feat: deep site analysis engine + fix AI assess for any domain

site_analyzer.py (new):
- Fresh scrape with timing, page size, server, CMS detection
- Lorem ipsum detection (16 phrases incl. user's example)
- Placeholder content detection (hello world, sample page, etc.)
- Analytics: GA4, GTM, Facebook Pixel, Hotjar, Clarity
- Webmaster: Google Search Console, Bing, Yandex verification tags
- sitemap.xml and robots.txt check + Googlebot block detection
- Mobile viewport check, word count, image/script count
- Full contact extraction: emails, phones, WhatsApp, social links
- Kit Digital signal detection

AI worker fix:
- No longer requires pre-enrichment — works on ANY selected domain
- Does fresh site_analyzer scrape then calls Gemini with full context
- Stores site_analysis JSON alongside AI assessment
- Upserts into enriched_domains even if domain was never enriched

Gemini prompt now includes:
- Complete technical snapshot (load time, size, server, SSL)
- Full SEO signals (sitemap, robots, analytics, webmaster verified)
- Content quality (lorem ipsum matches, placeholder matches)
- Kit Digital signals
- All extracted contacts
- 500-word page text sample
- Outputs: summary, site_quality_score/10, content_issues[],
  urgency_signals[], performance_notes, seo_status,
  best_contact_channel+value, all_contacts, ES pitch,
  services_needed, outreach_notes

UI: rich AI modal with summary banner, quality grid, content issues,
    urgency signals, full contact list, technical snapshot

Fixes: correct Replicate token, ai_queue status='running' bug

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/enricher.py

@@ -13,7 +13,7 @@ import dns.resolver
 import aiosqlite
 from bs4 import BeautifulSoup
 
-from app.db import SQLITE_PATH, queue_ai, save_ai_assessment, get_ai_queue_status
+from app.db import SQLITE_PATH, queue_ai, save_ai_assessment
 from app.scorer import score
 
 logger = logging.getLogger(__name__)
@@ -340,17 +340,17 @@ async def worker_loop():
 
 async def ai_worker_loop():
     from app.replicate_ai import assess_domain as gemini_assess
+    from app.site_analyzer import analyze_site
 
     while True:
         async with aiosqlite.connect(SQLITE_PATH) as db:
             async with db.execute(
-                "SELECT domain FROM ai_queue WHERE status='pending' LIMIT 20"
+                "SELECT domain FROM ai_queue WHERE status='pending' LIMIT 10"
             ) as cur:
                 rows = await cur.fetchall()
-            # Mark as running
             if rows:
                 await db.executemany(
-                    "UPDATE ai_queue SET status='running', created_at=created_at WHERE domain=?",
+                    "UPDATE ai_queue SET status='running' WHERE domain=?",
                     [(r[0],) for r in rows],
                 )
                 await db.commit()
@@ -361,16 +361,11 @@ async def ai_worker_loop():
 
         async def assess_one(domain: str):
             try:
-                async with aiosqlite.connect(SQLITE_PATH) as db:
-                    db.row_factory = aiosqlite.Row
-                    async with db.execute(
-                        "SELECT * FROM enriched_domains WHERE domain=?", (domain,)
-                    ) as cur:
-                        row = await cur.fetchone()
-                if not row:
-                    return
-                assessment = await gemini_assess(dict(row))
-                await save_ai_assessment(domain, assessment)
+                # Always do a fresh deep scrape — no pre-enrichment required
+                analysis = await analyze_site(domain)
+                assessment = await gemini_assess(analysis)
+                await save_ai_assessment(domain, assessment, site_analysis=analysis)
+                logger.info("AI done: %s → %s", domain, assessment.get("lead_quality"))
             except Exception as e:
                 async with aiosqlite.connect(SQLITE_PATH) as db:
                     await db.execute(
@@ -380,6 +375,7 @@ async def ai_worker_loop():
                     await db.commit()
                 logger.error("AI worker error %s: %s", domain, e)
 
+        # AI_CONCURRENCY concurrent assessments (already enforced by replicate_ai semaphore)
         await asyncio.gather(*[asyncio.create_task(assess_one(r[0])) for r in rows], return_exceptions=True)