feat(tiktok): add TikTok as 7th signal source via Apify

Add TikTok search, scoring, and rendering using the Apify platform
(clockworks/tiktok-scraper actor). Users bring their own APIFY_API_TOKEN
($5/month free credits, no CC required). The shared apify_client_wrapper
module is designed for reuse by future Facebook/Instagram sources.

- New modules: tiktok.py (search + caption extraction), apify_client_wrapper.py
- Schema: TikTokItem dataclass, shares field on Engagement, Report.tiktok
- Pipeline: normalize → filter → score → sort → dedupe → cross-link → render
- Scoring: 0.50*log1p(views) + 0.30*log1p(likes) + 0.20*log1p(comments)
- SKILL.md bumped to v2.7 with TikTok stats, citations, and security docs
- 26 unit tests covering relevance, normalize, score, dedupe, render, round-trip

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

scripts/lib/normalize.py

@@ -4,7 +4,7 @@ from typing import Any, Dict, List, TypeVar, Union
 
 from . import dates, schema
 
-T = TypeVar("T", schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem, schema.HackerNewsItem, schema.PolymarketItem)
+T = TypeVar("T", schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem, schema.TikTokItem, schema.HackerNewsItem, schema.PolymarketItem)
 
 
 def filter_by_date_range(
@@ -200,6 +200,53 @@ def normalize_youtube_items(
     return normalized
 
 
+def normalize_tiktok_items(
+    items: List[Dict[str, Any]],
+    from_date: str,
+    to_date: str,
+) -> List[schema.TikTokItem]:
+    """Normalize raw TikTok items to schema.
+
+    Args:
+        items: Raw TikTok items from Apify
+        from_date: Start of date range
+        to_date: End of date range
+
+    Returns:
+        List of TikTokItem objects
+    """
+    normalized = []
+
+    for i, item in enumerate(items):
+        # Parse engagement
+        eng_raw = item.get("engagement") or {}
+        engagement = schema.Engagement(
+            views=eng_raw.get("views"),
+            likes=eng_raw.get("likes"),
+            num_comments=eng_raw.get("comments"),
+            shares=eng_raw.get("shares"),
+        )
+
+        # TikTok dates are reliable (exact timestamps from Apify)
+        date_str = item.get("date")
+
+        normalized.append(schema.TikTokItem(
+            id=f"TK{i+1}",
+            text=item.get("text", ""),
+            url=item.get("url", ""),
+            author_name=item.get("author_name", ""),
+            date=date_str,
+            date_confidence="high",
+            engagement=engagement,
+            caption_snippet=item.get("caption_snippet", ""),
+            hashtags=item.get("hashtags", []),
+            relevance=item.get("relevance", 0.7),
+            why_relevant=item.get("why_relevant", ""),
+        ))
+
+    return normalized
+
+
 def normalize_hackernews_items(
     items: List[Dict[str, Any]],
     from_date: str,