Cookbook UI: Ollama browser, advanced serve fold, API tokens form, diagnosis toolbar, polish

Surface a lot of accumulated cookbook + UI work as a single non-agent
commit so the agent rework lands cleanly.

Highlights:
- Ollama as a first-class backend in the Cookbook:
  * Download input accepts ollama-style names (name:tag) → backend=ollama
  * /api/cookbook/ollama/library (cached scrape of ollama.com + curated
    fallback so classic models like qwen2.5 stay reachable)
  * "Browse Ollama library" toggle below Download with size chips
  * Engine=Ollama in hwfit toolbar merges the Ollama library into the
    main scan list as per-tag rows with the same Fit/Param/Quant/VRAM
    columns; click → fills Download input
- API Tokens form added to Integrations panel (matching wired
  loadTokens()/initTokenForm() that had no HTML)
- Serve panel polish: Advanced fold tightening (-8px nudges on vLLM
  checks, Extra args, Spec row), n_cpu_moe + Split Mode controls
  pulled up 8px to align with the row's checkboxes, GGUF File dropdown
  exposed for Ollama backend, GPU re-render on Edit serve restore,
  _forceBackend flag so saved serveState wins over backend detection,
  cookbook:servers-changed CustomEvent so panels don't need refresh
- Models page redesign: Add Models row (URL + hidden API key reveal +
  Type select + Scan/Ollama/Key/Test/Add icon buttons), Probe All +
  Clear-offline buttons in Added Models toolbar, offline-pill removed
  (opacity already conveys state), Engine dropdown gains Ollama option
- _ping_endpoint probes /v1/models then base, accepts 4xx as
  reachable (vLLM returns 404 on bare /v1, fully working endpoints
  were showing offline)
- Diagnosis card: × dismiss + Copy bundle buttons restored on the
  serve error feedback card
- Orphan tmux sweep re-enabled behind a 60s rate-limit + background
  Thread (off the main event loop) so dead serves get discovered
- cookbook_routes auto-register watchdog: drops the endpoint if the
  serve session exits non-zero within the first ~3min
- ollama-rocm sidecar awareness in download wrapper (`docker exec
  ollama-rocm ollama pull` when host ollama isn't installed)
- Skill extractor sets initial_status="published" when
  auto_approve_skills pref is on (audit demotes later)
- Skill list / model list / cookbook scan misc polish

services/hwfit/data/hf_models.json

@@ -14036,6 +14036,29 @@
    "vision"
   ]
  },
+ {
+  "name": "google/gemma-4-12B",
+  "provider": "Google",
+  "parameter_count": "12.0B",
+  "parameters_raw": 12000000000,
+  "min_ram_gb": 24.0,
+  "recommended_ram_gb": 32.0,
+  "min_vram_gb": 24.0,
+  "quantization": "BF16",
+  "context_length": 131072,
+  "use_case": "General purpose, multimodal",
+  "is_moe": false,
+  "num_experts": null,
+  "active_experts": null,
+  "active_parameters": null,
+  "architecture": "gemma4",
+  "pipeline_tag": "image-text-to-text",
+  "release_date": "2026-04-01",
+  "gguf_sources": [],
+  "capabilities": [
+   "vision"
+  ]
+ },
  {
   "name": "google/gemma-4-31B-it",
   "provider": "Google",
@@ -19121,4 +19144,4 @@
   ],
   "_discovered": true
  }
-]
+]

services/memory/skill_extractor.py

@@ -243,6 +243,20 @@ async def maybe_extract_skill(
             logger.debug("[skill-extract] '%s' already exists — dropped as duplicate", title)
             return None
 
+        # Auto-publish gate: if the user has `auto_approve_skills` on, the
+        # newly-extracted skill is created `published` immediately rather
+        # than waiting for the next audit batch. The audit still runs later
+        # and can demote it back to `draft` (or delete) on failure. Default
+        # ON matches the UI label "Auto-approve skills".
+        _initial_status = "draft"
+        try:
+            from routes.prefs_routes import _load_for_user as _load_prefs
+            _prefs = _load_prefs(owner) or {}
+            if _prefs.get("auto_approve_skills", True):
+                _initial_status = "published"
+        except Exception:
+            pass
+
         entry = skills_manager.add_skill(
             title=title,
             problem=data.get("problem", ""),
@@ -253,6 +267,7 @@ async def maybe_extract_skill(
             confidence=data.get("confidence", 0.7),
             session_id=getattr(session, "session_id", None),
             owner=owner,
+            status=_initial_status,
         )
         try:
             from src.event_bus import fire_event