feat: domestic flights, road traffic, CCTV webcams, AI situation brief

Four new intelligence domains for the dashboard:

1. Domestic flights (OpenSky) — global airborne aircraft count by
   region with commercial/general breakdown. No API key needed.

2. Road traffic (TomTom) — real-time congestion % for 20 major world
   cities + traffic incidents in 5 strategic regions. Needs
   TOMTOM_API_KEY (free 2500 req/day at developer.tomtom.com).

3. CCTV webcams (Windy) — public traffic camera locations worldwide.
   Needs WINDY_API_KEY (free 100 req/day at api.windy.com).

4. AI situation brief (Ollama) — LLM-generated 3-paragraph
   intelligence brief synthesizing all dashboard data. Uses local
   Ollama (llama3.2). Falls back to structured metrics summary.

New files: sources/traffic.py, sources/webcams.py, analysis/situation.py
Modified: sources/aviation.py (+fetch_domestic_flights), dashboard/app.py,
dashboard/index.html (drawer sections + HUD pills for all 4 domains).

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

src/world_intel_mcp/sources/aviation.py

@@ -1,11 +1,14 @@
-"""FAA airport delay data source for world-intel-mcp.
+"""Aviation data sources for world-intel-mcp.
 
 Provides real-time US airport delay information from the FAA Airport
-Status Web Service (ASWS) API.  No API key required.
+Status Web Service (ASWS) API, and global domestic air traffic counts
+from OpenSky Network.  No API key required for either.
 """
 
 import asyncio
+import base64
 import logging
+import os
 from datetime import datetime, timezone
 
 from ..fetcher import Fetcher
@@ -131,3 +134,119 @@ async def fetch_airport_delays(fetcher: Fetcher) -> dict:
         "source": "faa",
         "timestamp": now_iso,
     }
+
+
+# ---------------------------------------------------------------------------
+# Domestic / commercial air traffic (OpenSky Network)
+# ---------------------------------------------------------------------------
+
+_OPENSKY_STATES_URL = "https://opensky-network.org/api/states/all"
+
+_AIR_REGIONS = {
+    "north_america": (15, -170, 72, -50),
+    "europe": (35, -25, 72, 45),
+    "east_asia": (15, 95, 55, 155),
+    "middle_east": (12, 25, 42, 65),
+    "south_asia": (5, 60, 40, 100),
+    "africa": (-35, -20, 37, 55),
+    "south_america": (-56, -82, 15, -34),
+    "oceania": (-50, 110, 0, 180),
+}
+
+_COMMERCIAL_PREFIXES = [
+    "UAL", "AAL", "DAL", "SWA", "JBU", "ASA", "NKS", "FFT", "SKW",
+    "BAW", "EZY", "RYR", "DLH", "AFR", "KLM", "SAS", "AUA", "TAP",
+    "QFA", "ANZ", "JST", "VOZ", "CPA", "SIA", "THA", "ANA", "JAL",
+    "CES", "CSN", "CCA", "HDA", "AIC", "UAE", "ETH", "SAA", "RAM",
+    "TAM", "GLO", "AZU", "AVA", "LAN", "THY", "TRK", "SHT",
+]
+
+
+def _opensky_auth_headers() -> dict[str, str] | None:
+    username = os.environ.get("OPENSKY_USERNAME")
+    password = os.environ.get("OPENSKY_PASSWORD")
+    if username and password:
+        cred = base64.b64encode(f"{username}:{password}".encode()).decode()
+        return {"Authorization": f"Basic {cred}"}
+    return None
+
+
+def _classify_region(lat: float | None, lon: float | None) -> str:
+    if lat is None or lon is None:
+        return "unknown"
+    for name, (lat_min, lon_min, lat_max, lon_max) in _AIR_REGIONS.items():
+        if lat_min <= lat <= lat_max and lon_min <= lon <= lon_max:
+            return name
+    return "other"
+
+
+def _is_commercial(callsign: str | None) -> bool:
+    if not callsign:
+        return False
+    cs = callsign.strip().upper()
+    return any(cs.startswith(p) for p in _COMMERCIAL_PREFIXES)
+
+
+async def fetch_domestic_flights(fetcher: Fetcher) -> dict:
+    """Fetch global air traffic counts from OpenSky Network.
+
+    Queries all airborne aircraft once, then buckets by region and type.
+    """
+    data = await fetcher.get_json(
+        _OPENSKY_STATES_URL,
+        source="opensky-domestic",
+        cache_key="aviation:opensky:all",
+        cache_ttl=120,
+        headers=_opensky_auth_headers(),
+    )
+
+    if data is None or not isinstance(data, dict):
+        return {
+            "total_aircraft": 0,
+            "by_region": {},
+            "busiest_origins": [],
+            "error": "OpenSky API unavailable",
+            "source": "opensky-domestic",
+            "timestamp": _utc_now_iso(),
+        }
+
+    states = data.get("states") or []
+
+    by_region: dict[str, dict] = {r: {"count": 0, "commercial": 0, "general": 0} for r in _AIR_REGIONS}
+    by_region["other"] = {"count": 0, "commercial": 0, "general": 0}
+    by_region["unknown"] = {"count": 0, "commercial": 0, "general": 0}
+    country_counts: dict[str, int] = {}
+    total = 0
+
+    for s in states:
+        if not isinstance(s, list) or len(s) < 15:
+            continue
+        if s[8]:  # on_ground
+            continue
+
+        total += 1
+        lat, lon = s[6], s[5]
+        callsign = s[1]
+        origin = s[2] or "Unknown"
+
+        region = _classify_region(lat, lon)
+        by_region[region]["count"] += 1
+        if _is_commercial(callsign):
+            by_region[region]["commercial"] += 1
+        else:
+            by_region[region]["general"] += 1
+
+        country_counts[origin] = country_counts.get(origin, 0) + 1
+
+    # Remove empty regions
+    by_region = {k: v for k, v in by_region.items() if v["count"] > 0}
+
+    busiest = sorted(country_counts.items(), key=lambda x: -x[1])[:15]
+
+    return {
+        "total_aircraft": total,
+        "by_region": by_region,
+        "busiest_origins": [{"country": c, "count": n} for c, n in busiest],
+        "source": "opensky-domestic",
+        "timestamp": _utc_now_iso(),
+    }