cli+dashboard: --panel flag, panel dict keys, TF-dialog panel section

main.py: --panel runs the agent panel on demand (force bypasses
enabled/surface/quality toggles; daily budget + LLM cap still apply),
prints the verdict + persona takes, and report_to_dict carries the four
panel keys — which auto-propagates them into the dashboard's
snapshot.json via report_to_webapp_dict. dashboard_publisher attaches
in scan_tf gated by agent_panel.dashboard (default off); dashboard.html
renders an "AI panel (advisory)" section in the TF detail dialog.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SisdfkxnXNL9iZsXxN3T15

dashboard_publisher.py

@@ -49,6 +49,7 @@ from datetime import datetime, timezone
 from logging.handlers import TimedRotatingFileHandler
 from typing import Dict, List, Optional
 
+from agent_panel import attach_panel
 from config import PlatformConfig, Timeframe, XAUUSD
 from main import report_to_dict
 from news_engine import NewsEngine
@@ -357,6 +358,13 @@ class DashboardPublisher:
         try:
             report = produce_signal(XAUUSD, tf, self._provider(),
                                     CONFIRMATIONS[tf])
+            # Advisory AI panel, post-hoc and display-only. Off unless
+            # config.json agent_panel.dashboard is true. The reuse-TTL
+            # cache + max_panels_per_day budget bound the spend against
+            # this process's own DeepSeek cap (a persisting M5 signal
+            # re-serves its cached verdict instead of re-running every
+            # bar); a fresh panel adds ≤~25 s before this TF publishes.
+            attach_panel(report, surface="dashboard")
             self.snapshots[tf.value] = {
                 "status": "ok",
                 "scanned_at": datetime.now(timezone.utc).isoformat(),