feat: rewrite briefing pipeline to conversational prose

Replace two-pass structured LLM synthesis (## Your Day / ## The World
sections with bullets and formatted news cards) with a single
conversational pass. The new prompt instructs the model to write 3-5
flowing sentences covering weather, today's tasks/events, and 1-2 news
highlights — no markdown, no headers, no lists. Full news detail stays
in the right panel; weather detail stays in the weather card.

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

src/fabledassistant/services/briefing_pipeline.py

@@ -7,7 +7,7 @@ Slot names: 'compilation' (4am), 'morning' (8am), 'midday' (12pm), 'afternoon' (
 import asyncio
 import hashlib
 import logging
-from datetime import date, datetime, timezone
+from datetime import datetime, timezone
 from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
 
 import httpx
@@ -22,14 +22,6 @@ logger = logging.getLogger(__name__)
 SLOT_NAMES = ("compilation", "morning", "midday", "afternoon")
 
 
-def slot_greeting(slot: str) -> str:
-    return {
-        "compilation": "Good morning",
-        "morning": "Good morning — you're at the office",
-        "midday": "Midday check-in",
-        "afternoon": "End of day wrap-up",
-    }.get(slot, "Update")
-
 
 def format_task(task: dict) -> str:
     parts = [task.get("title", "Untitled")]
@@ -263,57 +255,69 @@ async def _llm_synthesise(system_prompt: str, user_prompt: str, model: str) -> s
         return ""
 
 
-def _internal_system_prompt(profile_body: str) -> str:
+def _unified_system_prompt(profile_body: str) -> str:
     return (
-        "You are a personal briefing assistant. Your job is to give the user a clear, "
+        "You are a personal assistant delivering a daily briefing. "
-        "concise summary of their internal workload: tasks, calendar, and projects. "
+        "Speak naturally and conversationally — as if talking to the user, not writing a report. "
-        "Be direct and prioritised — lead with what's urgent. Use plain text with light "
+        "Use no markdown: no headers, no bullet points, no bold, no lists. Write in flowing prose. "
-        "markdown. Do not include weather or news.\n\n"
+        "Weave together what matters today: mention the weather in a sentence, note any calendar "
+        "events or tasks due today, and briefly reference one or two noteworthy news stories. "
+        "Only mention projects if a task from one is specifically due today. "
+        "Be warm, concise, and human — aim for 3 to 5 sentences. "
+        "Future context like emails and messages will be added over time — keep the tone open and helpful.\n\n"
         + (f"User profile:\n{profile_body}\n" if profile_body else "")
     )
 
 
-def _external_system_prompt() -> str:
+def _unified_user_prompt(internal_data: dict, external_data: dict, slot: str, temp_unit: str = "C") -> str:
-    return (
+    lines = [f"Date: {internal_data['date']}", f"Slot: {slot}", ""]
-        "You are a briefing assistant for external information. Your job is to present "
-        "selected news items and summarise any remaining RSS content. "
-        "IMPORTANT: Weather is handled separately — do NOT include any weather section.\n\n"
-        "Format each news item EXACTLY as:\n"
-        "**[Headline text](source_url)**\n"
-        "*Outlet Name · Day Month*\n"
-        "One or two sentence summary.\n\n"
-        "Present news items in the EXACT ORDER they are provided. Do not reorder them. "
-        "After the news cards, add a brief paragraph for any remaining context."
-    )
 
+    # Weather (brief — card handles detail)
+    weather = external_data.get("weather") or []
+    if weather:
+        loc = weather[0]
+        days = loc.get("days") or []
+        if days:
+            d = days[0]
+            t_min = _format_temp(d["temp_min"], temp_unit)
+            t_max = _format_temp(d["temp_max"], temp_unit)
+            unit_sym = f"°{temp_unit}"
+            lines.append(
+                f"WEATHER: {loc['location_label']} — {d['description']}, "
+                f"{t_min}–{t_max}{unit_sym}"
+            )
+            lines.append("")
 
-def _internal_user_prompt(data: dict, slot: str) -> str:
+    # Today's calendar events
-    lines = [f"Briefing slot: {slot}", f"Date: {data['date']}", ""]
+    if internal_data.get("calendar_events"):
-    if data.get("unchanged_task_count", 0) > 0:
+        lines.append("TODAY'S EVENTS:")
-        lines.append(
+        lines.extend(f"  - {e}" for e in internal_data["calendar_events"])
-            f"({data['unchanged_task_count']} tasks are unchanged since the last briefing "
-            "— acknowledge briefly, do not list them.)"
-        )
         lines.append("")
-    changed = data.get("changed_tasks") or data.get("overdue_tasks", [])
+
-    if changed:
+    # Tasks due today
-        lines.append(f"CHANGED/NEW TASKS ({len(changed)}):")
+    if internal_data.get("due_today"):
-        lines.extend(f"  - {t}" for t in changed)
+        lines.append("DUE TODAY:")
+        lines.extend(f"  - {t}" for t in internal_data["due_today"])
         lines.append("")
-    if data.get("due_today"):
+
-        lines.append(f"DUE TODAY ({len(data['due_today'])}):")
+    # Overdue tasks (brief mention only)
-        lines.extend(f"  - {t}" for t in data["due_today"])
+    if internal_data.get("overdue_tasks"):
+        overdue = internal_data["overdue_tasks"]
+        lines.append(f"OVERDUE ({len(overdue)} task{'s' if len(overdue) != 1 else ''}):")
+        lines.extend(f"  - {t}" for t in overdue[:3])
+        if len(overdue) > 3:
+            lines.append(f"  (and {len(overdue) - 3} more)")
         lines.append("")
-    if data.get("high_priority"):
+
-        lines.append("HIGH PRIORITY (in progress):")
+    # News highlights (top 3 — right panel shows full list)
-        lines.extend(f"  - {t}" for t in data["high_priority"])
+    rss = external_data.get("rss_items") or []
+    if rss:
+        lines.append("NEWS HIGHLIGHTS (mention 1-2 briefly, the full list is shown separately):")
+        for item in rss[:3]:
+            source = item.get("feed_title") or item.get("source") or "News"
+            lines.append(f"  [{source}] {item.get('title', '')}")
         lines.append("")
-    if data["calendar_events"]:
+
-        lines.append("CALENDAR TODAY:")
-        lines.extend(f"  - {e}" for e in data["calendar_events"])
-        lines.append("")
-    if data["active_projects"]:
-        lines.append(f"ACTIVE PROJECTS: {', '.join(data['active_projects'])}")
     return "\n".join(lines)
 
 
@@ -324,31 +328,6 @@ def _format_temp(value: float, unit: str) -> str:
     return f"{value:.0f}"
 
 
-def _external_user_prompt(data: dict, slot: str, temp_unit: str = "C") -> str:
-    unit_sym = f"°{temp_unit}"
-    lines = [f"Briefing slot: {slot}", ""]
-    if data["weather"]:
-        lines.append("WEATHER:")
-        for loc in data["weather"]:
-            lines.append(f"  {loc['location_label']}:")
-            for day in loc["days"][:3]:
-                t_min = _format_temp(day["temp_min"], temp_unit)
-                t_max = _format_temp(day["temp_max"], temp_unit)
-                lines.append(
-                    f"    {day['date']}: {day['description']}, "
-                    f"{t_min}–{t_max}{unit_sym}, {day['precip_mm']}mm rain"
-                )
-            if loc["changes_since_last_fetch"]:
-                lines.append("  FORECAST CHANGES:")
-                lines.extend(f"    - {c}" for c in loc["changes_since_last_fetch"])
-        lines.append("")
-    if data["rss_items"]:
-        lines.append(f"RSS DIGEST ({len(data['rss_items'])} items):")
-        for item in data["rss_items"][:15]:
-            lines.append(f"  [{item.get('feed_title', 'Feed')}] {item['title']}")
-            if item.get("content"):
-                lines.append(f"    {item['content'][:200]}")
-    return "\n".join(lines)
 
 
 # ── Main entry point ───────────────────────────────────────────────────────────
@@ -434,7 +413,6 @@ async def run_compilation(
 
     # ── LLM Synthesis ──────────────────────────────────────────────────────────
     # Build filtered internal data with only changed tasks
-    today = internal_data["date"]
     internal_data_filtered = dict(internal_data)
     internal_data_filtered["unchanged_task_count"] = unchanged_count
     internal_data_filtered["changed_tasks"] = [format_task(t) for t in changed_tasks]
@@ -445,17 +423,10 @@ async def run_compilation(
         "weather": [],
     }
 
-    internal_text, external_text = await asyncio.gather(
+    briefing_text = await _llm_synthesise(
-        _llm_synthesise(
+        _unified_system_prompt(profile_body),
-            _internal_system_prompt(profile_body),
+        _unified_user_prompt(internal_data_filtered, external_data_filtered, slot, temp_unit),
-            _internal_user_prompt(internal_data_filtered, slot),
+        model,
-            model,
-        ),
-        _llm_synthesise(
-            _external_system_prompt(),
-            _external_user_prompt(external_data_filtered, slot, temp_unit),
-            model,
-        ),
     )
 
     # ── Post-processing ─────────────────────────────────────────────────────────
@@ -463,18 +434,11 @@ async def run_compilation(
 
     metadata: dict = {"rss_item_ids": rss_item_ids, "rss_items": rss_items_meta, "weather": weather_card}
 
-    if not internal_text and not external_text:
+    if not briefing_text:
         logger.warning("Briefing compilation produced no content for user %d slot %s", user_id, slot)
         return "", metadata
 
-    greeting = slot_greeting(slot)
+    return briefing_text, metadata
-    parts = [f"**{greeting} — {today}**", ""]
-    if internal_text:
-        parts += ["## Your Day", "", internal_text, ""]
-    if external_text:
-        parts += ["## The World", "", external_text]
-
-    return "\n".join(parts).strip(), metadata
 
 
 async def run_slot_injection(user_id: int, slot: str, model: str | None = None) -> str:
@@ -492,13 +456,12 @@ async def run_slot_injection(user_id: int, slot: str, model: str | None = None)
     )
 
     system = (
-        f"You are a briefing assistant providing a {slot} update. Be brief — "
+        f"You are a personal assistant giving a brief {slot} check-in. "
-        "the user has already seen the morning briefing. Focus on what's changed or new."
+        "The user already had their morning briefing — focus only on what's changed or newly relevant. "
+        "Speak naturally in 2-3 sentences, no markdown formatting, no headers or bullet points."
     )
-    user_prompt = (
+    return await _llm_synthesise(
-        f"Slot: {slot}\n\n"
+        system,
-        + _internal_user_prompt(internal_data, slot)
+        _unified_user_prompt(internal_data, external_data, slot, temp_unit),
-        + "\n\n"
+        model,
-        + _external_user_prompt(external_data, slot, temp_unit)
     )
-    return await _llm_synthesise(system, user_prompt, model)