archive a
+file lives inside instead of every attachment on the post. Nullable FK with
+ON DELETE SET NULL — a loose (non-archive) download leaves it NULL, and deleting
+the archive attachment forgets the linkage without destroying the (image, post)
+provenance edge. Existing rows are NULL until the reextract backfill stamps them.
+
+Revision ID: 0055
+Revises: 0054
+Create Date: 2026-06-22
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0055"
+down_revision: Union[str, None] = "0054"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "image_provenance",
+        sa.Column("from_attachment_id", sa.Integer(), nullable=True),
+    )
+    op.create_index(
+        "ix_image_provenance_from_attachment_id",
+        "image_provenance",
+        ["from_attachment_id"],
+    )
+    op.create_foreign_key(
+        "fk_image_provenance_from_attachment",
+        "image_provenance",
+        "post_attachment",
+        ["from_attachment_id"],
+        ["id"],
+        ondelete="SET NULL",
+    )
+
+
+def downgrade() -> None:
+    op.drop_constraint(
+        "fk_image_provenance_from_attachment",
+        "image_provenance",
+        type_="foreignkey",
+    )
+    op.drop_index(
+        "ix_image_provenance_from_attachment_id",
+        table_name="image_provenance",
+    )
+    op.drop_column("image_provenance", "from_attachment_id")

alembic/versions/0056_tag_eval_run.py

@@ -0,0 +1,43 @@
+"""tag_eval_run: persisted head-vs-centroid tagging eval runs (#1130)
+
+Milestone #114 slice 1. A long ml-queue eval whose full report must SURVIVE
+navigation, so the run + report live in a row the admin card rehydrates from
+(mirrors library_audit_run). running -> ready / error.
+
+Revision ID: 0056
+Revises: 0055
+Create Date: 2026-06-28
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects.postgresql import JSONB
+
+revision: str = "0056"
+down_revision: Union[str, None] = "0055"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "tag_eval_run",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column("params", JSONB(), nullable=False),
+        sa.Column("status", sa.String(length=16), nullable=False, server_default="running"),
+        sa.Column(
+            "started_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column("finished_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column("report", JSONB(), nullable=True),
+        sa.Column("error", sa.Text(), nullable=True),
+        sa.Column("last_progress_at", sa.DateTime(timezone=True), nullable=True),
+    )
+    op.create_index("ix_tag_eval_run_status", "tag_eval_run", ["status"])
+
+
+def downgrade() -> None:
+    op.drop_index("ix_tag_eval_run_status", table_name="tag_eval_run")
+    op.drop_table("tag_eval_run")

alembic/versions/0057_tag_positive_confirmation.py

@@ -0,0 +1,40 @@
+"""tag_positive_confirmation: operator-affirmed correct positives (#1130)
+
+Mirror of tag_suggestion_rejection. "Keep" on a doubted positive records here so
+the eval's doubts list stops resurfacing confirmed-correct images every run.
+
+Revision ID: 0057
+Revises: 0056
+Create Date: 2026-06-28
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0057"
+down_revision: Union[str, None] = "0056"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "tag_positive_confirmation",
+        sa.Column(
+            "image_record_id", sa.Integer(),
+            sa.ForeignKey("image_record.id", ondelete="CASCADE"), primary_key=True,
+        ),
+        sa.Column(
+            "tag_id", sa.Integer(),
+            sa.ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True, index=True,
+        ),
+        sa.Column(
+            "confirmed_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("tag_positive_confirmation")

alembic/versions/0058_tag_head.py

@@ -0,0 +1,95 @@
+"""tag_head + head_training_run: production heads that learn from tags (#114)
+
+The eval (#1130) proved the frozen-embedding + trained-head spine; this lands its
+production form. tag_head stores one logistic-regression head per concept (the
+new suggestion source, replacing Camie + centroid); head_training_run tracks the
+batch that (re)trains them. Adds two head-training tunables to ml_settings.
+
+Revision ID: 0058
+Revises: 0057
+Create Date: 2026-06-28
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from pgvector.sqlalchemy import Vector
+from sqlalchemy.dialects.postgresql import JSONB
+
+revision: str = "0058"
+down_revision: Union[str, None] = "0057"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+_HEAD_DIM = 1152
+
+
+def upgrade() -> None:
+    op.create_table(
+        "tag_head",
+        sa.Column(
+            "tag_id", sa.Integer(),
+            sa.ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True,
+        ),
+        sa.Column("embedding_version", sa.String(length=128), nullable=False),
+        sa.Column("weights", Vector(_HEAD_DIM), nullable=False),
+        sa.Column("bias", sa.Float(), nullable=False),
+        sa.Column("suggest_threshold", sa.Float(), nullable=False),
+        sa.Column("auto_apply_threshold", sa.Float(), nullable=True),
+        sa.Column("n_pos", sa.Integer(), nullable=False),
+        sa.Column("n_neg", sa.Integer(), nullable=False),
+        sa.Column("ap", sa.Float(), nullable=False),
+        sa.Column("precision_cv", sa.Float(), nullable=False),
+        sa.Column("recall", sa.Float(), nullable=False),
+        sa.Column(
+            "trained_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column("metrics", JSONB(), nullable=True),
+    )
+
+    op.create_table(
+        "head_training_run",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column("params", JSONB(), nullable=False),
+        sa.Column(
+            "status", sa.String(length=16), nullable=False,
+            server_default="running",
+        ),
+        sa.Column(
+            "started_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column("finished_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column("n_trained", sa.Integer(), nullable=True),
+        sa.Column("n_skipped", sa.Integer(), nullable=True),
+        sa.Column("error", sa.Text(), nullable=True),
+        sa.Column("last_progress_at", sa.DateTime(timezone=True), nullable=True),
+    )
+    op.create_index(
+        "ix_head_training_run_status", "head_training_run", ["status"],
+    )
+
+    # Head-training tunables on the ml_settings singleton.
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "head_min_positives", sa.Integer(), nullable=False,
+            server_default="8",
+        ),
+    )
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "head_auto_apply_precision", sa.Float(), nullable=False,
+            server_default="0.97",
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("ml_settings", "head_auto_apply_precision")
+    op.drop_column("ml_settings", "head_min_positives")
+    op.drop_index("ix_head_training_run_status", table_name="head_training_run")
+    op.drop_table("head_training_run")
+    op.drop_table("tag_head")

alembic/versions/0059_head_auto_apply.py

@@ -0,0 +1,70 @@
+"""head_auto_apply_run + earned-auto-apply settings (#114)
+
+A graduated head can apply its tag without a human, gated by a master switch +
+a support floor. head_auto_apply_run tracks each sweep / dry-run preview.
+
+Revision ID: 0059
+Revises: 0058
+Create Date: 2026-06-29
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects.postgresql import JSONB
+
+revision: str = "0059"
+down_revision: Union[str, None] = "0058"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "head_auto_apply_run",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column(
+            "dry_run", sa.Boolean(), nullable=False, server_default=sa.false()
+        ),
+        sa.Column("params", JSONB(), nullable=False),
+        sa.Column(
+            "status", sa.String(length=16), nullable=False,
+            server_default="running",
+        ),
+        sa.Column(
+            "started_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column("finished_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column("n_applied", sa.Integer(), nullable=True),
+        sa.Column("report", JSONB(), nullable=True),
+        sa.Column("error", sa.Text(), nullable=True),
+        sa.Column("last_progress_at", sa.DateTime(timezone=True), nullable=True),
+    )
+    op.create_index(
+        "ix_head_auto_apply_run_status", "head_auto_apply_run", ["status"],
+    )
+
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "head_auto_apply_enabled", sa.Boolean(), nullable=False,
+            server_default=sa.true(),  # opt-out: on by default (operator-asked)
+        ),
+    )
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "head_auto_apply_min_positives", sa.Integer(), nullable=False,
+            server_default="30",
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("ml_settings", "head_auto_apply_min_positives")
+    op.drop_column("ml_settings", "head_auto_apply_enabled")
+    op.drop_index(
+        "ix_head_auto_apply_run_status", table_name="head_auto_apply_run"
+    )
+    op.drop_table("head_auto_apply_run")

alembic/versions/0060_head_metrics.py

@@ -0,0 +1,74 @@
+"""head_metric + head_metrics_snapshot: auto-apply observability (#114)
+
+Running misfire/under-fire counters per concept (captured at correction time,
+since image_tag.source is lost on delete) + a daily per-concept time-series so
+the operator can tune the precision target + support floor from real data.
+
+Revision ID: 0060
+Revises: 0059
+Create Date: 2026-06-29
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0060"
+down_revision: Union[str, None] = "0059"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "head_metric",
+        sa.Column(
+            "tag_id", sa.Integer(),
+            sa.ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True,
+        ),
+        sa.Column("n_misfires", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("n_underfires", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column(
+            "updated_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+    )
+
+    op.create_table(
+        "head_metrics_snapshot",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column(
+            "tag_id", sa.Integer(),
+            sa.ForeignKey("tag.id", ondelete="CASCADE"),
+        ),
+        sa.Column("name", sa.String(length=255), nullable=False),
+        sa.Column(
+            "snapshot_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column("n_auto_applied", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("n_misfires", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("n_underfires", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("ap", sa.Float(), nullable=True),
+        sa.Column("precision_cv", sa.Float(), nullable=True),
+        sa.Column("recall", sa.Float(), nullable=True),
+        sa.Column("n_pos", sa.Integer(), nullable=True),
+    )
+    op.create_index(
+        "ix_head_metrics_snapshot_tag_id", "head_metrics_snapshot", ["tag_id"],
+    )
+    op.create_index(
+        "ix_head_metrics_snapshot_snapshot_at", "head_metrics_snapshot",
+        ["snapshot_at"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index(
+        "ix_head_metrics_snapshot_snapshot_at", table_name="head_metrics_snapshot"
+    )
+    op.drop_index(
+        "ix_head_metrics_snapshot_tag_id", table_name="head_metrics_snapshot"
+    )
+    op.drop_table("head_metrics_snapshot")
+    op.drop_table("head_metric")

alembic/versions/0061_image_region.py

@@ -0,0 +1,59 @@
+"""image_region: detected/proposed regions + their crop embeddings (#114)
+
+Storage backbone of the crop pipeline. A region = normalized bbox + the crop's
+embedding (CCIP for face/figure → character id; SigLIP for concept regions →
+head bag-of-embeddings). Also serves as grounded-tag bbox provenance.
+
+Revision ID: 0061
+Revises: 0060
+Create Date: 2026-06-29
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from pgvector.sqlalchemy import Vector
+
+revision: str = "0061"
+down_revision: Union[str, None] = "0060"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+_CCIP_DIM = 768
+_SIGLIP_DIM = 1152
+
+
+def upgrade() -> None:
+    op.create_table(
+        "image_region",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column(
+            "image_record_id", sa.Integer(),
+            sa.ForeignKey("image_record.id", ondelete="CASCADE"), nullable=False,
+        ),
+        sa.Column("kind", sa.String(length=16), nullable=False),
+        # Video/animated: source frame timestamp (seconds); NULL for stills.
+        sa.Column("frame_time", sa.Float(), nullable=True),
+        sa.Column("rx", sa.Float(), nullable=False),
+        sa.Column("ry", sa.Float(), nullable=False),
+        sa.Column("rw", sa.Float(), nullable=False),
+        sa.Column("rh", sa.Float(), nullable=False),
+        sa.Column("score", sa.Float(), nullable=True),
+        sa.Column("detector_version", sa.String(length=64), nullable=True),
+        sa.Column("crop_version", sa.String(length=64), nullable=True),
+        sa.Column("embedding_version", sa.String(length=128), nullable=True),
+        sa.Column("ccip_embedding", Vector(_CCIP_DIM), nullable=True),
+        sa.Column("siglip_embedding", Vector(_SIGLIP_DIM), nullable=True),
+        sa.Column(
+            "created_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+    )
+    op.create_index(
+        "ix_image_region_image_record_id", "image_region", ["image_record_id"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_image_region_image_record_id", table_name="image_region")
+    op.drop_table("image_region")

alembic/versions/0062_gpu_job.py

@@ -0,0 +1,55 @@
+"""gpu_job: the HTTP-leased GPU work queue for the desktop agent (#114)
+
+The agent stays HTTP-only — the server enqueues per-(image, task) jobs here and
+the agent leases/submits over the web API; Redis/Postgres stay private.
+
+Revision ID: 0062
+Revises: 0061
+Create Date: 2026-06-29
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0062"
+down_revision: Union[str, None] = "0061"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "gpu_job",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column(
+            "image_record_id", sa.Integer(),
+            sa.ForeignKey("image_record.id", ondelete="CASCADE"), nullable=False,
+        ),
+        sa.Column("task", sa.String(length=32), nullable=False),
+        sa.Column(
+            "status", sa.String(length=16), nullable=False,
+            server_default="pending",
+        ),
+        sa.Column("lease_token", sa.String(length=64), nullable=True),
+        sa.Column("leased_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column("lease_expires_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column("attempts", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("error", sa.Text(), nullable=True),
+        sa.Column(
+            "created_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column(
+            "updated_at", sa.DateTime(timezone=True), nullable=False,
+            server_default=sa.func.now(),
+        ),
+    )
+    op.create_index("ix_gpu_job_image_record_id", "gpu_job", ["image_record_id"])
+    op.create_index("ix_gpu_job_status", "gpu_job", ["status"])
+
+
+def downgrade() -> None:
+    op.drop_index("ix_gpu_job_status", table_name="gpu_job")
+    op.drop_index("ix_gpu_job_image_record_id", table_name="gpu_job")
+    op.drop_table("gpu_job")

alembic/versions/0063_ccip_match_threshold.py

@@ -0,0 +1,33 @@
+"""ml_settings.ccip_match_threshold — tunable CCIP character-match cut (#114)
+
+The v1 matcher used a flat 0.75 cosine; live data showed that over-fires (a
+high-reference character matched a scatter of images). 0.85 keeps the confident
+single-character matches and drops the noise. Tunable from the GPU agent card.
+
+Revision ID: 0063
+Revises: 0062
+Create Date: 2026-06-29
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0063"
+down_revision: Union[str, None] = "0062"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "ccip_match_threshold", sa.Float(), nullable=False,
+            server_default="0.85",
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("ml_settings", "ccip_match_threshold")

alembic/versions/0064_ccip_auto_apply.py

@@ -0,0 +1,42 @@
+"""ml_settings: CCIP auto-apply switch + threshold (#114)
+
+Confident CCIP character matches auto-tag (source='ccip_auto') on a daily sweep,
+so identity tags keep flowing without pressing a button. ON by default (opt-out,
+like head auto-apply); the high threshold (0.92, above the 0.85 suggest cut) +
+single-character references keep it safe, and every auto-tag is reversible.
+
+Revision ID: 0064
+Revises: 0063
+Create Date: 2026-06-30
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0064"
+down_revision: Union[str, None] = "0063"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "ccip_auto_apply_enabled", sa.Boolean(), nullable=False,
+            server_default=sa.true(),
+        ),
+    )
+    op.add_column(
+        "ml_settings",
+        sa.Column(
+            "ccip_auto_apply_threshold", sa.Float(), nullable=False,
+            server_default="0.92",
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("ml_settings", "ccip_auto_apply_threshold")
+    op.drop_column("ml_settings", "ccip_auto_apply_enabled")

backend/app/api/__init__.py

@@ -20,11 +20,14 @@ def all_blueprints() -> list[Blueprint]:
     from .artist import artist_bp
     from .artists import artists_bp
     from .attachments import attachments_bp
+    from .ccip import ccip_bp
     from .cleanup import cleanup_bp
     from .credentials import credentials_bp
     from .downloads import downloads_bp
     from .extension import extension_bp
     from .gallery import gallery_bp
+    from .gpu import gpu_bp
+    from .heads import heads_bp
     from .import_admin import import_admin_bp
     from .ml_admin import ml_admin_bp
     from .platforms import platforms_bp
@@ -36,6 +39,7 @@ def all_blueprints() -> list[Blueprint]:
     from .suggestions import suggestions_bp
     from .system_activity import system_activity_bp
     from .system_backup import system_backup_bp
+    from .tag_eval import tag_eval_bp
     from .tags import tags_bp
     from .thumbnails import thumbnails_bp
     return [
@@ -56,6 +60,10 @@ def all_blueprints() -> list[Blueprint]:
         suggestions_bp,
         allowlist_bp,
         aliases_bp,
+        tag_eval_bp,
+        heads_bp,
+        gpu_bp,
+        ccip_bp,
         ml_admin_bp,
         thumbnails_bp,
         sources_bp,

backend/app/api/admin.py

@@ -6,6 +6,7 @@ Five action surfaces:
   DELETE /api/admin/tags/<int:tag_id>              (Tier B)
   POST   /api/admin/tags/<int:dest_id>/merge       (Tier B)
   POST   /api/admin/tags/prune-unused              (Tier A)
+  POST   /api/admin/posts/prune-bare               (Tier A)
   POST   /api/admin/tags/purge-legacy              (Tier A)
   GET    /api/admin/tags/<int:tag_id>/usage-count  (helper)
 
@@ -19,7 +20,7 @@ from __future__ import annotations
 import hashlib
 
 from quart import Blueprint, jsonify, request
-from sqlalchemy import select
+from sqlalchemy import select, text
 
 from ..extensions import get_session
 from ..models import Artist
@@ -38,6 +39,31 @@ def _bulk_image_confirm_token(image_ids: list[int]) -> str:
     return digest[:8]
 
 
+async def _run_dry_run_op(service_fn, **service_kwargs):
+    """Shared body for the Tier-A dry-run/apply endpoints: read the `dry_run`
+    flag, run the cleanup_service predicate under `run_sync`, and return its
+    result dict. The SAME `service_fn` drives both preview and apply (the flag
+    just toggles), so a handler physically can't let its preview diverge from
+    its delete (rule 93). Default False preserves the existing contract — the UI
+    always passes `dry_run` explicitly (true to preview, false to apply). Extra
+    service kwargs (e.g. `source_id`) pass straight through."""
+    body = await request.get_json(silent=True) or {}
+    dry_run = bool(body.get("dry_run", False))
+    async with get_session() as session:
+        result = await session.run_sync(
+            lambda sync_sess: service_fn(sync_sess, dry_run=dry_run, **service_kwargs)
+        )
+    return jsonify(result)
+
+
+def _queued(async_result):
+    """Standard 202 for an operator-triggered maintenance task: hand the UI the
+    Celery task id so it can tail /maintenance/task-result (or the activity
+    dashboard) for the summary. (trigger_vacuum stays bespoke — the UI doesn't
+    poll it, so it returns no task id.)"""
+    return jsonify({"task_id": async_result.id, "status": "queued"}), 202
+
+
 @admin_bp.route("/artists/<slug>/cascade-delete", methods=["POST"])
 async def artist_cascade_delete(slug: str):
     body = await request.get_json(silent=True) or {}
@@ -145,6 +171,30 @@ async def tag_merge(dest_id: int):
     if not isinstance(source_id, int) or source_id == dest_id:
         return _bad("invalid_source_id", detail="source_id must be int and differ from dest")
 
+    # dry_run: non-mutating preview (counts + sample) so the operator can
+    # confirm the target before the irreversible merge (#8, rule 93 parity).
+    if body.get("dry_run"):
+        async with get_session() as session:
+            try:
+                p = await TagService(session).merge_preview(
+                    source_id=source_id, target_id=dest_id,
+                )
+            except TagValidationError as exc:
+                return _bad("tag_not_found", status=404, detail=str(exc))
+        return jsonify({
+            "preview": {
+                "source_id": p.source_id, "source_name": p.source_name,
+                "target_id": p.target_id, "target_name": p.target_name,
+                "compatible": p.compatible,
+                "images_moving": p.images_moving,
+                "images_already_on_target": p.images_already_on_target,
+                "source_total": p.source_total,
+                "series_pages": p.series_pages,
+                "will_alias": p.will_alias,
+                "sample_thumbnails": p.sample_thumbnails,
+            },
+        })
+
     async with get_session() as session:
         try:
             result = await TagService(session).merge(
@@ -192,16 +242,39 @@ async def tags_prune_unused():
     re-call with dry_run=false."""
     from ..services.cleanup_service import prune_unused_tags
 
-    body = await request.get_json(silent=True) or {}
-    dry_run = bool(body.get("dry_run", False))
+    return await _run_dry_run_op(prune_unused_tags)
 
-    async with get_session() as session:
-        result = await session.run_sync(
-            lambda sync_sess: prune_unused_tags(
-                sync_sess, dry_run=dry_run,
-            )
-        )
-    return jsonify(result)
+
+@admin_bp.route("/posts/prune-bare", methods=["POST"])
+async def posts_prune_bare():
+    """Tier-A: delete bare posts — Post rows with no linked images (primary OR
+    provenance) and no attachments. Dry-run preview list IS the prompt: UI calls
+    with dry_run=true first, shows the count + sample, operator confirms by
+    re-calling with dry_run=false. Same preview/apply-parity predicate as the
+    prune itself, so the preview can't diverge from the delete."""
+    from ..services.cleanup_service import prune_bare_posts
+
+    return await _run_dry_run_op(prune_bare_posts)
+
+
+@admin_bp.route("/posts/reconcile-duplicates", methods=["POST"])
+async def posts_reconcile_duplicates():
+    """Tier-A: unify duplicate post rows for the same real post — the gallery-dl
+    (attachment-id) + native (post-id) duplicates — onto ONE post-id-keyed keeper,
+    moving image/provenance/attachment/link rows over. Images are untouched.
+    dry_run=true returns {groups, posts_to_merge, sample}; dry_run=false applies
+    and returns {groups, merged, sample}. Optional source_id scopes to one source.
+    Same find_duplicate_post_groups predicate drives preview + apply (rule 93)."""
+    from ..services.cleanup_service import reconcile_duplicate_posts
+
+    body = await request.get_json(silent=True) or {}
+    raw_source = body.get("source_id")
+    try:
+        source_id = int(raw_source) if raw_source is not None else None
+    except (TypeError, ValueError):
+        return _bad("invalid_source_id", detail="source_id must be an integer")
+
+    return await _run_dry_run_op(reconcile_duplicate_posts, source_id=source_id)
 
 
 @admin_bp.route("/tags/purge-legacy", methods=["POST"])
@@ -214,11 +287,161 @@ async def tags_purge_legacy():
     operator confirms with dry_run=false."""
     from ..services.cleanup_service import purge_legacy_tags
 
-    body = await request.get_json(silent=True) or {}
-    dry_run = bool(body.get("dry_run", False))
+    return await _run_dry_run_op(purge_legacy_tags)
 
+
+@admin_bp.route("/tags/reset-content", methods=["POST"])
+async def tags_reset_content():
+    """Tier-A: delete ALL general + character tags (the Camie-suggestable
+    content vocabulary) so the operator can re-tag from scratch via
+    auto-suggest. fandom + series tags + series_page ordering are preserved,
+    and image_prediction rows are untouched so suggestions repopulate.
+    dry-run preview returns per-kind counts + applications + a sample so the
+    UI shows exactly what'll go before the operator confirms (dry_run=false).
+    Irreversible except via DB backup restore."""
+    from ..services.cleanup_service import reset_content_tagging
+
+    return await _run_dry_run_op(reset_content_tagging)
+
+
+@admin_bp.route("/tags/normalize", methods=["POST"])
+async def tags_normalize():
+    """#714: retro-normalize existing tags to the #701 canonical form (Title
+    Case + collapsed whitespace) and merge case/whitespace-variant duplicates.
+
+    dry_run=true (default) returns a projection inline — group/collision/rename
+    counts + a sample of the changes — so the UI shows exactly what'll happen.
+    dry_run=false dispatches the long-running maintenance task (the merge FK
+    repoints can touch many tags); the UI tails the activity dashboard for the
+    summary. Idempotent; back up first (the merges are irreversible)."""
+    from ..services.tag_service import normalize_existing_tags
+
+    body = await request.get_json(silent=True) or {}
+    dry_run = bool(body.get("dry_run", True))
+
+    if dry_run:
+        async with get_session() as session:
+            result = await normalize_existing_tags(session, dry_run=True)
+        return jsonify(result)
+
+    from ..tasks.admin import normalize_tags_task
+
+    async_result = normalize_tags_task.delay()
+    return _queued(async_result)
+
+
+@admin_bp.route("/maintenance/db-stats", methods=["GET"])
+async def db_stats():
+    """Per-table bloat readout (pg_stat_user_tables) for the high-churn tables
+    so the operator can see when a VACUUM is worth running."""
+    from ..tasks.maintenance import VACUUM_TABLES
+
+    wanted = set(VACUUM_TABLES)
     async with get_session() as session:
-        result = await session.run_sync(
-            lambda sync_sess: purge_legacy_tags(sync_sess, dry_run=dry_run)
-        )
-    return jsonify(result)
+        rows = (await session.execute(text(
+            "SELECT relname, n_live_tup, n_dead_tup, last_vacuum, "
+            "last_autovacuum, last_analyze FROM pg_stat_user_tables"
+        ))).all()
+
+    def _iso(v):
+        return v.isoformat() if v is not None else None
+
+    out = []
+    for r in rows:
+        if r.relname not in wanted:
+            continue
+        live = r.n_live_tup or 0
+        dead = r.n_dead_tup or 0
+        total = live + dead
+        out.append({
+            "table": r.relname,
+            "live": live,
+            "dead": dead,
+            "dead_pct": round(100 * dead / total, 1) if total else 0.0,
+            "last_vacuum": _iso(r.last_vacuum),
+            "last_autovacuum": _iso(r.last_autovacuum),
+            "last_analyze": _iso(r.last_analyze),
+        })
+    out.sort(key=lambda t: t["dead"], reverse=True)
+    return jsonify({"tables": out})
+
+
+@admin_bp.route("/maintenance/vacuum", methods=["POST"])
+async def trigger_vacuum():
+    """Operator-triggered VACUUM (ANALYZE) over the high-churn tables — the
+    same maintenance-queue task the weekly Beat schedule runs."""
+    from ..tasks.maintenance import vacuum_analyze
+
+    vacuum_analyze.delay()
+    return jsonify({"status": "queued"}), 202
+
+
+@admin_bp.route("/maintenance/reextract-archives", methods=["POST"])
+async def trigger_reextract_archives():
+    """Operator-triggered re-extract (#713): PostAttachments that are actually
+    archives but were filed opaquely (pre magic-byte gate) get extracted and
+    their members linked to the post. Idempotent; runs on the maintenance queue."""
+    from ..tasks.admin import reextract_archive_attachments_task
+
+    async_result = reextract_archive_attachments_task.delay()
+    return _queued(async_result)
+
+
+@admin_bp.route("/maintenance/prune-missing-files", methods=["POST"])
+async def trigger_prune_missing_files():
+    """Operator-triggered orphan repair (#859): delete ImageRecords whose backing
+    file is gone from disk (e.g. left by the external-attach unlink bug), so they
+    stop 404-ing on playback. The task aborts WITHOUT deleting if a large fraction
+    of files look missing (a filesystem/NFS stall). Maintenance queue;
+    operator-triggered only — never an unattended sweep."""
+    from ..tasks.admin import prune_missing_file_records_task
+
+    async_result = prune_missing_file_records_task.delay()
+    return _queued(async_result)
+
+
+@admin_bp.route("/maintenance/dedup-videos", methods=["POST"])
+async def trigger_dedup_videos():
+    """Tier-1 video dedup (#871). Body {"dry_run": bool}: dry_run=true previews
+    what would be removed (groups / redundant count / reclaimable bytes) WITHOUT
+    deleting; dry_run=false applies it (re-link posts to the keeper, then delete
+    the redundant copies). Either way it first re-probes NULL-duration videos so
+    the existing library participates. Returns the Celery task id — poll
+    /maintenance/task-result/<id> for the summary."""
+    from ..tasks.admin import dedup_videos_task
+
+    body = await request.get_json(silent=True) or {}
+    dry_run = bool(body.get("dry_run", True))  # default to the SAFE preview
+    async_result = dedup_videos_task.delay(dry_run=dry_run)
+    return _queued(async_result)
+
+
+@admin_bp.route("/maintenance/purge-gated-previews", methods=["POST"])
+async def trigger_purge_gated_previews():
+    """Cleanup (#874 follow-up). Body {"dry_run": bool}: dry_run=true previews how
+    many blurred locked-preview images (grabbed from tier-gated Patreon posts
+    before the fix) would be removed WITHOUT deleting; dry_run=false applies it.
+    Re-walks every enabled Patreon source read-only and matches by content hash, so
+    real content downloaded when access existed is provably spared. Returns the
+    Celery task id — poll /maintenance/task-result/<id> for the summary."""
+    from ..tasks.admin import purge_gated_previews_task
+
+    body = await request.get_json(silent=True) or {}
+    dry_run = bool(body.get("dry_run", True))  # default to the SAFE preview
+    async_result = purge_gated_previews_task.delay(dry_run=dry_run)
+    return _queued(async_result)
+
+
+@admin_bp.route("/maintenance/task-result/<task_id>", methods=["GET"])
+async def maintenance_task_result(task_id: str):
+    """Poll a maintenance Celery task's result (the summary dict it returns).
+    Used by the video-dedup card to show the dry-run projection before apply."""
+    from ..celery_app import celery
+
+    res = celery.AsyncResult(task_id)
+    ready = res.ready()
+    return jsonify({
+        "ready": ready,
+        "successful": res.successful() if ready else None,
+        "result": res.result if (ready and res.successful()) else None,
+    })

backend/app/api/allowlist.py

@@ -20,12 +20,37 @@ async def list_allowlist():
                 "tag_name": r.tag_name,
                 "tag_kind": r.tag_kind,
                 "min_confidence": r.min_confidence,
+                "applied_count": r.applied_count,
+                "coverage_count": r.coverage_count,
             }
             for r in rows
         ]
     )
 
 
+@allowlist_bp.route("/tags/<int:tag_id>/allowlist/coverage", methods=["GET"])
+async def coverage(tag_id: int):
+    """Live "at threshold T, a sweep would cover ~N images" projection for the
+    allowlist tuning dashboard. Defaults to the tag's stored threshold."""
+    raw = request.args.get("threshold")
+    async with get_session() as session:
+        svc = AllowlistService(session)
+        if raw is not None:
+            try:
+                threshold = float(raw)
+            except ValueError:
+                return jsonify({"error": "threshold must be a float"}), 400
+            if not (0 < threshold <= 1):
+                return jsonify({"error": "threshold must be in (0, 1]"}), 400
+        else:
+            row = await session.get(TagAllowlist, tag_id)
+            if row is None:
+                return jsonify({"error": "not on allowlist"}), 404
+            threshold = row.min_confidence
+        count = await svc.coverage(tag_id, threshold)
+    return jsonify({"count": count, "threshold": threshold})
+
+
 @allowlist_bp.route("/tags/<int:tag_id>/allowlist", methods=["GET"])
 async def get_one(tag_id: int):
     async with get_session() as session:

backend/app/api/ccip.py

@@ -0,0 +1,124 @@
+"""CCIP / region observability API (#114) — read-only, analysis-shaped.
+
+So the work can be checked through an API as the agent fills in vectors: overall
+coverage (regions by kind, how many images have figure CCIP vectors, which
+characters have enough reference examples to match on) + a per-image drill-down
+(its regions + the CCIP character matches it would get). Mirrors the heads
+metrics endpoint; no GPU, just reads what's stored.
+"""
+
+from quart import Blueprint, jsonify
+from sqlalchemy import distinct, func, select
+
+from ..extensions import get_session
+from ..models import ImageRegion, Tag, TagKind
+from ..models.tag import image_tag
+from ..services.ml.ccip import match_image
+
+ccip_bp = Blueprint("ccip", __name__, url_prefix="/api/ccip")
+
+_FIGURE_KINDS = ("face", "figure")
+
+
+@ccip_bp.route("/overview", methods=["GET"])
+async def overview():
+    async with get_session() as session:
+        by_kind = dict(
+            (
+                await session.execute(
+                    select(ImageRegion.kind, func.count()).group_by(ImageRegion.kind)
+                )
+            ).all()
+        )
+        images_with_figure_ccip = (
+            await session.execute(
+                select(func.count(distinct(ImageRegion.image_record_id)))
+                .where(ImageRegion.kind.in_(_FIGURE_KINDS))
+                .where(ImageRegion.ccip_embedding.is_not(None))
+            )
+        ).scalar_one()
+        # Concept-crop (SigLIP bag) coverage — how far the back-catalogue embed
+        # has progressed, so the max-over-bag scorer's reach is checkable.
+        images_with_concept_siglip = (
+            await session.execute(
+                select(func.count(distinct(ImageRegion.image_record_id)))
+                .where(ImageRegion.kind == "concept")
+                .where(ImageRegion.siglip_embedding.is_not(None))
+            )
+        ).scalar_one()
+        # Per-character reference counts (no vectors loaded) — which characters
+        # have enough examples to match on.
+        ref_rows = (
+            await session.execute(
+                select(image_tag.c.tag_id, Tag.name, func.count())
+                .select_from(ImageRegion)
+                .join(
+                    image_tag,
+                    image_tag.c.image_record_id == ImageRegion.image_record_id,
+                )
+                .join(Tag, Tag.id == image_tag.c.tag_id)
+                .where(Tag.kind == TagKind.character)
+                .where(ImageRegion.kind.in_(_FIGURE_KINDS))
+                .where(ImageRegion.ccip_embedding.is_not(None))
+                .group_by(image_tag.c.tag_id, Tag.name)
+                .order_by(func.count().desc())
+            )
+        ).all()
+        versions = [
+            v for (v,) in (
+                await session.execute(
+                    select(distinct(ImageRegion.embedding_version))
+                )
+            ).all() if v
+        ]
+        auto_applied = (
+            await session.execute(
+                select(func.count()).select_from(image_tag).where(
+                    image_tag.c.source == "ccip_auto"
+                )
+            )
+        ).scalar_one()
+    return jsonify({
+        "regions_by_kind": by_kind,
+        "images_with_figure_ccip": images_with_figure_ccip,
+        "images_with_concept_siglip": images_with_concept_siglip,
+        "characters_with_references": len(ref_rows),
+        "character_references": [
+            {"tag_id": t, "name": n, "n_refs": c} for (t, n, c) in ref_rows
+        ],
+        "embedding_versions": versions,
+        "auto_applied": auto_applied,
+    })
+
+
+@ccip_bp.route("/images/<int:image_id>", methods=["GET"])
+async def image_detail(image_id: int):
+    """An image's stored regions + the CCIP character matches it would get —
+    for spot-checking the agent's output + the matcher."""
+    async with get_session() as session:
+        regions = (
+            await session.execute(
+                select(ImageRegion)
+                .where(ImageRegion.image_record_id == image_id)
+                .order_by(ImageRegion.id)
+            )
+        ).scalars().all()
+        matches = await match_image(session, image_id)
+    return jsonify({
+        "image_id": image_id,
+        "regions": [
+            {
+                "id": r.id,
+                "kind": r.kind,
+                "bbox": [r.rx, r.ry, r.rw, r.rh],
+                "frame_time": r.frame_time,
+                "score": r.score,
+                "detector_version": r.detector_version,
+                "embedding_version": r.embedding_version,
+                "has_ccip": r.ccip_embedding is not None,
+                "has_siglip": r.siglip_embedding is not None,
+            }
+            for r in regions
+        ],
+        "ccip_matches": matches,
+    })

backend/app/api/cleanup.py

@@ -154,12 +154,15 @@ async def audit_history():
         limit = min(int(request.args.get("limit", "20")), 100)
     except ValueError:
         return _bad("invalid_limit")
+    # Optional rule filter so a card can reconnect to ITS latest run on mount
+    # (?rule=transparency&limit=1) — the audit survives navigation; the UI
+    # rehydrates from this rather than losing the in-flight scan.
+    rule = request.args.get("rule") or None
     async with get_session() as session:
-        rows = (await session.execute(
-            select(LibraryAuditRun)
-            .order_by(LibraryAuditRun.id.desc())
-            .limit(limit)
-        )).scalars().all()
+        stmt = select(LibraryAuditRun).order_by(LibraryAuditRun.id.desc())
+        if rule is not None:
+            stmt = stmt.where(LibraryAuditRun.rule == rule)
+        rows = (await session.execute(stmt.limit(limit))).scalars().all()
         return jsonify({"runs": [_serialize_audit_run(r) for r in rows]})
 
 

backend/app/api/credentials.py

@@ -121,13 +121,15 @@ async def delete_credential(platform: str):
 
 @credentials_bp.route("/<platform>/verify", methods=["POST"])
 async def verify_credential(platform: str):
-    """Test the stored credential by running gallery-dl --simulate
-    against one of the platform's enabled sources. On success stamps
-    last_verified. Returns {valid: bool|null, reason, last_verified?}.
-    valid=null means "couldn't test" (no credential, or no enabled
-    source to point at)."""
+    """Test the stored credential against one of the platform's enabled sources,
+    WITHOUT downloading. Routes through the platform's backend
+    (download_backends.verify_credential) — native ingester for Patreon, an
+    authenticated API page; gallery-dl --simulate for the rest. On success
+    stamps last_verified. Returns {valid: bool|null, reason, last_verified?};
+    valid=null means "couldn't test" (no credential, no enabled source, or an
+    inconclusive network/drift result)."""
     from ..models import Artist, Source
-    from ..services.gallery_dl import GalleryDLService, SourceConfig
+    from ..services.download_backends import verify_source_credential
 
     async with get_session() as session:
         if not await _ext_key_ok(session):
@@ -154,14 +156,14 @@ async def verify_credential(platform: str):
         cookies_path = await svc.get_cookies_path(platform)
         auth_token = await svc.get_token(platform)
 
-    gdl = GalleryDLService(images_root=Path("/images"))
-    ok, message = await gdl.verify(
+    ok, message = await verify_source_credential(
+        platform=platform,
         url=source.url,
         artist_slug=artist.slug,
-        platform=platform,
-        source_config=SourceConfig.from_dict(source.config_overrides or {}),
+        config_overrides=source.config_overrides or {},
         cookies_path=str(cookies_path) if cookies_path else None,
         auth_token=auth_token,
+        images_root=Path("/images"),
     )
 
     last_verified = None

backend/app/api/downloads.py

@@ -44,6 +44,9 @@ def _list_record(event: DownloadEvent, source: Source | None, artist: Artist | N
         "bytes_downloaded": event.bytes_downloaded,
         "error": event.error,
         "summary": _summary_from_metadata(event.metadata_),
+        # plan #709: mid-walk live counts for a RUNNING native-ingester event
+        # (None otherwise; phase 3 overwrites metadata with run_stats on finish).
+        "live": (event.metadata_ or {}).get("live"),
     }
 
 

backend/app/api/gallery.py

@@ -1,4 +1,6 @@
-"""Gallery API: cursor scroll, timeline, jump, image detail."""
+"""Gallery API: cursor scroll, timeline, jump, image detail, facets."""
+
+from datetime import UTC, datetime, timedelta
 
 from quart import Blueprint, jsonify, request
 
@@ -8,46 +10,104 @@ from ..services.gallery_service import GalleryService
 gallery_bp = Blueprint("gallery", __name__, url_prefix="/api/gallery")
 
 
+def _image_json(i):
+    """Serialize a GalleryImage for the scroll/similar list responses."""
+    return {
+        "id": i.id,
+        "sha256": i.sha256,
+        "mime": i.mime,
+        "width": i.width,
+        "height": i.height,
+        "created_at": i.created_at.isoformat(),
+        "posted_at": i.posted_at.isoformat() if i.posted_at else None,
+        "thumbnail_url": i.thumbnail_url,
+        "artist": i.artist,
+    }
+
+
+def _parse_date(raw):
+    """Parse a YYYY-MM-DD query value to a UTC midnight datetime, or None.
+    Raises ValueError (→ 400) on a malformed value."""
+    if not raw:
+        return None
+    return datetime.strptime(raw, "%Y-%m-%d").replace(tzinfo=UTC)
+
+
+def _parse_filters():
+    """Parse the composable gallery filters from query args, returning
+    ``(filters_dict, sort)``. Raises ValueError (→ 400) on malformed ids/dates.
+
+    The structured tag filter (#6) is AND-of-OR plus exclusions:
+    - `tag_id` accepts a single id or a comma-separated list — all ANDed
+      (the include common case; back-compat).
+    - `tag_or` is REPEATABLE; each instance is a comma-separated OR-group, and
+      the image must match at least one tag from EACH group (groups ANDed).
+    - `tag_not` is a comma-separated exclude list (image must carry none).
+
+    `media` is image|video; `sort` is newest|oldest; `platform` selects one
+    platform (or the UNSOURCED_PLATFORM sentinel); `untagged`/`no_artist` are
+    boolean flags; `date_from`/`date_to` are inclusive calendar-day bounds
+    (date_to is widened by a day so the whole day is covered by the service's
+    half-open `< date_to`)."""
+    tag_raw = request.args.get("tag_id")
+    tag_ids = (
+        [int(x) for x in tag_raw.split(",") if x.strip()] if tag_raw else None
+    ) or None
+    tag_or_groups = [
+        grp for raw in request.args.getlist("tag_or")
+        if (grp := [int(x) for x in raw.split(",") if x.strip()])
+    ] or None
+    not_raw = request.args.get("tag_not")
+    tag_exclude = (
+        [int(x) for x in not_raw.split(",") if x.strip()] if not_raw else None
+    ) or None
+    post_id_raw = request.args.get("post_id")
+    post_id = int(post_id_raw) if post_id_raw else None
+    artist_id_raw = request.args.get("artist_id")
+    artist_id = int(artist_id_raw) if artist_id_raw else None
+    media = request.args.get("media")
+    media_type = media if media in ("image", "video") else None
+    sort = request.args.get("sort")
+    sort = sort if sort in ("newest", "oldest") else "newest"
+    platform = request.args.get("platform") or None
+    untagged = request.args.get("untagged") in ("1", "true", "yes")
+    no_artist = request.args.get("no_artist") in ("1", "true", "yes")
+    date_from = _parse_date(request.args.get("date_from"))
+    date_to = _parse_date(request.args.get("date_to"))
+    if date_to is not None:
+        date_to += timedelta(days=1)  # inclusive of the date_to calendar day
+    filters = {
+        "tag_ids": tag_ids, "post_id": post_id, "artist_id": artist_id,
+        "media_type": media_type,
+        "tag_or_groups": tag_or_groups, "tag_exclude": tag_exclude,
+        "platform": platform,
+        "untagged": untagged, "no_artist": no_artist,
+        "date_from": date_from, "date_to": date_to,
+    }
+    return filters, sort
+
+
 @gallery_bp.route("/scroll", methods=["GET"])
 async def scroll():
     cursor = request.args.get("cursor") or None
     try:
         limit = int(request.args.get("limit", "50"))
+        filters, sort = _parse_filters()
     except ValueError:
-        return jsonify({"error": "limit must be an integer"}), 400
-    tag_id_raw = request.args.get("tag_id")
-    tag_id = int(tag_id_raw) if tag_id_raw else None
-    post_id_raw = request.args.get("post_id")
-    post_id = int(post_id_raw) if post_id_raw else None
-    artist_id_raw = request.args.get("artist_id")
-    artist_id = int(artist_id_raw) if artist_id_raw else None
+        return jsonify({"error": "invalid filter or limit parameter"}), 400
 
     async with get_session() as session:
         svc = GalleryService(session)
         try:
             page = await svc.scroll(
-                cursor=cursor, limit=limit, tag_id=tag_id,
-                post_id=post_id, artist_id=artist_id,
+                cursor=cursor, limit=limit, sort=sort, **filters,
             )
         except ValueError as exc:
             return jsonify({"error": str(exc)}), 400
 
     return jsonify(
         {
-            "images": [
-                {
-                    "id": i.id,
-                    "sha256": i.sha256,
-                    "mime": i.mime,
-                    "width": i.width,
-                    "height": i.height,
-                    "created_at": i.created_at.isoformat(),
-                    "posted_at": i.posted_at.isoformat() if i.posted_at else None,
-                    "thumbnail_url": i.thumbnail_url,
-                    "artist": i.artist,
-                }
-                for i in page.images
-            ],
+            "images": [_image_json(i) for i in page.images],
             "next_cursor": page.next_cursor,
             "date_groups": [
                 {"year": y, "month": m, "image_ids": ids} for y, m, ids in page.date_groups
@@ -56,20 +116,46 @@ async def scroll():
     )
 
 
-@gallery_bp.route("/timeline", methods=["GET"])
-async def timeline():
-    tag_id_raw = request.args.get("tag_id")
-    tag_id = int(tag_id_raw) if tag_id_raw else None
-    post_id_raw = request.args.get("post_id")
-    post_id = int(post_id_raw) if post_id_raw else None
-    artist_id_raw = request.args.get("artist_id")
-    artist_id = int(artist_id_raw) if artist_id_raw else None
+@gallery_bp.route("/similar", methods=["GET"])
+async def similar():
+    """Visual "more like this": images ranked by cosine distance to the
+    `similar_to` image's embedding. Composes with the scope filters (AND) but
+    ignores post_id and sort. Bounded top-N, no cursor."""
+    try:
+        similar_to = int(request.args["similar_to"])
+        limit = int(request.args.get("limit", "100"))
+        filters, _sort = _parse_filters()
+    except (KeyError, ValueError):
+        return jsonify({"error": "similar_to query param required"}), 400
+    # post_id is the exclusive post-detail view — not a similarity scope.
+    scope = {k: v for k, v in filters.items() if k != "post_id"}
     async with get_session() as session:
         svc = GalleryService(session)
         try:
-            buckets = await svc.timeline(
-                tag_id=tag_id, post_id=post_id, artist_id=artist_id
-            )
+            images = await svc.similar(image_id=similar_to, limit=limit, **scope)
+        except ValueError as exc:
+            return jsonify({"error": str(exc)}), 400
+    if images is None:
+        return jsonify({"error": "not found"}), 404
+    return jsonify(
+        {
+            "images": [_image_json(i) for i in images],
+            "next_cursor": None,
+            "date_groups": [],
+        }
+    )
+
+
+@gallery_bp.route("/timeline", methods=["GET"])
+async def timeline():
+    try:
+        filters, _sort = _parse_filters()
+    except ValueError:
+        return jsonify({"error": "invalid filter parameter"}), 400
+    async with get_session() as session:
+        svc = GalleryService(session)
+        try:
+            buckets = await svc.timeline(**filters)
         except ValueError as exc:
             return jsonify({"error": str(exc)}), 400
     return jsonify(
@@ -77,25 +163,43 @@ async def timeline():
     )
 
 
+@gallery_bp.route("/facets", methods=["GET"])
+async def facets():
+    try:
+        filters, _sort = _parse_filters()
+    except ValueError:
+        return jsonify({"error": "invalid filter parameter"}), 400
+    async with get_session() as session:
+        svc = GalleryService(session)
+        try:
+            f = await svc.facets(**filters)
+        except ValueError as exc:
+            return jsonify({"error": str(exc)}), 400
+    return jsonify(
+        {
+            "total": f.total,
+            "platforms": f.platforms,
+            "untagged": f.untagged,
+            "no_artist": f.no_artist,
+            "date_min": f.date_min.isoformat() if f.date_min else None,
+            "date_max": f.date_max.isoformat() if f.date_max else None,
+        }
+    )
+
+
 @gallery_bp.route("/jump", methods=["GET"])
 async def jump():
     try:
         year = int(request.args["year"])
         month = int(request.args["month"])
+        filters, sort = _parse_filters()
     except (KeyError, ValueError):
         return jsonify({"error": "year and month query params required"}), 400
-    tag_id_raw = request.args.get("tag_id")
-    tag_id = int(tag_id_raw) if tag_id_raw else None
-    post_id_raw = request.args.get("post_id")
-    post_id = int(post_id_raw) if post_id_raw else None
-    artist_id_raw = request.args.get("artist_id")
-    artist_id = int(artist_id_raw) if artist_id_raw else None
     async with get_session() as session:
         svc = GalleryService(session)
         try:
             cursor = await svc.jump_cursor(
-                year=year, month=month, tag_id=tag_id,
-                post_id=post_id, artist_id=artist_id,
+                year=year, month=month, sort=sort, **filters,
             )
         except ValueError as exc:
             return jsonify({"error": str(exc)}), 400

backend/app/api/gpu.py

@@ -0,0 +1,220 @@
+"""GPU-job API (#114): the HTTP surface the desktop agent pulls work from.
+
+The agent stays HTTP-only — it leases jobs, fetches image pixels via the normal
+FC image URLs, and submits embeddings/regions back, all over this API. Redis and
+Postgres are never exposed. The agent endpoints are gated by a bearer token
+(Authorization: Bearer <token>) stored in AppSetting; the admin endpoints
+(token / backfill / status) ride the browser session like the rest of FC's
+homelab admin.
+"""
+
+import secrets
+
+from quart import Blueprint, jsonify, request
+from sqlalchemy import func, select
+from sqlalchemy.dialects.postgresql import insert as pg_insert
+
+from ..extensions import get_session
+from ..models import AppSetting, GpuJob, ImageRecord, MLSettings
+from ..services.gallery_service import image_url
+from ..services.ml.embedder import MODEL_NAME as EMBED_MODEL_NAME
+from ..services.ml.gpu_jobs import GpuJobService
+from ..services.ml.regions import RegionService
+
+gpu_bp = Blueprint("gpu", __name__, url_prefix="/api/gpu")
+
+_TOKEN_KEY = "gpu_agent_token"
+
+
+def _bearer() -> str | None:
+    h = request.headers.get("Authorization", "")
+    return h[7:].strip() if h.startswith("Bearer ") else None
+
+
+async def _agent_authed(session) -> bool:
+    supplied = _bearer()
+    if not supplied:
+        return False
+    stored = (
+        await session.execute(
+            select(AppSetting.value).where(AppSetting.key == _TOKEN_KEY)
+        )
+    ).scalar_one_or_none()
+    return stored is not None and secrets.compare_digest(supplied, stored)
+
+
+# --- Admin (browser): token + backfill + status -------------------------
+
+@gpu_bp.route("/token", methods=["GET"])
+async def get_token():
+    async with get_session() as session:
+        tok = (
+            await session.execute(
+                select(AppSetting.value).where(AppSetting.key == _TOKEN_KEY)
+            )
+        ).scalar_one_or_none()
+    return jsonify({"token": tok, "configured": tok is not None})
+
+
+@gpu_bp.route("/token/rotate", methods=["POST"])
+async def rotate_token():
+    token = secrets.token_urlsafe(32)
+    async with get_session() as session:
+        await session.execute(
+            pg_insert(AppSetting)
+            .values(key=_TOKEN_KEY, value=token)
+            .on_conflict_do_update(index_elements=["key"], set_={"value": token})
+        )
+        await session.commit()
+    return jsonify({"token": token})
+
+
+@gpu_bp.route("/status", methods=["GET"])
+async def status():
+    async with get_session() as session:
+        rows = (
+            await session.execute(
+                select(GpuJob.status, func.count()).group_by(GpuJob.status)
+            )
+        ).all()
+    counts = dict(rows)
+    return jsonify({
+        "pending": counts.get("pending", 0),
+        "leased": counts.get("leased", 0),
+        "done": counts.get("done", 0),
+        "error": counts.get("error", 0),
+    })
+
+
+@gpu_bp.route("/backfill", methods=["POST"])
+async def backfill():
+    """Enqueue a job for every image that doesn't already have one for `task`."""
+    body = await request.get_json(silent=True) or {}
+    task = str(body.get("task") or "ccip")
+    from ..tasks.ml import enqueue_gpu_backfill
+
+    r = enqueue_gpu_backfill.delay(task)
+    return jsonify({"celery_task_id": r.id, "task": task}), 202
+
+
+# --- Agent (bearer token): lease / submit / heartbeat / fail ------------
+
+@gpu_bp.route("/jobs/lease", methods=["POST"])
+async def lease():
+    body = await request.get_json(silent=True) or {}
+    agent_id = str(body.get("agent_id") or "agent")
+    try:
+        batch = min(max(int(body.get("batch_size", 8)), 1), 64)
+    except (TypeError, ValueError):
+        batch = 8
+    async with get_session() as session:
+        if not await _agent_authed(session):
+            return jsonify({"error": "unauthorized"}), 401
+        jobs = await GpuJobService(session).lease(agent_id, batch_size=batch)
+        ml = (
+            await session.execute(select(MLSettings).where(MLSettings.id == 1))
+        ).scalar_one()
+        # image rows for url/mime in one shot
+        ids = [j.image_record_id for j in jobs]
+        imgs = {
+            i.id: i for i in (
+                await session.execute(
+                    select(ImageRecord).where(ImageRecord.id.in_(ids))
+                )
+            ).scalars()
+        } if ids else {}
+        await session.commit()
+        out = []
+        for j in jobs:
+            img = imgs.get(j.image_record_id)
+            if img is None:
+                continue
+            out.append({
+                "job_id": j.id,
+                "image_id": j.image_record_id,
+                "task": j.task,
+                "mime": img.mime,
+                "image_url": image_url(img.path),
+                # For video/animated: the agent samples at this cadence.
+                "frame_interval_seconds": ml.video_frame_interval_seconds,
+                "max_frames": ml.video_max_frames,
+                # The embedding model the agent must use for concept crops, so
+                # its region vectors land in the SAME space the heads trained in.
+                # Server-announced → the agent stays model-agnostic; a swap is a
+                # server setting + a re-embed migration, never an agent change.
+                "embed_model_name": EMBED_MODEL_NAME,
+                "embed_version": ml.embedder_model_version,
+            })
+    return jsonify({"jobs": out})
+
+
+@gpu_bp.route("/jobs/heartbeat", methods=["POST"])
+async def heartbeat():
+    body = await request.get_json(silent=True) or {}
+    agent_id = str(body.get("agent_id") or "agent")
+    job_ids = [int(x) for x in (body.get("job_ids") or [])]
+    async with get_session() as session:
+        if not await _agent_authed(session):
+            return jsonify({"error": "unauthorized"}), 401
+        n = await GpuJobService(session).heartbeat(agent_id, job_ids)
+        await session.commit()
+    return jsonify({"extended": n})
+
+
+@gpu_bp.route("/jobs/submit", methods=["POST"])
+async def submit():
+    """Store a job's regions + close it. regions: [{kind, bbox:[x,y,w,h],
+    frame_time?, score?, *_version?, ccip_embedding?, siglip_embedding?}].
+    replace_kinds defaults to the kinds present in the submitted regions."""
+    body = await request.get_json(silent=True) or {}
+    agent_id = str(body.get("agent_id") or "agent")
+    job_id = body.get("job_id")
+    regions = body.get("regions") or []
+    if job_id is None:
+        return jsonify({"error": "job_id required"}), 400
+    kinds = body.get("replace_kinds") or sorted({r["kind"] for r in regions})
+    async with get_session() as session:
+        if not await _agent_authed(session):
+            return jsonify({"error": "unauthorized"}), 401
+        job = await session.get(GpuJob, int(job_id))
+        if job is None or job.status != "leased" or job.lease_token != agent_id:
+            return jsonify({"error": "lease_invalid"}), 409
+        if kinds:
+            await RegionService(session).replace_regions(
+                job.image_record_id, kinds, regions
+            )
+        await GpuJobService(session).complete(agent_id, int(job_id))
+        await session.commit()
+    return jsonify({"ok": True, "stored": len(regions)})
+
+
+@gpu_bp.route("/jobs/fail", methods=["POST"])
+async def fail():
+    body = await request.get_json(silent=True) or {}
+    agent_id = str(body.get("agent_id") or "agent")
+    job_id = body.get("job_id")
+    if job_id is None:
+        return jsonify({"error": "job_id required"}), 400
+    async with get_session() as session:
+        if not await _agent_authed(session):
+            return jsonify({"error": "unauthorized"}), 401
+        ok = await GpuJobService(session).fail(
+            agent_id, int(job_id), str(body.get("error") or "")
+        )
+        await session.commit()
+    return jsonify({"ok": ok})
+
+
+@gpu_bp.route("/jobs/release", methods=["POST"])
+async def release():
+    """Graceful stop: the agent hands its still-leased jobs back to pending so
+    they're picked up immediately instead of waiting out the lease."""
+    body = await request.get_json(silent=True) or {}
+    agent_id = str(body.get("agent_id") or "agent")
+    job_ids = [int(x) for x in (body.get("job_ids") or [])]
+    async with get_session() as session:
+        if not await _agent_authed(session):
+            return jsonify({"error": "unauthorized"}), 401
+        n = await GpuJobService(session).release(agent_id, job_ids)
+        await session.commit()
+    return jsonify({"released": n})

backend/app/api/heads.py

@@ -0,0 +1,285 @@
+"""Heads API (#114): train + inspect the per-concept heads that power
+suggestions (replacing Camie + centroid).
+
+POST /api/heads/train   — (re)train all eligible heads (one run at a time).
+GET  /api/heads         — status: head count, last-trained, running run, the
+                          per-concept head table (strength + auto-apply ready),
+                          and recent training runs. The card rehydrates from
+                          here so status survives navigation.
+"""
+
+from quart import Blueprint, jsonify, request
+from sqlalchemy import desc, func, select
+
+from ..extensions import get_session
+from ..models import (
+    HeadAutoApplyRun,
+    HeadMetric,
+    HeadMetricsSnapshot,
+    HeadTrainingRun,
+    Tag,
+    TagHead,
+)
+from ..models.tag import image_tag
+from ..services.ml.heads import (
+    HeadAutoApplyAlreadyRunning,
+    HeadAutoApplyDisabled,
+    HeadTrainingAlreadyRunning,
+    start_head_auto_apply_run,
+    start_head_training_run,
+)
+
+heads_bp = Blueprint("heads", __name__, url_prefix="/api/heads")
+
+
+def _serialize_run(run: HeadTrainingRun) -> dict:
+    return {
+        "id": run.id,
+        "params": run.params,
+        "status": run.status,
+        "started_at": run.started_at.isoformat() if run.started_at else None,
+        "finished_at": run.finished_at.isoformat() if run.finished_at else None,
+        "n_trained": run.n_trained,
+        "n_skipped": run.n_skipped,
+        "error": run.error,
+    }
+
+
+@heads_bp.route("/train", methods=["POST"])
+async def train():
+    body = await request.get_json(silent=True) or {}
+    params = body.get("params") or body or {}
+    async with get_session() as session:
+        try:
+            run_id = await session.run_sync(
+                lambda s: start_head_training_run(s, params)
+            )
+        except HeadTrainingAlreadyRunning as running:
+            return jsonify({
+                "error": "training_already_running",
+                "running_id": int(running.args[0]),
+            }), 409
+        await session.commit()
+    return jsonify({"run_id": run_id, "status": "running"}), 202
+
+
+@heads_bp.route("", methods=["GET"])
+async def status():
+    async with get_session() as session:
+        count, last_trained = (
+            await session.execute(
+                select(func.count(), func.max(TagHead.trained_at))
+            )
+        ).one()
+        graduated = (
+            await session.execute(
+                select(func.count()).where(
+                    TagHead.auto_apply_threshold.is_not(None)
+                )
+            )
+        ).scalar_one()
+        running = (
+            await session.execute(
+                select(HeadTrainingRun.id)
+                .where(HeadTrainingRun.status == "running")
+                .order_by(HeadTrainingRun.id.desc())
+                .limit(1)
+            )
+        ).scalar_one_or_none()
+        runs = (
+            await session.execute(
+                select(HeadTrainingRun)
+                .order_by(HeadTrainingRun.id.desc())
+                .limit(10)
+            )
+        ).scalars().all()
+        # The per-concept table: strongest first, capped for the admin card.
+        head_rows = (
+            await session.execute(
+                select(
+                    TagHead.tag_id, Tag.name, Tag.kind,
+                    TagHead.n_pos, TagHead.n_neg, TagHead.ap,
+                    TagHead.precision_cv, TagHead.recall,
+                    TagHead.auto_apply_threshold, TagHead.trained_at,
+                )
+                .join(Tag, Tag.id == TagHead.tag_id)
+                .order_by(desc(TagHead.ap))
+                .limit(500)
+            )
+        ).all()
+        heads = [
+            {
+                "tag_id": r.tag_id,
+                "name": r.name,
+                "category": r.kind.value if hasattr(r.kind, "value") else str(r.kind),
+                "n_pos": r.n_pos,
+                "n_neg": r.n_neg,
+                "ap": r.ap,
+                "precision": r.precision_cv,
+                "recall": r.recall,
+                "auto_apply": r.auto_apply_threshold is not None,
+                "trained_at": r.trained_at.isoformat() if r.trained_at else None,
+            }
+            for r in head_rows
+        ]
+    return jsonify({
+        "head_count": count,
+        "graduated_count": graduated,
+        "last_trained_at": last_trained.isoformat() if last_trained else None,
+        "running_id": running,
+        "runs": [_serialize_run(r) for r in runs],
+        "heads": heads,
+    })
+
+
+def _serialize_apply_run(run: HeadAutoApplyRun) -> dict:
+    return {
+        "id": run.id,
+        "dry_run": run.dry_run,
+        "status": run.status,
+        "started_at": run.started_at.isoformat() if run.started_at else None,
+        "finished_at": run.finished_at.isoformat() if run.finished_at else None,
+        "n_applied": run.n_applied,
+        "report": run.report,
+        "error": run.error,
+    }
+
+
+@heads_bp.route("/auto-apply", methods=["POST"])
+async def auto_apply():
+    """Trigger an earned-auto-apply sweep. {dry_run:true} previews (writes
+    nothing); a real sweep needs head_auto_apply_enabled on."""
+    body = await request.get_json(silent=True) or {}
+    params = {"dry_run": bool(body.get("dry_run", False))}
+    async with get_session() as session:
+        try:
+            run_id = await session.run_sync(
+                lambda s: start_head_auto_apply_run(s, params)
+            )
+        except HeadAutoApplyAlreadyRunning as running:
+            return jsonify({
+                "error": "auto_apply_already_running",
+                "running_id": int(running.args[0]),
+            }), 409
+        except HeadAutoApplyDisabled:
+            return jsonify({"error": "auto_apply_disabled"}), 400
+        await session.commit()
+    return jsonify({"run_id": run_id, "status": "running"}), 202
+
+
+@heads_bp.route("/auto-apply", methods=["GET"])
+async def auto_apply_status():
+    async with get_session() as session:
+        running = (
+            await session.execute(
+                select(HeadAutoApplyRun.id)
+                .where(HeadAutoApplyRun.status == "running")
+                .order_by(HeadAutoApplyRun.id.desc())
+                .limit(1)
+            )
+        ).scalar_one_or_none()
+        runs = (
+            await session.execute(
+                select(HeadAutoApplyRun)
+                .order_by(HeadAutoApplyRun.id.desc())
+                .limit(10)
+            )
+        ).scalars().all()
+    return jsonify({
+        "running_id": running,
+        "runs": [_serialize_apply_run(r) for r in runs],
+    })
+
+
+@heads_bp.route("/metrics", methods=["GET"])
+async def metrics():
+    """Auto-apply observability: per-concept current counts (volume, misfires,
+    under-fires, realized misfire rate, head quality) + the daily time-series so
+    the operator can tune the precision target + support floor from real data."""
+    async with get_session() as session:
+        head_rows = (
+            await session.execute(
+                select(
+                    TagHead.tag_id, Tag.name, TagHead.ap, TagHead.precision_cv,
+                    TagHead.recall, TagHead.auto_apply_threshold, TagHead.n_pos,
+                ).join(Tag, Tag.id == TagHead.tag_id)
+            )
+        ).all()
+        heads = {r.tag_id: r for r in head_rows}
+        metric_rows = (
+            await session.execute(
+                select(
+                    HeadMetric.tag_id, HeadMetric.n_misfires, HeadMetric.n_underfires
+                )
+            )
+        ).all()
+        mets = {r.tag_id: r for r in metric_rows}
+        applied = dict(
+            (
+                await session.execute(
+                    select(image_tag.c.tag_id, func.count())
+                    .where(image_tag.c.source == "head_auto")
+                    .group_by(image_tag.c.tag_id)
+                )
+            ).all()
+        )
+        names = {r.tag_id: r.name for r in head_rows}
+        # Names for metric-only tags (head pruned but corrections recorded).
+        missing = [t for t in mets if t not in names]
+        if missing:
+            for tid, nm in (
+                await session.execute(
+                    select(Tag.id, Tag.name).where(Tag.id.in_(missing))
+                )
+            ).all():
+                names[tid] = nm
+
+        concepts = []
+        for tid in set(heads) | set(mets):
+            h = heads.get(tid)
+            m = mets.get(tid)
+            n_applied = applied.get(tid, 0)
+            n_mis = m.n_misfires if m else 0
+            denom = n_applied + n_mis
+            concepts.append({
+                "tag_id": tid,
+                "name": names.get(tid, str(tid)),
+                "n_auto_applied": n_applied,
+                "n_misfires": n_mis,
+                "n_underfires": m.n_underfires if m else 0,
+                # Of everything this head ever auto-applied, the fraction you
+                # removed — the misfire rate (null until something fired).
+                "misfire_rate": round(n_mis / denom, 4) if denom else None,
+                "ap": h.ap if h else None,
+                "precision_cv": h.precision_cv if h else None,
+                "recall": h.recall if h else None,
+                "auto_apply": bool(h and h.auto_apply_threshold is not None),
+                "n_pos": h.n_pos if h else None,
+            })
+        concepts.sort(key=lambda c: (c["n_misfires"], c["n_auto_applied"]), reverse=True)
+
+        snaps = (
+            await session.execute(
+                select(HeadMetricsSnapshot)
+                .order_by(HeadMetricsSnapshot.snapshot_at.desc())
+                .limit(1000)
+            )
+        ).scalars().all()
+    return jsonify({
+        "concepts": concepts,
+        "snapshots": [
+            {
+                "tag_id": s.tag_id,
+                "name": s.name,
+                "snapshot_at": s.snapshot_at.isoformat() if s.snapshot_at else None,
+                "n_auto_applied": s.n_auto_applied,
+                "n_misfires": s.n_misfires,
+                "n_underfires": s.n_underfires,
+                "ap": s.ap,
+                "precision_cv": s.precision_cv,
+                "recall": s.recall,
+                "n_pos": s.n_pos,
+            }
+            for s in snaps
+        ],
+    })

backend/app/api/ml_admin.py

@@ -13,6 +13,17 @@ _EDITABLE = (
     "suggestion_threshold_general",
     "centroid_similarity_threshold",
     "min_reference_images",
+    "tagger_store_floor",
+    "video_frame_interval_seconds",
+    "video_max_frames",
+    "video_min_tag_frames",
+    "head_min_positives",
+    "head_auto_apply_precision",
+    "head_auto_apply_enabled",
+    "head_auto_apply_min_positives",
+    "ccip_match_threshold",
+    "ccip_auto_apply_enabled",
+    "ccip_auto_apply_threshold",
 )
 
 
@@ -30,8 +41,19 @@ async def get_settings():
             "suggestion_threshold_general": s.suggestion_threshold_general,
             "centroid_similarity_threshold": s.centroid_similarity_threshold,
             "min_reference_images": s.min_reference_images,
+            "tagger_store_floor": s.tagger_store_floor,
+            "video_frame_interval_seconds": s.video_frame_interval_seconds,
+            "video_max_frames": s.video_max_frames,
+            "video_min_tag_frames": s.video_min_tag_frames,
             "tagger_model_version": s.tagger_model_version,
             "embedder_model_version": s.embedder_model_version,
+            "head_min_positives": s.head_min_positives,
+            "head_auto_apply_precision": s.head_auto_apply_precision,
+            "head_auto_apply_enabled": s.head_auto_apply_enabled,
+            "head_auto_apply_min_positives": s.head_auto_apply_min_positives,
+            "ccip_match_threshold": s.ccip_match_threshold,
+            "ccip_auto_apply_enabled": s.ccip_auto_apply_enabled,
+            "ccip_auto_apply_threshold": s.ccip_auto_apply_threshold,
         }
     )
 
@@ -47,13 +69,65 @@ async def patch_settings():
         s = (
             await session.execute(select(MLSettings).where(MLSettings.id == 1))
         ).scalar_one()
+
+        # Merge the patch over current values, then validate the result as a
+        # whole — the store-floor invariant couples three fields, so they
+        # can't be checked one at a time.
+        proposed = {f: getattr(s, f) for f in _EDITABLE}
         for field in _EDITABLE:
             if field in body:
-                setattr(s, field, body[field])
+                proposed[field] = body[field]
+
+        err = _validate(proposed)
+        if err is not None:
+            return jsonify({"error": err}), 400
+
+        for field in _EDITABLE:
+            setattr(s, field, proposed[field])
         await session.commit()
     return await get_settings()
 
 
+def _validate(p: dict) -> str | None:
+    """Returns an error string if the proposed settings are invalid, else None.
+
+    Invariant (plan-task #764): the per-category suggestion thresholds can't
+    drop below tagger_store_floor — nothing below the floor is stored, so a
+    lower threshold would silently surface nothing in that gap. The UI clamps
+    the sliders to the floor; this is the server-side backstop.
+    """
+    floor = p["tagger_store_floor"]
+    if not (0.0 <= floor <= 1.0):
+        return "tagger_store_floor must be between 0 and 1"
+    for cat in ("character", "general"):
+        if p[f"suggestion_threshold_{cat}"] < floor:
+            return (
+                f"suggestion_threshold_{cat} cannot be below tagger_store_floor "
+                f"({floor}) — predictions below the floor are not stored"
+            )
+    # Video tagging (#747).
+    if p["video_frame_interval_seconds"] <= 0:
+        return "video_frame_interval_seconds must be > 0"
+    if p["video_max_frames"] < 1:
+        return "video_max_frames must be >= 1"
+    if p["video_min_tag_frames"] < 1:
+        return "video_min_tag_frames must be >= 1"
+    if p["video_min_tag_frames"] > p["video_max_frames"]:
+        return "video_min_tag_frames cannot exceed video_max_frames"
+    # Head training (#114).
+    if int(p["head_min_positives"]) < 1:
+        return "head_min_positives must be >= 1"
+    if not (0.5 <= float(p["head_auto_apply_precision"]) <= 0.999):
+        return "head_auto_apply_precision must be between 0.5 and 0.999"
+    if int(p["head_auto_apply_min_positives"]) < 1:
+        return "head_auto_apply_min_positives must be >= 1"
+    if not (0.5 <= float(p["ccip_match_threshold"]) <= 0.999):
+        return "ccip_match_threshold must be between 0.5 and 0.999"
+    if not (0.5 <= float(p["ccip_auto_apply_threshold"]) <= 0.999):
+        return "ccip_auto_apply_threshold must be between 0.5 and 0.999"
+    return None
+
+
 @ml_admin_bp.route("/backfill", methods=["POST"])
 async def trigger_backfill():
     from ..tasks.ml import backfill

backend/app/api/posts.py

@@ -17,6 +17,7 @@ async def list_posts():
     cursor = args.get("cursor") or None
     artist_id_raw = args.get("artist_id")
     platform = args.get("platform") or None
+    q = (args.get("q") or "").strip() or None
     limit_raw = args.get("limit", "24")
     direction = args.get("direction", "older")
     around_raw = args.get("around")
@@ -56,7 +57,7 @@ async def list_posts():
         if around_id is not None:
             result = await svc.around(
                 post_id=around_id, artist_id=artist_id,
-                platform=platform, limit=limit,
+                platform=platform, q=q, limit=limit,
             )
             if result is None:
                 return _bad("not_found", status=404, detail=f"post id={around_id}")
@@ -64,7 +65,7 @@ async def list_posts():
         try:
             page = await svc.scroll(
                 cursor=cursor, artist_id=artist_id,
-                platform=platform, limit=limit, direction=direction,
+                platform=platform, q=q, limit=limit, direction=direction,
             )
         except ValueError as exc:
             # Service raises ValueError for malformed cursors only;

backend/app/api/settings.py

@@ -25,6 +25,22 @@ _EDITABLE_FIELDS = (
     "download_schedule_default_seconds",
     "download_event_retention_days",
     "download_failure_warning_threshold",
+    "series_suggest_enabled",
+    "series_suggest_threshold",
+    "extdl_mega_enabled",
+    "extdl_gdrive_enabled",
+    "extdl_mediafire_enabled",
+    "extdl_dropbox_enabled",
+    "extdl_pixeldrain_enabled",
+)
+
+# Per-host external-download toggles — all plain booleans, validated uniformly.
+_EXTDL_TOGGLE_FIELDS = (
+    "extdl_mega_enabled",
+    "extdl_gdrive_enabled",
+    "extdl_mediafire_enabled",
+    "extdl_dropbox_enabled",
+    "extdl_pixeldrain_enabled",
 )
 
 
@@ -46,6 +62,13 @@ async def get_import_settings():
         "download_schedule_default_seconds": row.download_schedule_default_seconds,
         "download_event_retention_days": row.download_event_retention_days,
         "download_failure_warning_threshold": row.download_failure_warning_threshold,
+        "series_suggest_enabled": row.series_suggest_enabled,
+        "series_suggest_threshold": row.series_suggest_threshold,
+        "extdl_mega_enabled": row.extdl_mega_enabled,
+        "extdl_gdrive_enabled": row.extdl_gdrive_enabled,
+        "extdl_mediafire_enabled": row.extdl_mediafire_enabled,
+        "extdl_dropbox_enabled": row.extdl_dropbox_enabled,
+        "extdl_pixeldrain_enabled": row.extdl_pixeldrain_enabled,
     })
 
 
@@ -96,6 +119,22 @@ async def update_import_settings():
         if not isinstance(v, int) or isinstance(v, bool) or v < 1 or v > 100:
             return _bad_int("download_failure_warning_threshold", 1, 100)
 
+    if "series_suggest_enabled" in body and not isinstance(
+        body["series_suggest_enabled"], bool
+    ):
+        return jsonify(
+            {"error": "series_suggest_enabled must be a boolean"}
+        ), 400
+    for tog in _EXTDL_TOGGLE_FIELDS:
+        if tog in body and not isinstance(body[tog], bool):
+            return jsonify({"error": f"{tog} must be a boolean"}), 400
+    if "series_suggest_threshold" in body:
+        v = body["series_suggest_threshold"]
+        if not isinstance(v, (int, float)) or isinstance(v, bool) or v < 0 or v > 1:
+            return jsonify(
+                {"error": "series_suggest_threshold must be a number in [0, 1]"}
+            ), 400
+
     async with get_session() as session:
         row = await ImportSettings.load(session)
         for field in _EDITABLE_FIELDS:

backend/app/api/sources.py

@@ -85,6 +85,22 @@ async def create_source():
             return _bad("empty_url", detail=str(exc))
         except DuplicateSourceError as exc:
             return _bad("duplicate", status=409, existing_id=exc.existing_id)
+
+        # Immediate kickoff: a new enabled source is armed for backfill (#693)
+        # but would otherwise sit idle until the next scheduler tick (~60s).
+        # Enqueue the first walk now, skipping only if the platform is in a
+        # rate-limit cooldown (the scheduler picks it up when that clears).
+        dispatch_id = None
+        if record.enabled:
+            cooldowns = await active_platform_cooldowns(session)
+            if record.platform not in cooldowns:
+                session.add(DownloadEvent(source_id=record.id, status="pending"))
+                await session.commit()
+                dispatch_id = record.id
+
+    if dispatch_id is not None:
+        from ..tasks.download import download_source
+        download_source.delay(dispatch_id)
     return jsonify(record.to_dict()), 201
 
 
@@ -122,29 +138,123 @@ async def delete_source(source_id: int):
 
 @sources_bp.route("/<int:source_id>/backfill", methods=["POST"])
 async def set_backfill(source_id: int):
-    """Plan #544: arm a source for backfill mode for the next N download
-    runs. Body: `{"runs": int}` (1..10, default 3). Returns the updated
-    source dict. While backfill_runs_remaining > 0, downloads use
-    gallery-dl's full-walk config (skip: True + 30-min timeout) instead
-    of the catch-up default (skip: "exit:20" + 14.5-min timeout)."""
+    """Plan #693/#697 + #830: start/stop a backfill, or start a recovery /
+    recapture. Body: `{"action": "start" | "stop" | "recover" | "recapture"}`
+    (default "start"). 'start' walks the full post history in time-boxed chunks
+    until it reaches the bottom (then the source shows 'complete'); 'recover' is
+    the same walk but bypasses the Patreon seen-ledger to re-fetch
+    dropped-and-deleted near-dups under the current pHash threshold; 'recapture'
+    re-grabs EVERY post's body + external links and localizes on-disk inline
+    images WITHOUT re-downloading media; 'stop' cancels any back to tick mode.
+    Returns the updated source dict (incl. backfill_state / backfill_chunks /
+    backfill_bypass_seen / backfill_recapture)."""
+    from pathlib import Path
+
+    from ..services.credential_service import CredentialService
+    from ..services.download_backends import (
+        uses_native_ingester,
+        verify_source_credential,
+    )
+    from .credentials import _get_crypto
+
     payload = await request.get_json(silent=True) or {}
-    runs = payload.get("runs", 3)
-    try:
-        runs = int(runs)
-    except (TypeError, ValueError):
-        return _bad("invalid_runs", detail="runs must be an integer")
+    action = payload.get("action", "start")
+    if action not in ("start", "stop", "recover", "recapture"):
+        return _bad(
+            "invalid_action",
+            detail="action must be 'start', 'stop', 'recover', or 'recapture'",
+        )
+
+    # Pre-flight (plan #703 #2): before arming a deep walk on a native-ingester
+    # platform (where verify is one cheap API page), refuse if the credential is
+    # DEFINITIVELY rejected — don't burn chunks against expired cookies. Proceed
+    # on valid OR inconclusive (a network blip shouldn't block). Gated to native
+    # platforms: gallery-dl verify is a slow --simulate subprocess, too heavy for
+    # an arm action. The credential read happens in a session that's CLOSED
+    # before the verify network call (don't hold a DB conn across the request).
+    if action in ("start", "recover", "recapture"):
+        async with get_session() as session:
+            rec = await SourceService(session).get(source_id)
+            if rec is None:
+                return _bad("not_found", status=404)
+            native = uses_native_ingester(rec.platform)
+            if native:
+                cred = CredentialService(session, _get_crypto())
+                cookies_path = await cred.get_cookies_path(rec.platform)
+                auth_token = await cred.get_token(rec.platform)
+        if native:
+            ok, message = await verify_source_credential(
+                platform=rec.platform,
+                url=rec.url,
+                artist_slug=rec.artist_slug,
+                config_overrides=rec.config_overrides or {},
+                cookies_path=str(cookies_path) if cookies_path else None,
+                auth_token=auth_token,
+                images_root=Path("/images"),
+            )
+            if ok is False:
+                return _bad("credential_rejected", detail=message, status=409)
+
     async with get_session() as session:
         try:
-            record = await SourceService(session).set_backfill_runs(
-                source_id, runs,
-            )
+            svc = SourceService(session)
+            if action == "start":
+                record = await svc.start_backfill(source_id)
+            elif action == "recover":
+                record = await svc.start_recovery(source_id)
+            elif action == "recapture":
+                record = await svc.start_recapture(source_id)
+            else:
+                record = await svc.stop_backfill(source_id)
         except LookupError:
             return _bad("not_found", status=404)
-        except ValueError as exc:
-            return _bad("invalid_runs", detail=str(exc))
     return jsonify(record.to_dict())
 
 
+@sources_bp.route("/<int:source_id>/preview", methods=["POST"])
+async def preview_source_endpoint(source_id: int):
+    """Plan #708 B4: dry-run — count what a backfill WOULD download for a native
+    platform (Patreon today), without downloading. Walks the first few feed pages
+    and counts media not already in the seen/dead ledgers. Returns
+    {total_new, posts_scanned, pages_scanned, has_more, sample[]} or 409 + reason
+    (unresolvable campaign id / auth / drift). 400 for gallery-dl platforms (no
+    cheap dry-run — their verify is a slow --simulate)."""
+    from pathlib import Path
+
+    from ..services.credential_service import CredentialService
+    from ..services.download_backends import preview_source, uses_native_ingester
+    from ..tasks._sync_engine import sync_session_factory
+    from .credentials import _get_crypto
+
+    async with get_session() as session:
+        rec = await SourceService(session).get(source_id)
+        if rec is None:
+            return _bad("not_found", status=404)
+        if not uses_native_ingester(rec.platform):
+            return _bad(
+                "unsupported",
+                detail="Preview is only available for native-ingester platforms.",
+                status=400,
+            )
+        cred = CredentialService(session, _get_crypto())
+        cookies_path = await cred.get_cookies_path(rec.platform)
+
+    # The walk + ledger reads are sync (run off the request loop); the process
+    # sync engine is the same one the download task uses.
+    result = await preview_source(
+        platform=rec.platform,
+        url=rec.url,
+        source_id=source_id,
+        config_overrides=rec.config_overrides or {},
+        cookies_path=str(cookies_path) if cookies_path else None,
+        images_root=Path("/images"),
+        sync_session_factory=sync_session_factory(),
+    )
+    if "error" in result:
+        return _bad("preview_failed", detail=result["error"], status=409)
+    return jsonify(result)
+
+
 @sources_bp.route("/<int:source_id>/check", methods=["POST"])
 async def check_source(source_id: int):
     """FC-3c: enqueue a download for this source.

backend/app/api/suggestions.py

@@ -3,16 +3,48 @@
 from quart import Blueprint, jsonify, request
 
 from ..extensions import get_session
+from ..models import Tag, TagAllowlist
 from ..services.ml.allowlist import AllowlistService
 from ..services.ml.suggestions import SuggestionService
 
 suggestions_bp = Blueprint("suggestions", __name__, url_prefix="/api")
 
 
+async def _accept_payload(session, svc, newly_added: bool, tag_id: int) -> dict:
+    """Shape the accept/alias response. When accepting newly allowlists a tag,
+    include the coverage PROJECTION (at the tag's threshold) so the UI can show
+    a non-blocking "auto-applying to ~N images" toast — the actual apply runs
+    async via apply_allowlist_tags, so this is an estimate, not a post-hoc
+    count (#7)."""
+    payload = {"allowlisted": newly_added}
+    if newly_added:
+        tag = await session.get(Tag, tag_id)
+        row = await session.get(TagAllowlist, tag_id)
+        payload["tag_id"] = tag_id
+        payload["tag_name"] = tag.name if tag is not None else None
+        payload["projected_count"] = await svc.coverage(
+            tag_id, row.min_confidence if row is not None else 0.90,
+        )
+    return payload
+
+
 @suggestions_bp.route("/images/<int:image_id>/suggestions", methods=["GET"])
 async def get_suggestions(image_id: int):
+    # ?min=<float> overrides the configured per-category thresholds so the typed
+    # tag-input dropdown can surface EVERY stored prediction (min=0), including
+    # low-confidence actions/features, in canonical formatting. Omitted → the
+    # curated above-threshold list the Suggestions panel uses.
+    override = None
+    raw_min = request.args.get("min")
+    if raw_min is not None:
+        try:
+            override = min(1.0, max(0.0, float(raw_min)))
+        except ValueError:
+            return jsonify({"error": "min must be a float in [0,1]"}), 400
     async with get_session() as session:
-        sl = await SuggestionService(session).for_image(image_id)
+        sl = await SuggestionService(session).for_image(
+            image_id, threshold_override=override
+        )
     return jsonify(
         {
             "by_category": {
@@ -24,6 +56,15 @@ async def get_suggestions(image_id: int):
                         "score": round(s.score, 4),
                         "source": s.source,
                         "creates_new_tag": s.creates_new_tag,
+                        # raw model key (alias is stored under this) + whether an
+                        # operator alias produced this suggestion — drive the
+                        # modal's "Treat as alias"/"Remove alias" affordances.
+                        "raw_name": s.raw_name,
+                        "via_alias": s.via_alias,
+                        # operator dismissed this tag for this image — surfaced
+                        # (not dropped) so the rail can show it rejected + offer
+                        # one-click un-reject.
+                        "rejected": s.rejected,
                     }
                     for s in items
                 ]
@@ -42,13 +83,15 @@ async def accept_suggestion(image_id: int):
         return jsonify({"error": "tag_id required"}), 400
     tag_id = body["tag_id"]
     async with get_session() as session:
-        newly_added = await AllowlistService(session).accept(image_id, tag_id)
+        svc = AllowlistService(session)
+        newly_added = await svc.accept(image_id, tag_id)
+        payload = await _accept_payload(session, svc, newly_added, tag_id)
         await session.commit()
     if newly_added:
         from ..tasks.ml import apply_allowlist_tags
 
         apply_allowlist_tags.delay(tag_id=tag_id)
-    return "", 204
+    return jsonify(payload)
 
 
 @suggestions_bp.route(
@@ -59,19 +102,24 @@ async def alias_suggestion(image_id: int):
     required = {"alias_string", "alias_category", "canonical_tag_id"}
     if not body or not required.issubset(body):
         return jsonify({"error": f"required: {sorted(required)}"}), 400
+    canonical_tag_id = body["canonical_tag_id"]
     async with get_session() as session:
-        newly_added = await AllowlistService(session).add_alias_and_accept(
+        svc = AllowlistService(session)
+        newly_added = await svc.add_alias_and_accept(
             image_id,
             body["alias_string"],
             body["alias_category"],
-            body["canonical_tag_id"],
+            canonical_tag_id,
+        )
+        payload = await _accept_payload(
+            session, svc, newly_added, canonical_tag_id,
         )
         await session.commit()
     if newly_added:
         from ..tasks.ml import apply_allowlist_tags
 
-        apply_allowlist_tags.delay(tag_id=body["canonical_tag_id"])
-    return "", 204
+        apply_allowlist_tags.delay(tag_id=canonical_tag_id)
+    return jsonify(payload)
 
 
 @suggestions_bp.route(
@@ -87,6 +135,21 @@ async def dismiss_suggestion(image_id: int):
     return "", 204
 
 
+@suggestions_bp.route(
+    "/images/<int:image_id>/suggestions/undismiss", methods=["POST"]
+)
+async def undismiss_suggestion(image_id: int):
+    """Reverse a per-image dismissal (reject-recovery). Idempotent — undoing a
+    tag that isn't rejected is a no-op delete."""
+    body = await request.get_json()
+    if not body or "tag_id" not in body:
+        return jsonify({"error": "tag_id required"}), 400
+    async with get_session() as session:
+        await AllowlistService(session).undismiss(image_id, body["tag_id"])
+        await session.commit()
+    return "", 204
+
+
 @suggestions_bp.route("/suggestions/bulk", methods=["POST"])
 async def bulk_suggestions():
     body = await request.get_json()

backend/app/api/system_activity.py

@@ -31,7 +31,7 @@ system_activity_bp = Blueprint(
 # absent.
 _QUEUE_NAMES = (
     "default", "import", "thumbnail", "ml",
-    "download", "scan", "maintenance",
+    "download", "scan", "maintenance", "maintenance_long",
 )
 
 # Cache module-level so all requests share the cache between polls.
@@ -147,6 +147,7 @@ async def list_runs():
     """Paginated task_run history. Query params:
       queue=<name>     filter to one queue
       status=<status>  filter to one status (running/ok/error/timeout/retry)
+      task=<substr>    case-insensitive substring match on task_name
       limit=<int>      default 50, max 200
       before_id=<int>  cursor for keyset pagination
 
@@ -161,6 +162,7 @@ async def list_runs():
 
     queue = request.args.get("queue")
     status = request.args.get("status")
+    task = request.args.get("task")
     before_id_raw = request.args.get("before_id")
     before_id = int(before_id_raw) if before_id_raw else None
 
@@ -170,6 +172,11 @@ async def list_runs():
             stmt = stmt.where(TaskRun.queue == queue)
         if status:
             stmt = stmt.where(TaskRun.status == status)
+        if task:
+            # Task names contain literal underscores (download_source,
+            # vacuum_analyze) — escape LIKE wildcards so a search for
+            # "vacuum_analyze" doesn't treat "_" as a single-char match.
+            stmt = stmt.where(TaskRun.task_name.ilike(f"%{_escape_like(task)}%", escape="\\"))
         if before_id is not None:
             stmt = stmt.where(TaskRun.id < before_id)
         stmt = stmt.limit(limit + 1)
@@ -225,6 +232,12 @@ async def list_failures():
     })
 
 
+def _escape_like(value: str) -> str:
+    """Escape SQL LIKE/ILIKE metacharacters so user search text is matched
+    literally. Pairs with `escape="\\"` on the .ilike() call."""
+    return value.replace("\\", "\\\\").replace("%", "\\%").replace("_", "\\_")
+
+
 def _row_to_dict(r: TaskRun) -> dict:
     return {
         "id": r.id,

backend/app/api/tag_eval.py

@@ -0,0 +1,70 @@
+"""Tag-eval API (#1130): trigger + revisit the head-vs-centroid eval.
+
+The run + full report live in the tag_eval_run row, so the admin card rehydrates
+from GET (history / detail) on mount — the report survives navigation rather than
+living in transient frontend state.
+"""
+
+from quart import Blueprint, jsonify, request
+from sqlalchemy import select
+
+from ..extensions import get_session
+from ..models import TagEvalRun
+from ..services.ml.tag_eval import EvalAlreadyRunning, start_tag_eval_run
+
+tag_eval_bp = Blueprint("tag_eval", __name__, url_prefix="/api/tag-eval")
+
+
+def _serialize(run: TagEvalRun, *, include_report: bool) -> dict:
+    out = {
+        "id": run.id,
+        "params": run.params,
+        "status": run.status,
+        "started_at": run.started_at.isoformat() if run.started_at else None,
+        "finished_at": run.finished_at.isoformat() if run.finished_at else None,
+        "error": run.error,
+    }
+    if include_report:
+        out["report"] = run.report
+    return out
+
+
+@tag_eval_bp.route("", methods=["POST"])
+async def create():
+    body = await request.get_json(silent=True) or {}
+    params = body.get("params") or body or {}
+    async with get_session() as session:
+        try:
+            run_id = await session.run_sync(
+                lambda s: start_tag_eval_run(s, params)
+            )
+        except EvalAlreadyRunning as running:
+            return jsonify({
+                "error": "eval_already_running",
+                "running_id": int(running.args[0]),
+            }), 409
+        await session.commit()
+    return jsonify({"run_id": run_id, "status": "running"}), 202
+
+
+@tag_eval_bp.route("", methods=["GET"])
+async def history():
+    try:
+        limit = min(int(request.args.get("limit", "20")), 100)
+    except ValueError:
+        return jsonify({"error": "invalid_limit"}), 400
+    async with get_session() as session:
+        rows = (await session.execute(
+            select(TagEvalRun).order_by(TagEvalRun.id.desc()).limit(limit)
+        )).scalars().all()
+        # List is light — no full report (the detail endpoint carries it).
+        return jsonify({"runs": [_serialize(r, include_report=False) for r in rows]})
+
+
+@tag_eval_bp.route("/<int:run_id>", methods=["GET"])
+async def detail(run_id: int):
+    async with get_session() as session:
+        run = await session.get(TagEvalRun, run_id)
+        if run is None:
+            return jsonify({"error": "not_found"}), 404
+        return jsonify(_serialize(run, include_report=True))

backend/app/api/tags.py

@@ -2,18 +2,23 @@
 
 from quart import Blueprint, jsonify, request
 from sqlalchemy import exists, select
+from sqlalchemy.dialects.postgresql import insert as pg_insert
 from sqlalchemy.exc import IntegrityError
 
 from ..extensions import get_session
-from ..models import Tag, TagKind
+from ..models import Tag, TagKind, TagPositiveConfirmation
 from ..models.tag_allowlist import TagAllowlist
 from ..services.bulk_tag_service import BulkTagService
+from ..services.ml.aliases import AliasService
+from ..services.series_match_service import SeriesMatchService
 from ..services.series_service import SeriesError, SeriesService
 from ..services.tag_directory_service import TagDirectoryService
+from ..services.tag_query import serialize_tag
 from ..services.tag_service import (
     TagMergeConflict,
     TagService,
     TagValidationError,
+    normalize_tag_name,
 )
 from ..utils.tag_prefix import parse_kind_prefix
 
@@ -70,17 +75,7 @@ async def autocomplete():
         hits = await svc.autocomplete(q, kind=kind, limit=limit)
 
     return jsonify(
-        [
-            {
-                "id": h.id,
-                "name": h.name,
-                "kind": h.kind,
-                "fandom_id": h.fandom_id,
-                "fandom_name": h.fandom_name,
-                "image_count": h.image_count,
-            }
-            for h in hits
-        ]
+        [{**serialize_tag(h), "image_count": h.image_count} for h in hits]
     )
 
 
@@ -141,6 +136,11 @@ async def create_tag():
 
     fandom_id = body.get("fandom_id")
 
+    # #701: Title-Case operator-entered tags. Only here (the explicit create
+    # endpoint), NOT in the shared find_or_create — the ML tagger uses that path
+    # and must keep the booru vocabulary's casing for allowlist matching.
+    name = normalize_tag_name(name)
+
     async with get_session() as session:
         svc = TagService(session)
         try:
@@ -158,17 +158,7 @@ async def list_tags_for_image(image_id: int):
     async with get_session() as session:
         svc = TagService(session)
         tags = await svc.list_for_image(image_id)
-    return jsonify(
-        [
-            {
-                "id": t.id,
-                "name": t.name,
-                "kind": t.kind.value,
-                "fandom_id": t.fandom_id,
-            }
-            for t in tags
-        ]
-    )
+    return jsonify([serialize_tag(t) for t in tags])
 
 
 @tags_bp.route("/images/<int:image_id>/tags", methods=["POST"])
@@ -194,15 +184,79 @@ async def remove_tag_from_image(image_id: int, tag_id: int):
     return "", 204
 
 
+@tags_bp.route("/images/<int:image_id>/tags/<int:tag_id>/confirm", methods=["POST"])
+async def confirm_tag_on_image(image_id: int, tag_id: int):
+    """Operator affirmed an applied tag is correct ("keep" on a doubted positive).
+    Idempotent; recorded so the eval's doubts list stops resurfacing it (#1130)."""
+    async with get_session() as session:
+        await session.execute(
+            pg_insert(TagPositiveConfirmation)
+            .values(image_record_id=image_id, tag_id=tag_id)
+            .on_conflict_do_nothing(index_elements=["image_record_id", "tag_id"])
+        )
+        await session.commit()
+    return "", 204
+
+
+@tags_bp.route("/tags/<int:tag_id>", methods=["GET"])
+async def get_tag(tag_id: int):
+    """Resolve a single tag (used by the gallery to label its active
+    tag-filter chip)."""
+    async with get_session() as session:
+        tag = await session.get(Tag, tag_id)
+        if tag is None:
+            return jsonify({"error": "tag not found"}), 404
+        return jsonify(
+            {
+                "id": tag.id,
+                "name": tag.name,
+                "kind": tag.kind.value,
+                "fandom_id": tag.fandom_id,
+            }
+        )
+
+
+@tags_bp.route("/tags/<int:tag_id>/aliases", methods=["GET"])
+async def list_tag_aliases(tag_id: int):
+    """Model keys that fold into this tag (tag-side alias view). Remove via the
+    shared DELETE /api/aliases/<string>/<category>."""
+    async with get_session() as session:
+        if await session.get(Tag, tag_id) is None:
+            return jsonify({"error": "tag not found"}), 404
+        rows = await AliasService(session).list_for_tag(tag_id)
+    return jsonify(
+        [
+            {
+                "alias_string": r.alias_string,
+                "alias_category": r.alias_category,
+            }
+            for r in rows
+        ]
+    )
+
+
 @tags_bp.route("/tags/<int:tag_id>", methods=["PATCH"])
-async def rename_tag(tag_id: int):
-    body = await request.get_json()
-    if not body or "name" not in body:
-        return jsonify({"error": "name required"}), 400
+async def update_tag(tag_id: int):
+    """Rename and/or re-fandom a tag. Body may carry `name` and/or
+    `fandom_id` (a fandom tag id, or null to clear — character tags only).
+    `merge: true` resolves a collision by merging into the existing tag.
+    """
+    body = await request.get_json() or {}
+    has_name = "name" in body
+    has_fandom = "fandom_id" in body
+    if not has_name and not has_fandom:
+        return jsonify({"error": "name or fandom_id required"}), 400
+    do_merge = bool(body.get("merge"))
     async with get_session() as session:
         svc = TagService(session)
         try:
-            tag = await svc.rename(tag_id, body["name"])
+            tag = None
+            if has_name:
+                tag = await svc.rename(tag_id, body["name"])
+            if has_fandom:
+                tag = await svc.set_fandom(
+                    tag_id, body["fandom_id"], merge=do_merge
+                )
         except TagMergeConflict as exc:
             return jsonify(
                 {
@@ -219,7 +273,12 @@ async def rename_tag(tag_id: int):
             return jsonify({"error": str(exc)}), 400
         await session.commit()
         return jsonify(
-            {"id": tag.id, "name": tag.name, "kind": tag.kind.value}
+            {
+                "id": tag.id,
+                "name": tag.name,
+                "kind": tag.kind.value,
+                "fandom_id": tag.fandom_id,
+            }
         )
 
 
@@ -326,6 +385,31 @@ def _series_err(exc: SeriesError):
     return jsonify({"error": msg}), status
 
 
+def _opt_int(body, key: str):
+    """(value, error) — value is None when absent, error is (json, status)."""
+    if not body or body.get(key) is None:
+        return None, None
+    try:
+        return int(body[key]), None
+    except (TypeError, ValueError):
+        return None, (jsonify({"error": f"{key} must be an integer"}), 400)
+
+
+def _parse_int_list(body, key: str, *, max_ids: int = 500):
+    """(list, error) for a required list of ints under `key`."""
+    if not body or key not in body:
+        return None, (jsonify({"error": f"{key} required"}), 400)
+    raw = body[key]
+    if not isinstance(raw, list) or not raw:
+        return None, (jsonify({"error": f"{key} must be a non-empty list"}), 400)
+    if len(raw) > max_ids:
+        return None, (jsonify({"error": f"too many ids (max {max_ids})"}), 400)
+    try:
+        return [int(x) for x in raw], None
+    except (TypeError, ValueError):
+        return None, (jsonify({"error": f"{key} must be integers"}), 400)
+
+
 @tags_bp.route("/series/<int:tag_id>/pages", methods=["GET"])
 async def series_pages(tag_id: int):
     async with get_session() as session:
@@ -366,15 +450,26 @@ async def series_remove(tag_id: int):
     return jsonify({"removed_count": n})
 
 
-@tags_bp.route("/series/<int:tag_id>/reorder", methods=["POST"])
-async def series_reorder(tag_id: int):
-    body = await request.get_json()
-    ids, err = _parse_bulk_ids(body, max_ids=500)
-    if err:
-        return err
+@tags_bp.route("/series/<int:tag_id>/pages/number", methods=["POST"])
+async def series_set_page_number(tag_id: int):
+    """Set one placed page's number — the operator's value (sparse, gaps
+    allowed); pass page_number: null to leave it unnumbered."""
+    body = await request.get_json() or {}
+    image_id, ierr = _opt_int(body, "image_id")
+    if ierr:
+        return ierr
+    if image_id is None:
+        return jsonify({"error": "image_id required"}), 400
+    if "page_number" not in body:
+        return jsonify({"error": "page_number required (may be null)"}), 400
+    page_number, perr = _opt_int(body, "page_number")
+    if perr:
+        return perr
     async with get_session() as session:
         try:
-            await SeriesService(session).reorder(tag_id, ids)
+            await SeriesService(session).set_page_number(
+                tag_id, image_id, page_number
+            )
         except SeriesError as exc:
             return _series_err(exc)
         await session.commit()
@@ -397,3 +492,201 @@ async def series_cover(tag_id: int):
             return _series_err(exc)
         await session.commit()
     return jsonify({"ok": True})
+
+
+# ---- chapter dividers (FC-6.x) -------------------------------------------
+# A chapter is a cosmetic divider anchored to the page that begins it; it owns
+# no pages. Page ordering follows each page's operator-set number (the
+# /pages/number endpoint), so there is no per-chapter reorder/merge — those are
+# gone.
+
+
+@tags_bp.route("/series/<int:tag_id>/chapters", methods=["POST"])
+async def series_chapter_create(tag_id: int):
+    body = await request.get_json() or {}
+    anchor, aerr = _opt_int(body, "anchor_image_id")
+    if aerr:
+        return aerr
+    if anchor is None:
+        return jsonify({"error": "anchor_image_id required"}), 400
+    title = body.get("title")
+    if title is not None and not isinstance(title, str):
+        return jsonify({"error": "title must be a string"}), 400
+    part, perr = _opt_int(body, "stated_part")
+    if perr:
+        return perr
+    async with get_session() as session:
+        try:
+            ch = await SeriesService(session).create_divider(
+                tag_id, anchor, title=title, stated_part=part,
+            )
+        except SeriesError as exc:
+            return _series_err(exc)
+        await session.commit()
+    return jsonify(ch)
+
+
+@tags_bp.route(
+    "/series/<int:tag_id>/chapters/<int:chapter_id>", methods=["PATCH"]
+)
+async def series_chapter_update(tag_id: int, chapter_id: int):
+    body = await request.get_json() or {}
+    kwargs: dict = {}
+    if "title" in body:
+        if body["title"] is not None and not isinstance(body["title"], str):
+            return jsonify({"error": "title must be a string"}), 400
+        kwargs.update(set_title=True, title=body["title"])
+    if "stated_part" in body:
+        part, perr = _opt_int(body, "stated_part")
+        if perr:
+            return perr
+        kwargs.update(set_part=True, stated_part=part)
+    if "anchor_image_id" in body:
+        anchor, aerr = _opt_int(body, "anchor_image_id")
+        if aerr:
+            return aerr
+        if anchor is None:
+            return jsonify(
+                {"error": "anchor_image_id must be an integer"}
+            ), 400
+        kwargs.update(set_anchor=True, anchor_image_id=anchor)
+    async with get_session() as session:
+        try:
+            await SeriesService(session).update_divider(
+                tag_id, chapter_id, **kwargs
+            )
+        except SeriesError as exc:
+            return _series_err(exc)
+        await session.commit()
+    return jsonify({"ok": True})
+
+
+@tags_bp.route(
+    "/series/<int:tag_id>/chapters/<int:chapter_id>", methods=["DELETE"]
+)
+async def series_chapter_delete(tag_id: int, chapter_id: int):
+    async with get_session() as session:
+        try:
+            await SeriesService(session).delete_divider(tag_id, chapter_id)
+        except SeriesError as exc:
+            return _series_err(exc)
+        await session.commit()
+    return jsonify({"ok": True})
+
+
+# ---- browse list + post→series flows (FC-6.2) -----------------------------
+
+
+@tags_bp.route("/series", methods=["GET"])
+async def series_list():
+    args = request.args
+    sort = args.get("sort", "recent")
+    if sort not in ("recent", "name", "size"):
+        return jsonify({"error": "sort must be recent|name|size"}), 400
+    artist_id = None
+    if args.get("artist_id") is not None:
+        try:
+            artist_id = int(args["artist_id"])
+        except ValueError:
+            return jsonify({"error": "artist_id must be an integer"}), 400
+    async with get_session() as session:
+        rows = await SeriesService(session).list_series(
+            sort=sort, artist_id=artist_id
+        )
+    return jsonify({"series": rows})
+
+
+@tags_bp.route("/series/from-post", methods=["POST"])
+async def series_from_post():
+    body = await request.get_json()
+    post_id, err = _opt_int(body, "post_id")
+    if err:
+        return err
+    if post_id is None:
+        return jsonify({"error": "post_id required"}), 400
+    async with get_session() as session:
+        try:
+            out = await SeriesService(session).promote_post_to_series(post_id)
+        except SeriesError as exc:
+            return _series_err(exc)
+        await session.commit()
+    return jsonify(out)
+
+
+@tags_bp.route("/series/<int:tag_id>/add-post", methods=["POST"])
+async def series_add_post(tag_id: int):
+    body = await request.get_json()
+    post_id, err = _opt_int(body, "post_id")
+    if err:
+        return err
+    if post_id is None:
+        return jsonify({"error": "post_id required"}), 400
+    async with get_session() as session:
+        try:
+            out = await SeriesService(session).add_post(tag_id, post_id)
+        except SeriesError as exc:
+            return _series_err(exc)
+        await session.commit()
+    return jsonify(out)
+
+
+@tags_bp.route("/series/<int:tag_id>/pending/place", methods=["POST"])
+async def series_place_pending(tag_id: int):
+    """Place staged (pending) pages into the run, numbered sequentially from
+    `start_page` in the given order (#789). start_page null → unnumbered."""
+    body = await request.get_json()
+    ids, err = _parse_bulk_ids(body, max_ids=500)
+    if err:
+        return err
+    start, serr = _opt_int(body, "start_page")
+    if serr:
+        return serr
+    async with get_session() as session:
+        try:
+            n = await SeriesService(session).place_pending(
+                tag_id, ids, start_page=start
+            )
+        except SeriesError as exc:
+            return _series_err(exc)
+        await session.commit()
+    return jsonify({"placed_count": n})
+
+
+# ---- suggestion queue (FC-6.3) --------------------------------------------
+
+
+@tags_bp.route("/series/suggestions", methods=["GET"])
+async def series_suggestions_list():
+    async with get_session() as session:
+        rows = await SeriesMatchService(session).list_pending()
+    return jsonify({"suggestions": rows})
+
+
+@tags_bp.route("/series/suggestions/<int:sid>/accept", methods=["POST"])
+async def series_suggestion_accept(sid: int):
+    async with get_session() as session:
+        try:
+            out = await SeriesMatchService(session).accept(sid)
+        except SeriesError as exc:
+            return _series_err(exc)
+        await session.commit()
+    return jsonify(out)
+
+
+@tags_bp.route("/series/suggestions/<int:sid>/dismiss", methods=["POST"])
+async def series_suggestion_dismiss(sid: int):
+    async with get_session() as session:
+        try:
+            await SeriesMatchService(session).dismiss(sid)
+        except SeriesError as exc:
+            return _series_err(exc)
+        await session.commit()
+    return jsonify({"ok": True})
+
+
+@tags_bp.route("/series/suggestions/rescan", methods=["POST"])
+async def series_suggestions_rescan():
+    from ..tasks.admin import rescan_series_suggestions_task
+
+    res = rescan_series_suggestions_task.delay()
+    return jsonify({"task_id": res.id})

backend/app/celery_app.py

@@ -30,6 +30,7 @@ def make_celery() -> Celery:
             "backend.app.tasks.maintenance",
             "backend.app.tasks.ml",
             "backend.app.tasks.download",
+            "backend.app.tasks.external",
             "backend.app.tasks.backup",
             "backend.app.tasks.admin",
             "backend.app.tasks.library_audit",
@@ -42,16 +43,51 @@ def make_celery() -> Celery:
             "backend.app.tasks.ml.*": {"queue": "ml"},
             "backend.app.tasks.thumbnail.*": {"queue": "thumbnail"},
             "backend.app.tasks.download.*": {"queue": "download"},
+            # External file-host fetches are downloads — same lane (they can run
+            # long, but the download worker already tolerates long backfills).
+            "backend.app.tasks.external.*": {"queue": "download"},
             "backend.app.tasks.scan.*": {"queue": "scan"},
+            # `maintenance` is the QUICK lane — recovery sweeps, vacuum, cleanup
+            # (concurrency-1 on the scheduler). The long one-shots (DB backups,
+            # library audits, admin maintenance: normalize/re-extract/cascade-
+            # delete) run on a SEPARATE `maintenance_long` lane + worker so they
+            # can never starve the quick self-healing sweeps (operator-flagged
+            # 2026-06-07: a 2h audit blocked vacuum/backup/normalize for hours).
             "backend.app.tasks.maintenance.*": {"queue": "maintenance"},
-            "backend.app.tasks.backup.*": {"queue": "maintenance"},
-            "backend.app.tasks.admin.*": {"queue": "maintenance"},
-            "backend.app.tasks.library_audit.*": {"queue": "maintenance"},
+            "backend.app.tasks.backup.*": {"queue": "maintenance_long"},
+            "backend.app.tasks.admin.*": {"queue": "maintenance_long"},
+            "backend.app.tasks.library_audit.*": {"queue": "maintenance_long"},
         },
         # Heavy ML tasks need fair dispatch — see ImageRepo's precedent.
         task_acks_late=True,
         worker_prefetch_multiplier=1,
+        # Broker resilience (2026-06-24): a swarm overlay-network blip after a
+        # redeploy left Redis healthy but transiently unreachable, and a worker
+        # starting in that window crash-looped on the initial broker connect
+        # (kombu OperationalError) instead of waiting it out — needing a manual
+        # Redis reset to recover. Retry the broker FOREVER (None) on startup and
+        # at runtime so a transient outage self-heals when routing returns,
+        # rather than the worker exiting.
         broker_connection_retry_on_startup=True,
+        broker_connection_retry=True,
+        broker_connection_max_retries=None,
+        # Redis-transport socket options (apply to the BROKER connection): a
+        # short connect timeout + TCP keepalive so a dead/blocked socket is
+        # noticed and retried, and a periodic health check that proactively
+        # reconnects a live worker through a network hiccup.
+        broker_transport_options={
+            "socket_connect_timeout": 5,
+            "socket_timeout": 30,
+            "socket_keepalive": True,
+            "retry_on_timeout": True,
+            "health_check_interval": 30,
+        },
+        # Same hardening for the Redis RESULT backend (separate connection pool).
+        redis_socket_connect_timeout=5,
+        redis_socket_timeout=30,
+        redis_socket_keepalive=True,
+        redis_retry_on_timeout=True,
+        redis_backend_health_check_interval=30,
         beat_schedule={
             "recover-interrupted-tasks": {
                 "task": "backend.app.tasks.maintenance.recover_interrupted_tasks",
@@ -73,6 +109,36 @@ def make_celery() -> Celery:
                 "task": "backend.app.tasks.ml.apply_allowlist_tags",
                 "schedule": 86400.0,
             },
+            "train-heads-nightly": {
+                "task": "backend.app.tasks.ml.scheduled_train_heads",
+                "schedule": 86400.0,  # passive cadence; manual retrain stays available
+            },
+            "apply-head-tags-daily": {
+                "task": "backend.app.tasks.ml.scheduled_apply_head_tags",
+                "schedule": 86400.0,  # no-op unless head_auto_apply_enabled
+            },
+            "recover-orphaned-gpu-jobs": {
+                "task": "backend.app.tasks.ml.recover_orphaned_gpu_jobs",
+                "schedule": 60.0,  # quick pickup of work a dead agent orphaned
+            },
+            "enqueue-ccip-backfill-hourly": {
+                "task": "backend.app.tasks.ml.enqueue_gpu_backfill",
+                "schedule": 3600.0,  # auto-feed new images (+ retry errored) so
+                "args": ("ccip",),   # the queue keeps moving without the button
+            },
+            "enqueue-siglip-backfill-daily": {
+                "task": "backend.app.tasks.ml.enqueue_gpu_backfill",
+                "schedule": 86400.0,  # drain the concept-crop back-catalogue +
+                "args": ("siglip",),  # retry failed embeds, no button needed
+            },
+            "ccip-auto-apply-daily": {
+                "task": "backend.app.tasks.ml.scheduled_ccip_auto_apply",
+                "schedule": 86400.0,  # no-op unless ccip_auto_apply_enabled
+            },
+            "snapshot-head-metrics-daily": {
+                "task": "backend.app.tasks.maintenance.snapshot_head_metrics",
+                "schedule": 86400.0,
+            },
             "integrity-verify-weekly": {
                 "task": "backend.app.tasks.maintenance.verify_integrity",
                 "schedule": 604800.0,  # weekly
@@ -97,6 +163,10 @@ def make_celery() -> Celery:
                 "task": "backend.app.tasks.maintenance.prune_task_runs",
                 "schedule": 86400.0,  # daily
             },
+            "vacuum-analyze": {
+                "task": "backend.app.tasks.maintenance.vacuum_analyze",
+                "schedule": 604800.0,  # weekly — reclaim dead-tuple bloat + refresh stats
+            },
             "fc3h-backup-db-nightly": {
                 "task": "backend.app.tasks.backup.backup_db_nightly",
                 "schedule": 3600.0,  # hourly tick; task self-gates on configured UTC hour
@@ -116,6 +186,18 @@ def make_celery() -> Celery:
                 "task": "backend.app.tasks.maintenance.recover_stalled_library_audit_runs",
                 "schedule": 300.0,
             },
+            "recover-stalled-tag-eval-runs": {
+                "task": "backend.app.tasks.maintenance.recover_stalled_tag_eval_runs",
+                "schedule": 300.0,
+            },
+            "recover-stalled-head-training-runs": {
+                "task": "backend.app.tasks.maintenance.recover_stalled_head_training_runs",
+                "schedule": 300.0,
+            },
+            "recover-stalled-head-auto-apply-runs": {
+                "task": "backend.app.tasks.maintenance.recover_stalled_head_auto_apply_runs",
+                "schedule": 300.0,
+            },
             "recover-stalled-import-batches": {
                 "task": "backend.app.tasks.maintenance.recover_stalled_import_batches",
                 "schedule": 300.0,
@@ -140,6 +222,21 @@ def make_celery() -> Celery:
                 "task": "backend.app.tasks.thumbnail.backfill_thumbnails",
                 "schedule": 86400.0,
             },
+            # External file-host downloads (#830): a steady sweep catches links
+            # the post-download hook missed (worker down, etc.); recovery re-tries
+            # dead links daily; retention prunes long-dead rows.
+            "extdl-sweep": {
+                "task": "backend.app.tasks.external.sweep_external_links",
+                "schedule": 600.0,  # every 10 min
+            },
+            "extdl-recover-daily": {
+                "task": "backend.app.tasks.external.recover_external_links",
+                "schedule": 86400.0,
+            },
+            "extdl-prune-daily": {
+                "task": "backend.app.tasks.external.prune_external_links",
+                "schedule": 86400.0,
+            },
         },
         timezone="UTC",
     )

backend/app/celery_signals.py

@@ -69,7 +69,15 @@ def _queue_for(task) -> str:
         return "ml"
     if name.startswith("backend.app.tasks.thumbnail."):
         return "thumbnail"
-    if name.startswith("backend.app.tasks.download."):
+    if name.startswith((
+        "backend.app.tasks.download.",
+        # External file-host fetches share the download lane (celery_app
+        # routes external.* → download). Mirror it here or TaskRun.queue
+        # lies 'default' for them, so per-queue dashboard filters and the
+        # per-queue threshold override miss them — the same gap the
+        # 2026-06-02 audit fixed for backup/admin/library_audit.
+        "backend.app.tasks.external.",
+    )):
         return "download"
     if name.startswith("backend.app.tasks.scan."):
         return "scan"

backend/app/models/__init__.py

@@ -2,24 +2,42 @@
 
 from .app_setting import AppSetting
 from .artist import Artist
+from .artist_visit import ArtistVisit
 from .backup_run import BackupRun
 from .base import Base
 from .credential import Credential
 from .download_event import DownloadEvent
+from .external_link import ExternalLink
+from .gpu_job import GpuJob
+from .head_auto_apply_run import HeadAutoApplyRun
+from .head_metric import HeadMetric
+from .head_metrics_snapshot import HeadMetricsSnapshot
+from .head_training_run import HeadTrainingRun
+from .image_prediction import ImagePrediction
 from .image_provenance import ImageProvenance
 from .image_record import ImageRecord
+from .image_region import ImageRegion
 from .import_batch import ImportBatch
 from .import_settings import ImportSettings
 from .import_task import ImportTask
 from .library_audit_run import LibraryAuditRun
 from .ml_settings import MLSettings
+from .patreon_failed_media import PatreonFailedMedia
+from .patreon_seen_media import PatreonSeenMedia
 from .post import Post
 from .post_attachment import PostAttachment
+from .series_chapter import SeriesChapter
 from .series_page import SeriesPage
+from .series_suggestion import SeriesSuggestion
 from .source import Source
+from .subscribestar_failed_media import SubscribeStarFailedMedia
+from .subscribestar_seen_media import SubscribeStarSeenMedia
 from .tag import Tag, TagKind, image_tag
 from .tag_alias import TagAlias
 from .tag_allowlist import TagAllowlist
+from .tag_eval_run import TagEvalRun
+from .tag_head import TagHead
+from .tag_positive_confirmation import TagPositiveConfirmation
 from .tag_reference_embedding import TagReferenceEmbedding
 from .tag_suggestion_rejection import TagSuggestionRejection
 from .task_run import TaskRun
@@ -28,25 +46,43 @@ __all__ = [
     "Base",
     "AppSetting",
     "Artist",
+    "ArtistVisit",
     "BackupRun",
     "Source",
     "Credential",
+    "PatreonFailedMedia",
+    "PatreonSeenMedia",
+    "SubscribeStarFailedMedia",
+    "SubscribeStarSeenMedia",
     "Post",
     "PostAttachment",
+    "SeriesChapter",
     "SeriesPage",
+    "SeriesSuggestion",
     "ImageRecord",
+    "ImagePrediction",
     "ImageProvenance",
+    "ImageRegion",
     "Tag",
     "TagKind",
     "image_tag",
     "DownloadEvent",
+    "ExternalLink",
+    "GpuJob",
     "ImportBatch",
     "ImportTask",
     "ImportSettings",
     "LibraryAuditRun",
     "MLSettings",
+    "HeadAutoApplyRun",
+    "HeadMetric",
+    "HeadMetricsSnapshot",
+    "HeadTrainingRun",
     "TagAlias",
     "TagAllowlist",
+    "TagEvalRun",
+    "TagHead",
+    "TagPositiveConfirmation",
     "TagReferenceEmbedding",
     "TagSuggestionRejection",
     "TaskRun",

backend/app/models/artist_visit.py

@@ -0,0 +1,36 @@
+"""ArtistVisit — per-artist 'last viewed' timestamp.
+
+Powers the "+N new since last visit" badge on the artists directory and
+the matching banner on `ArtistView`. One row per artist, single global
+operator. When the multi-user model lands, the PK widens to
+`(user_id, artist_id)` — currently aspirational only (no User model,
+no services/access.py); operator approved skipping `user_id` for now
+under rule #22 (breaking changes welcome).
+
+Seed at migration time: every existing artist gets `last_viewed_at = NOW()`
+so the badge starts at 0 across the board (no noisy "5000 unseen" on
+first deploy). New artists also auto-get a row via
+`ArtistService.find_or_create`.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import DateTime, ForeignKey, Integer, func
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class ArtistVisit(Base):
+    __tablename__ = "artist_visit"
+
+    artist_id: Mapped[int] = mapped_column(
+        Integer,
+        ForeignKey("artist.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    last_viewed_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        nullable=False,
+        server_default=func.now(),
+    )

backend/app/models/external_link.py

@@ -0,0 +1,73 @@
+"""ExternalLink — an off-platform file-host link found in a post body.
+
+Creators host the actual files (films, packs) on mega.nz / Google Drive /
+MediaFire / Dropbox / Pixeldrain and drop the link in the post text. This row
+is the record that the link existed (so nothing is silently dropped), the
+dedup + dead-letter ledger for fetching it, and the driver the download worker
+walks. `url` keeps the FULL link including the `#fragment` (mega's decryption
+key) — truncating it makes the file undownloadable.
+
+status lifecycle: pending → downloading → downloaded | failed | dead
+(too many attempts) | skipped (host disabled). `attachment_id` links the
+captured file once a download lands (SET NULL so deleting the attachment
+doesn't delete the link record).
+"""
+
+from datetime import datetime
+
+from sqlalchemy import (
+    DateTime,
+    Float,
+    ForeignKey,
+    Index,
+    Integer,
+    String,
+    Text,
+    func,
+    text,
+)
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+# Kept in sync with link_extract.SUPPORTED_HOSTS and the CHECK in migration 0049.
+HOSTS = ("mega", "gdrive", "mediafire", "dropbox", "pixeldrain")
+STATUSES = ("pending", "downloading", "downloaded", "failed", "skipped", "dead")
+
+
+class ExternalLink(Base):
+    __tablename__ = "external_link"
+    __table_args__ = (
+        # One row per (post, url). The full url (incl. #fragment) is the identity
+        # — the same file linked twice in a post collapses to one row.
+        Index("uq_external_link_post_url", "post_id", "url", unique=True),
+        Index("ix_external_link_status", "status"),
+    )
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    post_id: Mapped[int] = mapped_column(
+        ForeignKey("post.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    artist_id: Mapped[int | None] = mapped_column(
+        ForeignKey("artist.id", ondelete="SET NULL"), nullable=True, index=True
+    )
+    host: Mapped[str] = mapped_column(String(16), nullable=False)
+    url: Mapped[str] = mapped_column(Text, nullable=False)
+    label: Mapped[str | None] = mapped_column(Text, nullable=True)
+    status: Mapped[str] = mapped_column(
+        String(16), nullable=False, server_default="pending"
+    )
+    attempts: Mapped[int] = mapped_column(
+        Integer, nullable=False, server_default=text("0")
+    )
+    last_error: Mapped[str | None] = mapped_column(Text, nullable=True)
+    attachment_id: Mapped[int | None] = mapped_column(
+        ForeignKey("post_attachment.id", ondelete="SET NULL"), nullable=True
+    )
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    completed_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+    duration_seconds: Mapped[float | None] = mapped_column(Float, nullable=True)

backend/app/models/gpu_job.py

@@ -0,0 +1,50 @@
+"""GpuJob — a unit of GPU work the desktop agent pulls over HTTP (#114).
+
+The durable work list that lets the agent stay HTTP-only: the server enqueues a
+job per (image, task) — e.g. detect figures + CCIP-embed — and the agent LEASES a
+batch, computes on its GPU, then SUBMITS results, all over the already-exposed web
+API. Redis/Postgres stay private. A lease has an expiry; the lease query itself
+re-claims expired leases (agent died / stopped mid-batch), so the queue is
+self-healing without a separate sweep. One job is per ITEM; the agent fans a
+VIDEO out into per-frame instances internally (see image_region.frame_time).
+
+State: pending → leased → done | error (a failure under the attempt cap returns to
+pending for another agent).
+"""
+
+from datetime import datetime
+
+from sqlalchemy import DateTime, ForeignKey, Integer, String, Text, func
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class GpuJob(Base):
+    __tablename__ = "gpu_job"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    image_record_id: Mapped[int] = mapped_column(
+        ForeignKey("image_record.id", ondelete="CASCADE"), index=True
+    )
+    # What to compute, e.g. 'ccip' (detect figures + CCIP-embed) or 'siglip_region'.
+    task: Mapped[str] = mapped_column(String(32), nullable=False)
+    status: Mapped[str] = mapped_column(
+        String(16), nullable=False, default="pending", index=True
+    )
+    # pending | leased | done | error
+    lease_token: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    leased_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+    lease_expires_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+    attempts: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    error: Mapped[str | None] = mapped_column(Text, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

backend/app/models/head_auto_apply_run.py

@@ -0,0 +1,46 @@
+"""HeadAutoApplyRun — persisted lifecycle of an earned-auto-apply sweep (#114).
+
+A graduated head can apply its tag to images it scores above the head's
+auto-apply threshold, without a human. This row tracks one such sweep (or a
+dry-run PREVIEW of it) so the result survives navigation and the admin card can
+show what fired / what would fire. Mirrors HeadTrainingRun. State machine:
+running → ready / error. The `report` JSONB holds per-concept counts
+(applied / projected / scanned).
+"""
+
+from datetime import datetime
+from typing import Any
+
+from sqlalchemy import Boolean, DateTime, Integer, String, Text, func
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class HeadAutoApplyRun(Base):
+    __tablename__ = "head_auto_apply_run"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    # dry_run=True is a PREVIEW: scores + counts what WOULD apply, writes nothing
+    # (preview/apply parity, rule 93).
+    dry_run: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    params: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
+    status: Mapped[str] = mapped_column(
+        String(16), nullable=False, default="running", index=True
+    )
+    # running | ready | error
+    started_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    finished_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+    # Total tags applied across all heads this sweep (0 for a clean dry-run).
+    n_applied: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    # Per-concept breakdown: [{tag_id, name, applied, scanned, threshold}, ...].
+    report: Mapped[dict[str, Any] | None] = mapped_column(JSONB, nullable=True)
+    error: Mapped[str | None] = mapped_column(Text, nullable=True)
+    last_progress_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )

backend/app/models/head_metric.py

@@ -0,0 +1,32 @@
+"""HeadMetric — running correction counters per concept (#114 observability).
+
+Earned auto-apply fires graduated heads; to TUNE it we need to know how often a
+head's auto-applied tag was wrong (the operator removed it = a MISFIRE) and how
+often the operator had to add a tag a head exists for by hand (an UNDER-FIRE,
+the head missed it). image_tag.source is lost when a row is deleted, so these
+are captured as durable cumulative counters at correction time — they survive
+head retrain/prune (keyed by tag, not by the head row). The daily snapshot reads
+them into the time-series.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import DateTime, ForeignKey, Integer, func
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class HeadMetric(Base):
+    __tablename__ = "head_metric"
+
+    tag_id: Mapped[int] = mapped_column(
+        ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True
+    )
+    # An auto-applied (source='head_auto') tag the operator later REMOVED.
+    n_misfires: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    # A tag with a head that the operator added by HAND (the head missed it).
+    n_underfires: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

backend/app/models/head_metrics_snapshot.py

@@ -0,0 +1,38 @@
+"""HeadMetricsSnapshot — a daily per-concept time-series point (#114).
+
+The "amount of change over time" reporting the operator asked for: once a day,
+record each concept's auto-applied VOLUME (current head_auto tags), cumulative
+misfires/under-fires, and the head's measured quality. Plotting these rows over
+time shows whether auto-apply is landing better/worse and whether tagging more is
+sharpening a concept — the signal for tuning the precision target + support floor.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import DateTime, Float, ForeignKey, Integer, String, func
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class HeadMetricsSnapshot(Base):
+    __tablename__ = "head_metrics_snapshot"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    tag_id: Mapped[int] = mapped_column(
+        ForeignKey("tag.id", ondelete="CASCADE"), index=True
+    )
+    # Denormalized so a snapshot stays readable even if the tag is later renamed.
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    snapshot_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now(), index=True
+    )
+    # Current count of source='head_auto' applications still standing.
+    n_auto_applied: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    n_misfires: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    n_underfires: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    # The head's measured quality at snapshot time (null if no head exists).
+    ap: Mapped[float | None] = mapped_column(Float, nullable=True)
+    precision_cv: Mapped[float | None] = mapped_column(Float, nullable=True)
+    recall: Mapped[float | None] = mapped_column(Float, nullable=True)
+    n_pos: Mapped[int | None] = mapped_column(Integer, nullable=True)

backend/app/models/head_training_run.py

@@ -0,0 +1,44 @@
+"""HeadTrainingRun — persisted lifecycle of a head-training batch (#114).
+
+Mirrors TagEvalRun so the run SURVIVES navigation and the admin card can show
+live + historical status instead of holding it in transient frontend state.
+Training is idempotent (it upserts tag_head rows), so a SIGKILL'd run is harmless
+— a maintenance recovery sweep flips a stalled `running` row to `error`, and the
+next run re-trains. State machine: running → ready / error.
+"""
+
+from datetime import datetime
+from typing import Any
+
+from sqlalchemy import DateTime, Integer, String, Text, func
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class HeadTrainingRun(Base):
+    __tablename__ = "head_training_run"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    # Training parameters: {min_positives, neg_ratio, precision_target, ...}.
+    params: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
+    status: Mapped[str] = mapped_column(
+        String(16), nullable=False, default="running", index=True
+    )
+    # running | ready | error
+    started_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    finished_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+    # How many concepts got a (re)trained head vs were skipped (too few labels).
+    n_trained: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    n_skipped: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    error: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # Last time the task made progress — the recovery sweep tells a live run from
+    # a SIGKILL'd one by this (mirrors TagEvalRun).
+    last_progress_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )

backend/app/models/image_prediction.py

@@ -0,0 +1,37 @@
+"""ImagePrediction — one row per (image, tagger vocab prediction).
+
+Replaces the image_record.tagger_predictions JSON blob (#768). Storing the
+raw Camie/booru vocab name (not a tag_id) preserves the suggestion read
+path's semantics: raw_name → canonical Tag resolution happens at read time
+via the alias map, and accepting a prediction can CREATE the Tag. The store
+floor (ml_settings.tagger_store_floor) is applied at WRITE time, so only
+predictions >= the floor land here.
+"""
+
+from sqlalchemy import Float, ForeignKey, Index, String, UniqueConstraint
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class ImagePrediction(Base):
+    __tablename__ = "image_prediction"
+    __table_args__ = (
+        UniqueConstraint(
+            "image_record_id", "raw_name", name="image_raw_name",
+        ),
+        # Per-image read (suggestion build) and the "images with tag X above
+        # Y" query the JSON blob never allowed.
+        Index("ix_image_prediction_image", "image_record_id"),
+        Index("ix_image_prediction_name_score", "raw_name", "score"),
+    )
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    image_record_id: Mapped[int] = mapped_column(
+        ForeignKey("image_record.id", ondelete="CASCADE"), nullable=False,
+    )
+    # The raw tagger vocab key (booru form) — NOT a tag_id. Resolved to a
+    # canonical Tag at read time, exactly as the old JSON keys were.
+    raw_name: Mapped[str] = mapped_column(String(255), nullable=False)
+    category: Mapped[str] = mapped_column(String(64), nullable=False)
+    score: Mapped[float] = mapped_column(Float, nullable=False)

backend/app/models/image_provenance.py

@@ -41,6 +41,16 @@ class ImageProvenance(Base):
     source_id: Mapped[int | None] = mapped_column(
         ForeignKey("source.id", ondelete="SET NULL"), nullable=True, index=True
     )
+    # The archive PostAttachment this image was extracted FROM, when it came
+    # out of a .zip/.rar rather than as a loose file (milestone #87). Lets the
+    # provenance UI show the exact archive a file lives inside instead of every
+    # attachment on the post. NULL for loose downloads and pre-backfill rows.
+    # SET NULL so deleting the archive attachment never destroys the (image,
+    # post) edge — it just forgets which archive it came from.
+    from_attachment_id: Mapped[int | None] = mapped_column(
+        ForeignKey("post_attachment.id", ondelete="SET NULL"),
+        nullable=True, index=True,
+    )
     captured_metadata: Mapped[dict | None] = mapped_column(JSON, nullable=True)
     captured_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()

backend/app/models/image_record.py

@@ -13,6 +13,7 @@ from sqlalchemy import (
     BigInteger,
     DateTime,
     Enum,
+    Float,
     ForeignKey,
     Integer,
     String,
@@ -39,6 +40,10 @@ class ImageRecord(Base):
     mime: Mapped[str] = mapped_column(String(64), nullable=False)
     width: Mapped[int | None] = mapped_column(Integer, nullable=True)
     height: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    # Video container duration (seconds); NULL for images. The Tier-1 video
+    # near-dup key (#871): two videos of the same artist with matching duration
+    # (+ aspect) are the same content across re-encodes — dedup like image pHash.
+    duration_seconds: Mapped[float | None] = mapped_column(Float, nullable=True)
 
     # Integrity verification status. FC-2e populates this; FC-2a leaves rows at 'unknown'.
     # Values: 'unknown' (default), 'ok', 'corrupt', 'failed_verification'.
@@ -49,6 +54,18 @@ class ImageRecord(Base):
     # Thumbnail (populated by FC-2)
     thumbnail_path: Mapped[str | None] = mapped_column(Text, nullable=True)
 
+    # Source provenance for downloaded media (#830 Phase 2). `source_url` is the
+    # CDN/origin URL the file was fetched from (debugging + future re-fetch).
+    # `source_filehash` is the URL's 32-hex CDN identity segment
+    # (utils.paths.filehash_from_url) — the JOIN KEY that maps a post body's
+    # inline `<img src=CDN>` back to this local copy so the rendered body serves
+    # our stored image instead of hotlinking the public source. Indexed for the
+    # render-time lookup. NULL for filesystem-imported / pre-Phase-2 rows.
+    source_url: Mapped[str | None] = mapped_column(Text, nullable=True)
+    source_filehash: Mapped[str | None] = mapped_column(
+        String(32), nullable=True, index=True
+    )
+
     # Origin / provenance pointers
     origin: Mapped[str] = mapped_column(Enum(*ORIGIN_CHOICES, name="origin_enum"), nullable=False)
     primary_post_id: Mapped[int | None] = mapped_column(
@@ -60,8 +77,10 @@ class ImageRecord(Base):
         ForeignKey("artist.id", ondelete="SET NULL"), nullable=True, index=True
     )
 
-    # ML fields (populated by FC-2's ml-worker)
-    tagger_predictions: Mapped[dict | None] = mapped_column(JSON, nullable=True)
+    # ML fields (populated by FC-2's ml-worker). Per-tag predictions live in the
+    # normalized image_prediction table (#768) — the tagger_predictions JSON
+    # column was dropped in migration 0046. tagger_model_version stays as the
+    # "has this been tagged / is it current?" signal the backfill sweep reads.
     tagger_model_version: Mapped[str | None] = mapped_column(String(128), nullable=True)
     # 1152 = SigLIP-so400m embedding dim. Swapping models in FC-2 may require
     # a column-width migration.
@@ -74,6 +93,17 @@ class ImageRecord(Base):
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )
+    # Denormalized gallery sort key = COALESCE(primary post's post_date,
+    # created_at) (alembic 0035). The gallery used to compute this as a
+    # COALESCE across the Post outer join on every /scroll, which can't use
+    # an index and re-sorted a large slice of the library per page (×10 with
+    # the old serial batching). Materializing it lets the cursor scroll read
+    # ix_image_record_effective_date directly. Maintained by the importer
+    # (services/importer.py _apply_sidecar) when a primary post with a date
+    # is linked; plain inserts keep the created_at-equivalent server default.
+    effective_date: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
     updated_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True),
         nullable=False,

backend/app/models/image_region.py

@@ -0,0 +1,62 @@
+"""ImageRegion — a detected/proposed sub-region of an image + its crop embedding.
+
+The storage backbone of the crop pipeline (#114). A region is a normalized bbox
+plus the embedding of its crop:
+- kind='face' / 'figure' → embedded by CCIP for cross-artist character identity.
+- kind='concept' → embedded by SigLIP, a localized instance for a concept head's
+  bag-of-embeddings (a concept is "present if ANY instance matches").
+One row carries the embedding appropriate to its kind (the other is null). The
+bbox doubles as grounded-tag provenance (hover a tag → highlight its region; a
+wrong box is a precise negative). The GPU agent writes these via the job API;
+the few-shot character matcher + bag scorer read them — both server-side, no GPU.
+"""
+
+from datetime import datetime
+
+from pgvector.sqlalchemy import Vector
+from sqlalchemy import DateTime, Float, ForeignKey, Integer, String, func
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+CCIP_DIM = 768       # deepghs/imgutils CCIP character embedding
+SIGLIP_DIM = 1152    # matches image_record.siglip_embedding
+
+
+class ImageRegion(Base):
+    __tablename__ = "image_region"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    image_record_id: Mapped[int] = mapped_column(
+        ForeignKey("image_record.id", ondelete="CASCADE"), index=True
+    )
+    # 'frame' (a whole video frame → SigLIP bag) | 'face' | 'figure' (→ CCIP
+    # character id) | 'concept' (→ SigLIP head bag).
+    kind: Mapped[str] = mapped_column(String(16), nullable=False)
+    # For video/animated media: the source frame's timestamp in SECONDS. NULL for
+    # static images. Lets a video be a BAG of per-frame instances (fixes the
+    # mean-embedding muddle) + grounds a tag to "appears at 0:42".
+    frame_time: Mapped[float | None] = mapped_column(Float, nullable=True)
+    # Normalized bbox in [0,1]: top-left (rx, ry) + size (rw, rh). Named rx/ry/…
+    # rather than x/y/by to dodge SQL keyword ambiguity ('by').
+    rx: Mapped[float] = mapped_column(Float, nullable=False)
+    ry: Mapped[float] = mapped_column(Float, nullable=False)
+    rw: Mapped[float] = mapped_column(Float, nullable=False)
+    rh: Mapped[float] = mapped_column(Float, nullable=False)
+    # Proposer/detector confidence (null for deterministic proposers).
+    score: Mapped[float | None] = mapped_column(Float, nullable=True)
+    # Version stamps so a re-detect / re-crop / re-embed can be gated (compute
+    # once; only redo when the producing model version changes).
+    detector_version: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    crop_version: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    embedding_version: Mapped[str | None] = mapped_column(String(128), nullable=True)
+    # Exactly one is set, per kind.
+    ccip_embedding: Mapped[list[float] | None] = mapped_column(
+        Vector(CCIP_DIM), nullable=True
+    )
+    siglip_embedding: Mapped[list[float] | None] = mapped_column(
+        Vector(SIGLIP_DIM), nullable=True
+    )
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

backend/app/models/import_settings.py

@@ -64,6 +64,34 @@ class ImportSettings(Base):
         Integer, nullable=False, default=3,
     )
 
+    # FC-6.3 series continuation matcher. enabled gates the rescan; threshold is
+    # the weighted-score cut-off (0..1) above which a pending suggestion is made.
+    series_suggest_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=True,
+    )
+    series_suggest_threshold: Mapped[float] = mapped_column(
+        Float, nullable=False, default=0.5,
+    )
+
+    # #830 off-platform file-host downloads — per-host enable lever (default on,
+    # rule #26). Column names are extdl_<host>_enabled so the worker reads them
+    # via getattr(settings, f"extdl_{host}_enabled", True).
+    extdl_mega_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=True, server_default="true",
+    )
+    extdl_gdrive_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=True, server_default="true",
+    )
+    extdl_mediafire_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=True, server_default="true",
+    )
+    extdl_dropbox_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=True, server_default="true",
+    )
+    extdl_pixeldrain_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=True, server_default="true",
+    )
+
     @classmethod
     async def load(cls, session) -> ImportSettings:
         """The singleton settings row (id=1), via an async session."""

backend/app/models/library_audit_run.py

@@ -35,3 +35,10 @@ class LibraryAuditRun(Base):
     matched_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
     matched_ids: Mapped[list[int]] = mapped_column(JSONB, nullable=False, default=list)
     error: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # Chunked-scan state (alembic 0039): keyset cursor the next chunk resumes
+    # from, and the last time a chunk made progress (so the recovery sweep can
+    # tell a progressing multi-chunk audit from a stuck one).
+    resume_after_id: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    last_progress_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True,
+    )

backend/app/models/ml_settings.py

@@ -2,7 +2,15 @@
 
 from datetime import datetime
 
-from sqlalchemy import CheckConstraint, DateTime, Float, Integer, String, func
+from sqlalchemy import (
+    Boolean,
+    CheckConstraint,
+    DateTime,
+    Float,
+    Integer,
+    String,
+    func,
+)
 from sqlalchemy.orm import Mapped, mapped_column
 
 from .base import Base
@@ -28,9 +36,71 @@ class MLSettings(Base):
     centroid_similarity_threshold: Mapped[float] = mapped_column(
         Float, nullable=False, default=0.55
     )
+    # Ingest floor: tagger predictions below this confidence are not stored
+    # (tagger.Tagger.infer). Default 0.70 — the suggestion path already
+    # filters at 0.70 and the centroid/learned path covers low-confidence
+    # preferred tags, so the sub-0.70 tail is redundant weight (it had
+    # bloated image_record's TOAST to ~100 GB; plan-task #764). Operator-
+    # tunable via Settings → ML; must stay ≤ the suggestion thresholds.
+    tagger_store_floor: Mapped[float] = mapped_column(
+        Float, nullable=False, default=0.70
+    )
     min_reference_images: Mapped[int] = mapped_column(
         Integer, nullable=False, default=5
     )
+    # Video tagging (#747). Sample one frame every N seconds (fixed CADENCE, not a
+    # fixed count) so a tag's frame-presence reflects real screen time regardless
+    # of video length; cap the total so a long video can't explode into hundreds
+    # of inferences (the cadence stretches past the cap). A tag is kept only if it
+    # appears in >= video_min_tag_frames sampled frames (≈ that many × interval
+    # seconds on screen) — duration-independent noise rejection. Operator-tunable.
+    video_frame_interval_seconds: Mapped[float] = mapped_column(
+        Float, nullable=False, default=4.0
+    )
+    video_max_frames: Mapped[int] = mapped_column(
+        Integer, nullable=False, default=64
+    )
+    video_min_tag_frames: Mapped[int] = mapped_column(
+        Integer, nullable=False, default=3
+    )
+    # Tagging-v2 head training (#114). The head is the suggestion source that
+    # LEARNS from the operator's tags (replacing Camie + centroid). A concept
+    # needs >= head_min_positives labelled images before a head is trained;
+    # head_auto_apply_precision is the precision bar a head must clear (at some
+    # operating point) to "graduate" into earned auto-apply. Operator-tunable.
+    head_min_positives: Mapped[int] = mapped_column(
+        Integer, nullable=False, default=8
+    )
+    head_auto_apply_precision: Mapped[float] = mapped_column(
+        Float, nullable=False, default=0.97
+    )
+    # Earned auto-apply (#114). A graduated head fires (tags images without a
+    # human) when this master switch is on AND the head has at least
+    # head_auto_apply_min_positives clean labels — so a precise-looking but
+    # under-supported low-N head can't spray tags across the library. ON by
+    # default (operator-asked 2026-06-29: opt-OUT, not opt-in); the support +
+    # measured-precision gates keep it safe, and every auto-tag is reversible.
+    head_auto_apply_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=True
+    )
+    head_auto_apply_min_positives: Mapped[int] = mapped_column(
+        Integer, nullable=False, default=30
+    )
+    # CCIP character-match cosine cut (#114). 0.85 default — the v1 flat 0.75
+    # over-fired (high-reference characters matched a scatter of images); 0.85
+    # keeps the confident single-character matches. Tunable from the agent card.
+    ccip_match_threshold: Mapped[float] = mapped_column(
+        Float, nullable=False, default=0.85
+    )
+    # CCIP auto-apply (#114). Confident matches (>= ccip_auto_apply_threshold,
+    # above the suggest cut) auto-tag on a daily sweep. ON by default (opt-out);
+    # single-character references + the high bar keep it safe, every tag reversible.
+    ccip_auto_apply_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=True
+    )
+    ccip_auto_apply_threshold: Mapped[float] = mapped_column(
+        Float, nullable=False, default=0.92
+    )
     tagger_model_version: Mapped[str] = mapped_column(
         String(128), nullable=False, default="camie-tagger-v2"
     )

backend/app/models/patreon_failed_media.py

@@ -0,0 +1,45 @@
+"""PatreonFailedMedia — per-source dead-letter ledger of Patreon media that
+keeps failing to download/validate.
+
+Plan #705 (#7). A media that fails every walk (404'd CDN URL, deleted post,
+geo-blocked Mux stream, persistently-corrupt bytes) would otherwise re-error
+forever and re-burn backfill chunks. After ``attempts`` reaches the dead-letter
+threshold the ingester skips it on routine tick/backfill walks (recovery still
+re-attempts it — the operator's "try everything again"). A later clean download
+clears the row (the media recovered).
+
+`filehash` is the same per-media key the seen-ledger uses (32-hex CDN MD5, or a
+``video:`` / ``post:filename`` synthesized key) — hence String(128). UNIQUE
+(source_id, filehash) is the upsert key.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import ForeignKey, Integer, String, Text, UniqueConstraint, func
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy.types import DateTime
+
+from .base import Base
+
+
+class PatreonFailedMedia(Base):
+    __tablename__ = "patreon_failed_media"
+    __table_args__ = (
+        UniqueConstraint(
+            "source_id", "filehash", name="uq_patreon_failed_media_source_id"
+        ),
+    )
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    source_id: Mapped[int] = mapped_column(
+        ForeignKey("source.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    filehash: Mapped[str] = mapped_column(String(128), nullable=False)
+    attempts: Mapped[int] = mapped_column(Integer, nullable=False, default=1)
+    last_error: Mapped[str | None] = mapped_column(Text, nullable=True)
+    first_failed_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    last_failed_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

backend/app/models/patreon_seen_media.py

@@ -0,0 +1,38 @@
+"""PatreonSeenMedia — per-source ledger of Patreon media already
+downloaded+processed.
+
+Replaces gallery-dl's archive.sqlite3 with our own queryable table so
+routine walks can skip media we've already ingested (and a future
+"recovery" mode can deliberately bypass the ledger to re-walk).
+
+`filehash` is normally a Patreon CDN MD5 (32 hex chars), but videos —
+which have no stable content hash at discovery time — use a sentinel of
+the form ``video:<post_id>:<media_id>``, hence String(128) rather than 32.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import ForeignKey, Integer, String, UniqueConstraint, func
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy.types import DateTime
+
+from .base import Base
+
+
+class PatreonSeenMedia(Base):
+    __tablename__ = "patreon_seen_media"
+    __table_args__ = (
+        # Dedup key the downloader upserts against: one ledger row per
+        # (source, media). A second sighting of the same media is a no-op.
+        UniqueConstraint("source_id", "filehash", name="uq_patreon_seen_media_source_id"),
+    )
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    source_id: Mapped[int] = mapped_column(
+        ForeignKey("source.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    filehash: Mapped[str] = mapped_column(String(128), nullable=False)
+    post_id: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    seen_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

backend/app/models/post_attachment.py

@@ -14,10 +14,12 @@ from sqlalchemy import (
     BigInteger,
     DateTime,
     ForeignKey,
+    Index,
     Integer,
     String,
     Text,
     func,
+    text,
 )
 from sqlalchemy.orm import Mapped, mapped_column
 
@@ -26,6 +28,24 @@ from .base import Base
 
 class PostAttachment(Base):
     __tablename__ = "post_attachment"
+    # Dedup is PER-POST, not global (2026-06-08): the same non-art file attached
+    # to many posts gets one row per post over a single sha-addressed blob, so no
+    # post is left a bare shell. Partial uniques: (post_id, sha256) for real posts;
+    # (sha256) alone for the NULL-post filesystem case (one row per file there).
+    __table_args__ = (
+        Index(
+            "uq_post_attachment_post_sha",
+            "post_id", "sha256",
+            unique=True,
+            postgresql_where=text("post_id IS NOT NULL"),
+        ),
+        Index(
+            "uq_post_attachment_null_post_sha",
+            "sha256",
+            unique=True,
+            postgresql_where=text("post_id IS NULL"),
+        ),
+    )
 
     id: Mapped[int] = mapped_column(Integer, primary_key=True)
     post_id: Mapped[int | None] = mapped_column(
@@ -35,7 +55,7 @@ class PostAttachment(Base):
         ForeignKey("artist.id", ondelete="SET NULL"), nullable=True, index=True
     )
     sha256: Mapped[str] = mapped_column(
-        String(64), nullable=False, unique=True, index=True
+        String(64), nullable=False, index=True
     )
     path: Mapped[str] = mapped_column(Text, nullable=False)
     original_filename: Mapped[str] = mapped_column(Text, nullable=False)

backend/app/models/series_chapter.py

@@ -0,0 +1,47 @@
+"""SeriesChapter — a cosmetic chapter DIVIDER within a series (FC-6.x reframe).
+
+A series is ONE flat, series-global ordered run of SeriesPages. A chapter is NOT
+a container — it owns no pages. It is a labeled divider anchored to the page that
+BEGINS the chapter (anchor_page_id → series_page): "a new chapter starts here."
+A page's chapter is derived at read time as the nearest preceding divider.
+
+Dividers never affect page ordering or the series-global page numbers; they stay
+pinned to their anchor page across reorders. anchor_page_id is UNIQUE — at most
+one chapter begins at a given page — and FK-cascades, so removing the anchor page
+from the series drops the divider (the chapter merges into the preceding run).
+
+title is the optional chapter name; stated_part is the optional operator-facing
+"Part N" label (shown instead of a derived ordinal when set).
+"""
+
+from datetime import datetime
+
+from sqlalchemy import DateTime, ForeignKey, Integer, Text, func
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class SeriesChapter(Base):
+    __tablename__ = "series_chapter"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    series_tag_id: Mapped[int] = mapped_column(
+        ForeignKey("tag.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    anchor_page_id: Mapped[int] = mapped_column(
+        ForeignKey("series_page.id", ondelete="CASCADE"),
+        nullable=False,
+        unique=True,
+    )
+    title: Mapped[str | None] = mapped_column(Text, nullable=True)
+    stated_part: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        nullable=False,
+        server_default=func.now(),
+        onupdate=func.now(),
+    )

backend/app/models/series_page.py

@@ -1,14 +1,20 @@
 """SeriesPage — ordered image membership for a series-kind Tag.
 
-A series IS a Tag with kind='series'; series_page gives it ordered pages.
-An image belongs to at most one series (UNIQUE image_id). Cover = the
-lowest page_number. page_number is an ordering key only (not unique) —
-reorder rewrites 1..N wholesale.
+A series IS a Tag with kind='series'; series_page gives it a SINGLE flat,
+series-global ordered run of pages (FC-6.x divider reframe). An image belongs to
+at most one series (UNIQUE image_id). Reading order is `page_number` alone — a
+series-wide ordering key (not unique), rewritten 1..N wholesale on reorder so a
+reorder can't transiently collide on an index.
+
+Chapters are cosmetic DIVIDERS anchored to a page (see SeriesChapter); they do
+NOT own pages, so there is no chapter_id here — a page's chapter is derived at
+read time as the nearest preceding divider. stated_page carries the printed page
+number parsed from the source post, nullable when unknown.
 """
 
 from datetime import datetime
 
-from sqlalchemy import DateTime, ForeignKey, Integer, func
+from sqlalchemy import DateTime, ForeignKey, Integer, String, func
 from sqlalchemy.orm import Mapped, mapped_column
 
 from .base import Base
@@ -26,7 +32,13 @@ class SeriesPage(Base):
         nullable=False,
         unique=True,
     )
-    page_number: Mapped[int] = mapped_column(Integer, nullable=False)
+    # 'placed' = in the series-global run (page_number set); 'pending' = staged
+    # from a post awaiting the operator's sort (page_number NULL). (#789 P2)
+    status: Mapped[str] = mapped_column(
+        String(16), nullable=False, server_default="placed"
+    )
+    page_number: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    stated_page: Mapped[int | None] = mapped_column(Integer, nullable=True)
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )

backend/app/models/series_suggestion.py

@@ -0,0 +1,55 @@
+"""SeriesSuggestion — a confirm-only "this post may continue this series" hint.
+
+The matcher (FC-6.3) scores a (post, candidate series) pair from several weighted
+signals and, above the configured threshold, records a pending suggestion. The
+operator confirms (→ the post is added as a chapter) or dismisses it; FC never
+files a post into a series on its own. status is a plain string (no Postgres
+ENUM — see the check-existing-enums lesson): pending | added | dismissed.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import (
+    JSON,
+    DateTime,
+    Float,
+    ForeignKey,
+    Integer,
+    String,
+    UniqueConstraint,
+    func,
+)
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class SeriesSuggestion(Base):
+    __tablename__ = "series_suggestion"
+    __table_args__ = (
+        UniqueConstraint(
+            "post_id", "series_tag_id", name="uq_series_suggestion_post_series"
+        ),
+    )
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    post_id: Mapped[int] = mapped_column(
+        ForeignKey("post.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    series_tag_id: Mapped[int] = mapped_column(
+        ForeignKey("tag.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    score: Mapped[float] = mapped_column(Float, nullable=False)
+    signals: Mapped[dict | None] = mapped_column(JSON, nullable=True)
+    status: Mapped[str] = mapped_column(
+        String(16), nullable=False, server_default="pending", index=True
+    )
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        nullable=False,
+        server_default=func.now(),
+        onupdate=func.now(),
+    )

backend/app/models/subscribestar_failed_media.py

@@ -0,0 +1,44 @@
+"""SubscribeStarFailedMedia — per-source dead-letter ledger of SubscribeStar
+media that keeps failing to download/validate.
+
+Mirror of PatreonFailedMedia. Media that fails every walk (404'd CDN URL,
+deleted post, persistently-corrupt bytes) would otherwise re-error forever and
+re-burn backfill chunks. After ``attempts`` reaches the dead-letter threshold
+the ingester skips it on routine tick/backfill walks (recovery still
+re-attempts). A later clean download clears the row.
+
+`filehash` is the same per-media key the seen-ledger uses (CDN content hash or a
+synthesized ``<post_id>:<filename>`` key) — hence String(128). UNIQUE
+(source_id, filehash) is the upsert key.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import ForeignKey, Integer, String, Text, UniqueConstraint, func
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy.types import DateTime
+
+from .base import Base
+
+
+class SubscribeStarFailedMedia(Base):
+    __tablename__ = "subscribestar_failed_media"
+    __table_args__ = (
+        UniqueConstraint(
+            "source_id", "filehash", name="uq_subscribestar_failed_media_source_id"
+        ),
+    )
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    source_id: Mapped[int] = mapped_column(
+        ForeignKey("source.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    filehash: Mapped[str] = mapped_column(String(128), nullable=False)
+    attempts: Mapped[int] = mapped_column(Integer, nullable=False, default=1)
+    last_error: Mapped[str | None] = mapped_column(Text, nullable=True)
+    first_failed_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    last_failed_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

backend/app/models/subscribestar_seen_media.py

@@ -0,0 +1,40 @@
+"""SubscribeStarSeenMedia — per-source ledger of SubscribeStar media already
+downloaded+processed.
+
+Mirror of PatreonSeenMedia for the SubscribeStar native ingester (replacing
+gallery-dl). One queryable row per (source, media) so routine walks skip media
+we've already ingested; recovery mode bypasses the ledger to re-walk.
+
+`filehash` is a CDN content hash when the media URL carries one, else a
+synthesized ``<post_id>:<filename>`` key (SubscribeStar URLs aren't always
+content-addressed) — hence String(128) rather than 32.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import ForeignKey, Integer, String, UniqueConstraint, func
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy.types import DateTime
+
+from .base import Base
+
+
+class SubscribeStarSeenMedia(Base):
+    __tablename__ = "subscribestar_seen_media"
+    __table_args__ = (
+        # Dedup key the downloader upserts against: one ledger row per
+        # (source, media). A second sighting of the same media is a no-op.
+        UniqueConstraint(
+            "source_id", "filehash", name="uq_subscribestar_seen_media_source_id"
+        ),
+    )
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    source_id: Mapped[int] = mapped_column(
+        ForeignKey("source.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    filehash: Mapped[str] = mapped_column(String(128), nullable=False)
+    post_id: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    seen_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

backend/app/models/tag_alias.py

@@ -1,6 +1,6 @@
 """TagAlias — maps a model's (name, category) prediction to the operator's
-canonical tag. Resolved at suggestion-read time so raw predictions stay
-unmolested in image_record.tagger_predictions.
+canonical tag. Resolved at suggestion-read time so the raw predictions stored
+in image_prediction stay unmolested.
 """
 
 from datetime import datetime

backend/app/models/tag_allowlist.py

@@ -22,7 +22,11 @@ class TagAllowlist(Base):
     tag_id: Mapped[int] = mapped_column(
         ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True
     )
-    min_confidence: Mapped[float] = mapped_column(Float, nullable=False, default=0.95)
+    # Default auto-apply threshold for a newly-accepted tag. 0.90 (lowered from
+    # 0.95 on operator evidence 2026-06-07: 0.95 was too strict and skipped
+    # confident-enough applications). Per-tag value is still tunable in the
+    # allowlist table; existing rows keep whatever they were stored with.
+    min_confidence: Mapped[float] = mapped_column(Float, nullable=False, default=0.90)
     added_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )

backend/app/models/tag_eval_run.py

@@ -0,0 +1,45 @@
+"""TagEvalRun — persisted lifecycle of a head-vs-centroid tagging eval (#1130).
+
+Mirrors LibraryAuditRun so the result SURVIVES navigation: the run + its full
+report live in this row, and the admin card rehydrates from it on mount instead
+of holding the report in transient frontend state. State machine:
+running → ready / error. The async ml-queue task writes `report` (JSONB) when
+done; a maintenance recovery sweep flips a stalled `running` row to `error`.
+"""
+
+from datetime import datetime
+from typing import Any
+
+from sqlalchemy import DateTime, Integer, String, Text, func
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class TagEvalRun(Base):
+    __tablename__ = "tag_eval_run"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    # The eval parameters: {concepts: [...], curve_points: [...], neg_ratio,
+    # cv_folds, ...} — echoed back so the report is self-describing.
+    params: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
+    status: Mapped[str] = mapped_column(
+        String(16), nullable=False, default="running", index=True,
+    )
+    # running | ready | error
+    started_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now(),
+    )
+    finished_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True,
+    )
+    # The full result: per-concept metrics (head vs centroid), learning-curve
+    # points, and example image ids. Null until the task finishes.
+    report: Mapped[dict[str, Any] | None] = mapped_column(JSONB, nullable=True)
+    error: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # Last time the task made progress — the recovery sweep tells a live run
+    # from a SIGKILL'd one by this (mirrors LibraryAuditRun).
+    last_progress_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True,
+    )

backend/app/models/tag_head.py

@@ -0,0 +1,77 @@
+"""TagHead — a small per-concept classifier trained on the operator's tags.
+
+Milestone #114, tagging-v2: the production form of the head the eval (#1130)
+proved. One row per concept (general or character) that has enough labelled
+positives. The head is a logistic-regression boundary over the FROZEN SigLIP
+embedding (L2-normalized), trained on the operator's positives + negatives
+(rejections + sampled unlabeled). It REPLACES the Camie prediction + per-tag
+centroid as the suggestion source — and unlike them it LEARNS: every accept /
+reject re-trains it sharper.
+
+Scoring (suggestion path, API worker, NO numpy): p = sigmoid(weights · x̂ + bias)
+where x̂ is the L2-normalized image embedding. Surface as a suggestion when
+p >= suggest_threshold; auto-apply only once auto_apply_threshold is set (the
+head "graduated" — a precision-targeted operating point was achievable). The
+thresholds come from CROSS-VALIDATED out-of-fold scores so they're honest, not
+in-sample-optimistic; the deployable weights are fit on all data.
+"""
+
+from datetime import datetime
+from typing import Any
+
+from pgvector.sqlalchemy import Vector
+from sqlalchemy import (
+    DateTime,
+    Float,
+    ForeignKey,
+    Integer,
+    String,
+    func,
+)
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+# Matches image_record.siglip_embedding's dimensionality — the head operates in
+# the same space. A model-version change re-embeds AND retrains (embedding_version
+# guards staleness).
+HEAD_DIM = 1152
+
+
+class TagHead(Base):
+    __tablename__ = "tag_head"
+
+    # One head per concept tag; cascade so deleting a tag retires its head.
+    tag_id: Mapped[int] = mapped_column(
+        ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True
+    )
+    # The embedding the head was trained against (image_record's
+    # embedder_model_version). A mismatch with the current embedder means the
+    # head is stale and must be retrained, not scored.
+    embedding_version: Mapped[str] = mapped_column(String(128), nullable=False)
+    # Logistic-regression coefficients over the L2-normalized embedding, stored
+    # as a pgvector for compactness + a future in-DB dot-product path. NOT a
+    # similarity target, just a serialized weight vector.
+    weights: Mapped[list[float]] = mapped_column(Vector(HEAD_DIM), nullable=False)
+    bias: Mapped[float] = mapped_column(Float, nullable=False)
+    # Probability cutoff for SURFACING as a suggestion (F1-best on CV scores).
+    suggest_threshold: Mapped[float] = mapped_column(Float, nullable=False)
+    # Probability cutoff for EARNED auto-apply: the operating point that holds
+    # precision >= the configured target while maximizing recall. NULL = the head
+    # hasn't graduated (can't auto-apply without a human yet).
+    auto_apply_threshold: Mapped[float | None] = mapped_column(Float, nullable=True)
+    # Training-set sizes + cross-validated quality, surfaced in the admin card so
+    # the operator can see which concepts are strong / need more tags.
+    n_pos: Mapped[int] = mapped_column(Integer, nullable=False)
+    n_neg: Mapped[int] = mapped_column(Integer, nullable=False)
+    ap: Mapped[float] = mapped_column(Float, nullable=False)
+    # 'precision' is a SQL reserved word → store as precision_cv (the
+    # cross-validated precision at the suggest operating point).
+    precision_cv: Mapped[float] = mapped_column(Float, nullable=False)
+    recall: Mapped[float] = mapped_column(Float, nullable=False)
+    trained_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    # Extra detail (auto-apply operating point, F1, etc.) — non-load-bearing.
+    metrics: Mapped[dict[str, Any] | None] = mapped_column(JSONB, nullable=True)

backend/app/models/tag_positive_confirmation.py

@@ -0,0 +1,28 @@
+"""TagPositiveConfirmation — operator affirmed an applied tag is correct.
+
+The mirror of TagSuggestionRejection (#1130). When the operator "keeps" a
+positive the head doubts (low-scoring), record it so the eval's doubts list
+stops resurfacing the same confirmed-correct images every run. Does not change
+training (it's already a positive) — purely a "I've reviewed this" marker.
+"""
+
+from datetime import datetime
+
+from sqlalchemy import DateTime, ForeignKey, func
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class TagPositiveConfirmation(Base):
+    __tablename__ = "tag_positive_confirmation"
+
+    image_record_id: Mapped[int] = mapped_column(
+        ForeignKey("image_record.id", ondelete="CASCADE"), primary_key=True
+    )
+    tag_id: Mapped[int] = mapped_column(
+        ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True, index=True
+    )
+    confirmed_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

backend/app/services/archive_extractor.py

@@ -16,9 +16,45 @@ log = logging.getLogger(__name__)
 
 ARCHIVE_EXTS = {".zip", ".cbz", ".rar", ".7z"}
 
+# Magic-byte signatures, so an archive with a mangled / extension-less filename
+# is still recognised. Patreon attachment download URLs sanitize to names like
+# `01_https___www.patreon.com_media-u_v3_131083093`, whose `Path.suffix` is junk
+# (`.com_media-u_v3_131083093`), never `.zip` — an extension-only gate filed
+# those as opaque PostAttachments and NEVER extracted them (operator-flagged
+# 2026-06-06). Detection is by extension first (cheap), then header sniff.
+_RAR_MAGIC = b"Rar!\x1a\x07"
+_7Z_MAGIC = b"7z\xbc\xaf\x27\x1c"
+
+
+def detect_archive_format(path: Path) -> str | None:
+    """Return "zip" | "rar" | "7z" for an archive, else None.
+
+    Trusts a known extension first, then falls back to magic-byte sniffing so a
+    mis-named or extension-less archive is still handled. (zip covers .cbz too.)
+    """
+    ext = Path(path).suffix.lower()
+    if ext in (".zip", ".cbz"):
+        return "zip"
+    if ext == ".rar":
+        return "rar"
+    if ext == ".7z":
+        return "7z"
+    try:
+        if zipfile.is_zipfile(path):
+            return "zip"
+        with open(path, "rb") as fh:
+            head = fh.read(8)
+    except OSError:
+        return None
+    if head.startswith(_RAR_MAGIC):
+        return "rar"
+    if head.startswith(_7Z_MAGIC):
+        return "7z"
+    return None
+
 
 def is_archive(path: Path) -> bool:
-    return Path(path).suffix.lower() in ARCHIVE_EXTS
+    return detect_archive_format(path) is not None
 
 
 @contextmanager
@@ -32,16 +68,16 @@ def extract_archive(path: Path):
     members: list[tuple[str, Path]] = []
     try:
         try:
-            ext = Path(path).suffix.lower()
-            if ext in (".zip", ".cbz"):
+            fmt = detect_archive_format(path)
+            if fmt == "zip":
                 with zipfile.ZipFile(path) as zf:
                     zf.extractall(base)
-            elif ext == ".rar":
+            elif fmt == "rar":
                 import rarfile
 
                 with rarfile.RarFile(path) as rf:
                     rf.extractall(base)
-            elif ext == ".7z":
+            elif fmt == "7z":
                 import py7zr
 
                 with py7zr.SevenZipFile(path, "r") as zf:

backend/app/services/artist_directory_service.py

@@ -13,10 +13,10 @@ from __future__ import annotations
 import base64
 from dataclasses import dataclass
 
-from sqlalchemy import and_, exists, func, or_, select
+from sqlalchemy import and_, case, exists, func, or_, select
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from ..models import Artist, ImageRecord, Source
+from ..models import Artist, ArtistVisit, ImageRecord, Source
 from .gallery_service import thumbnail_url
 
 _SEP = "|"
@@ -58,9 +58,27 @@ class ArtistDirectoryService:
             raise ValueError("limit must be between 1 and 200")
 
         count_col = func.count(ImageRecord.id).label("image_count")
+        # Unseen = images imported since the artist's last_viewed_at.
+        # NULL last_viewed_at (artist created before alembic 0034 seed
+        # or before find_or_create autoseed) defensively counts as
+        # "never visited" → all images unseen. Single grouped query, no
+        # N+1.
+        unseen_col = func.count(
+            case(
+                (
+                    or_(
+                        ArtistVisit.last_viewed_at.is_(None),
+                        ImageRecord.created_at > ArtistVisit.last_viewed_at,
+                    ),
+                    ImageRecord.id,
+                ),
+                else_=None,
+            )
+        ).label("unseen_count")
         stmt = (
-            select(Artist, count_col)
+            select(Artist, count_col, unseen_col)
             .outerjoin(ImageRecord, ImageRecord.artist_id == Artist.id)
+            .outerjoin(ArtistVisit, ArtistVisit.artist_id == Artist.id)
             .group_by(Artist.id)
         )
         if q:
@@ -94,7 +112,7 @@ class ArtistDirectoryService:
             next_cursor = _encode(last_artist.name, last_artist.id)
             rows = rows[:limit]
 
-        artist_ids = [a.id for a, _ in rows]
+        artist_ids = [a.id for a, _, _ in rows]
         previews = await self._previews(artist_ids)
 
         cards = [
@@ -104,9 +122,10 @@ class ArtistDirectoryService:
                 "slug": artist.slug,
                 "is_subscription": bool(artist.is_subscription),
                 "image_count": int(image_count),
+                "unseen_count": int(unseen_count),
                 "preview_thumbnails": previews.get(artist.id, []),
             }
-            for artist, image_count in rows
+            for artist, image_count, unseen_count in rows
         ]
         return DirectoryPage(cards=cards, next_cursor=next_cursor)