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/apify_client_wrapper.py

@@ -0,0 +1,82 @@
+"""Shared Apify client utilities for last30days sources.
+
+Provides a common wrapper around the apify-client SDK so that
+TikTok, Facebook, Instagram (future) all share the same client
+initialization, error handling, and cost-control patterns.
+
+One APIFY_API_TOKEN covers all Apify-backed sources.
+"""
+
+import sys
+from typing import Any, Dict, List, Optional
+
+try:
+    from apify_client import ApifyClient
+except ImportError:
+    ApifyClient = None
+
+
+def is_apify_available() -> bool:
+    """Check if the apify-client library is installed."""
+    return ApifyClient is not None
+
+
+def get_apify_client(token: str) -> "ApifyClient":
+    """Initialize Apify client with token.
+
+    Args:
+        token: Apify API token (from https://console.apify.com)
+
+    Returns:
+        Initialized ApifyClient instance
+
+    Raises:
+        ImportError: If apify-client is not installed
+    """
+    if ApifyClient is None:
+        raise ImportError(
+            "apify-client is not installed. Run: pip install apify-client"
+        )
+    return ApifyClient(token=token)
+
+
+def run_actor_sync(
+    client: "ApifyClient",
+    actor_id: str,
+    run_input: Dict[str, Any],
+    timeout_secs: int = 300,
+    max_items: Optional[int] = None,
+) -> List[Dict[str, Any]]:
+    """Run an Apify actor synchronously and return dataset items.
+
+    Args:
+        client: Initialized ApifyClient
+        actor_id: Actor identifier, e.g. "clockworks/tiktok-scraper"
+        run_input: Actor-specific input dict
+        timeout_secs: Max wait time (default 5 min)
+        max_items: Cap on returned items (cost control)
+
+    Returns:
+        List of result dicts from the actor's default dataset
+    """
+    _log(f"Running actor {actor_id} (timeout={timeout_secs}s)")
+
+    run = client.actor(actor_id).call(
+        run_input=run_input,
+        timeout_secs=timeout_secs,
+    )
+
+    dataset_id = run["defaultDatasetId"]
+    items = list(client.dataset(dataset_id).iterate_items())
+
+    if max_items and len(items) > max_items:
+        items = items[:max_items]
+
+    _log(f"Actor {actor_id} returned {len(items)} items")
+    return items
+
+
+def _log(msg: str):
+    """Log to stderr."""
+    sys.stderr.write(f"[Apify] {msg}\n")
+    sys.stderr.flush()