feat: structured user profile with LLM-learned preferences

Replaces the freeform briefing-profile note with a DB-backed user_profiles
table. Users can edit job/industry/expertise/response preferences/interests/
work schedule via a new Settings → Profile tab. The LLM appends nightly
observations; at 14+ entries they are auto-consolidated into a learned_summary.
Profile context is injected into both briefing and chat system prompts.

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

src/fabledassistant/services/llm.py

@@ -512,11 +512,16 @@ async def build_context(
     tool_guidance = "\n".join(tool_lines)
 
     tz_line = f" The user's timezone is {user_timezone}." if user_timezone else ""
+
+    from fabledassistant.services.user_profile import build_profile_context
+    profile_context = await build_profile_context(user_id)
+    profile_section = f"\n\n{profile_context}" if profile_context else ""
+
     system_parts = [
         f"You are a helpful assistant named {assistant_name}, integrated into a note-taking and task-tracking app called Fabled Assistant. "
         "Help users with their notes, tasks, and general questions. "
         "When note context is provided, use it to give relevant answers. "
-        f"Today's date is {today}.{tz_line}\n\n"
+        f"Today's date is {today}.{tz_line}{profile_section}\n\n"
         f"{tool_guidance}"
     ]