added post insight feature

config/migrate_add_post_insights.sql

@@ -0,0 +1,122 @@
+-- Migration: Add LinkedIn post insights tables (daily snapshots)
+-- Description: Stores scraped post stats separately from linkedin_posts
+
+CREATE TABLE IF NOT EXISTS linkedin_post_insights_posts (
+    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
+    user_id UUID NOT NULL REFERENCES auth.users(id) ON DELETE CASCADE,
+    linkedin_account_id UUID REFERENCES linkedin_accounts(id) ON DELETE SET NULL,
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+
+    -- Identity
+    post_urn TEXT NOT NULL,
+    post_url TEXT,
+
+    -- Content
+    post_text TEXT,
+    post_date TIMESTAMP WITH TIME ZONE,
+    author_username TEXT,
+
+    -- Latest known totals (optional convenience)
+    total_reactions INTEGER DEFAULT 0,
+    likes INTEGER DEFAULT 0,
+    comments INTEGER DEFAULT 0,
+    shares INTEGER DEFAULT 0,
+    reactions_breakdown JSONB DEFAULT '{}'::JSONB,
+
+    -- Raw data snapshot
+    raw_data JSONB,
+
+    first_seen_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+
+    UNIQUE(user_id, post_urn)
+);
+
+CREATE TABLE IF NOT EXISTS linkedin_post_insights_daily (
+    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
+    user_id UUID NOT NULL REFERENCES auth.users(id) ON DELETE CASCADE,
+    post_id UUID NOT NULL REFERENCES linkedin_post_insights_posts(id) ON DELETE CASCADE,
+    snapshot_date DATE NOT NULL,
+
+    total_reactions INTEGER DEFAULT 0,
+    likes INTEGER DEFAULT 0,
+    comments INTEGER DEFAULT 0,
+    shares INTEGER DEFAULT 0,
+    reactions_breakdown JSONB DEFAULT '{}'::JSONB,
+
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+
+    UNIQUE(post_id, snapshot_date)
+);
+
+CREATE INDEX IF NOT EXISTS idx_post_insights_posts_user_id ON linkedin_post_insights_posts(user_id);
+CREATE INDEX IF NOT EXISTS idx_post_insights_posts_post_date ON linkedin_post_insights_posts(post_date);
+CREATE INDEX IF NOT EXISTS idx_post_insights_daily_user_id ON linkedin_post_insights_daily(user_id);
+CREATE INDEX IF NOT EXISTS idx_post_insights_daily_snapshot_date ON linkedin_post_insights_daily(snapshot_date);
+
+-- Triggers for updated_at
+CREATE OR REPLACE FUNCTION update_linkedin_post_insights_posts_updated_at()
+RETURNS TRIGGER AS $$
+BEGIN
+    NEW.updated_at = NOW();
+    RETURN NEW;
+END;
+$$ LANGUAGE plpgsql;
+
+DROP TRIGGER IF EXISTS update_linkedin_post_insights_posts_updated_at ON linkedin_post_insights_posts;
+CREATE TRIGGER update_linkedin_post_insights_posts_updated_at
+    BEFORE UPDATE ON linkedin_post_insights_posts
+    FOR EACH ROW
+    EXECUTE FUNCTION update_linkedin_post_insights_posts_updated_at();
+
+CREATE OR REPLACE FUNCTION update_linkedin_post_insights_daily_updated_at()
+RETURNS TRIGGER AS $$
+BEGIN
+    NEW.updated_at = NOW();
+    RETURN NEW;
+END;
+$$ LANGUAGE plpgsql;
+
+DROP TRIGGER IF EXISTS update_linkedin_post_insights_daily_updated_at ON linkedin_post_insights_daily;
+CREATE TRIGGER update_linkedin_post_insights_daily_updated_at
+    BEFORE UPDATE ON linkedin_post_insights_daily
+    FOR EACH ROW
+    EXECUTE FUNCTION update_linkedin_post_insights_daily_updated_at();
+
+-- Enable RLS
+ALTER TABLE linkedin_post_insights_posts ENABLE ROW LEVEL SECURITY;
+ALTER TABLE linkedin_post_insights_daily ENABLE ROW LEVEL SECURITY;
+
+CREATE POLICY "Users can view own post insights posts"
+    ON linkedin_post_insights_posts FOR SELECT
+    USING (auth.uid() = user_id);
+
+CREATE POLICY "Users can insert own post insights posts"
+    ON linkedin_post_insights_posts FOR INSERT
+    WITH CHECK (auth.uid() = user_id);
+
+CREATE POLICY "Users can update own post insights posts"
+    ON linkedin_post_insights_posts FOR UPDATE
+    USING (auth.uid() = user_id);
+
+CREATE POLICY "Users can delete own post insights posts"
+    ON linkedin_post_insights_posts FOR DELETE
+    USING (auth.uid() = user_id);
+
+CREATE POLICY "Users can view own post insights daily"
+    ON linkedin_post_insights_daily FOR SELECT
+    USING (auth.uid() = user_id);
+
+CREATE POLICY "Users can insert own post insights daily"
+    ON linkedin_post_insights_daily FOR INSERT
+    WITH CHECK (auth.uid() = user_id);
+
+CREATE POLICY "Users can update own post insights daily"
+    ON linkedin_post_insights_daily FOR UPDATE
+    USING (auth.uid() = user_id);
+
+CREATE POLICY "Users can delete own post insights daily"
+    ON linkedin_post_insights_daily FOR DELETE
+    USING (auth.uid() = user_id);
+

src/database/client.py

@@ -194,6 +194,81 @@ class DatabaseClient:
         )
         return [LinkedInPost(**item) for item in result.data]
 
+    # ==================== LINKEDIN POST INSIGHTS ====================
+
+    async def upsert_post_insights_posts(self, posts: List['LinkedInPostInsightPost']) -> List['LinkedInPostInsightPost']:
+        """Upsert LinkedIn post insights posts."""
+        from datetime import datetime
+        from src.database.models import LinkedInPostInsightPost
+
+        data = []
+        for p in posts:
+            post_dict = p.model_dump(exclude={"id", "created_at", "updated_at", "first_seen_at"}, exclude_none=True)
+            post_dict["user_id"] = str(post_dict["user_id"])
+            if post_dict.get("linkedin_account_id"):
+                post_dict["linkedin_account_id"] = str(post_dict["linkedin_account_id"])
+            if "post_date" in post_dict and isinstance(post_dict["post_date"], datetime):
+                post_dict["post_date"] = post_dict["post_date"].isoformat()
+            data.append(post_dict)
+
+        if not data:
+            return []
+
+        result = await asyncio.to_thread(
+            lambda: self.client.table("linkedin_post_insights_posts").upsert(
+                data,
+                on_conflict="user_id,post_urn"
+            ).execute()
+        )
+        return [LinkedInPostInsightPost(**item) for item in result.data]
+
+    async def upsert_post_insights_daily(self, snapshots: List['LinkedInPostInsightDaily']) -> List['LinkedInPostInsightDaily']:
+        """Upsert daily snapshots for post insights."""
+        from src.database.models import LinkedInPostInsightDaily
+
+        data = []
+        for s in snapshots:
+            snap_dict = s.model_dump(exclude={"id", "created_at", "updated_at"}, exclude_none=True)
+            snap_dict["user_id"] = str(snap_dict["user_id"])
+            snap_dict["post_id"] = str(snap_dict["post_id"])
+            if "snapshot_date" in snap_dict and hasattr(snap_dict["snapshot_date"], "isoformat"):
+                snap_dict["snapshot_date"] = snap_dict["snapshot_date"].isoformat()
+            data.append(snap_dict)
+
+        if not data:
+            return []
+
+        result = await asyncio.to_thread(
+            lambda: self.client.table("linkedin_post_insights_daily").upsert(
+                data,
+                on_conflict="post_id,snapshot_date"
+            ).execute()
+        )
+        return [LinkedInPostInsightDaily(**item) for item in result.data]
+
+    async def get_post_insights_posts(self, user_id: UUID) -> List['LinkedInPostInsightPost']:
+        """Get all LinkedIn post insights posts for user."""
+        from src.database.models import LinkedInPostInsightPost
+        result = await asyncio.to_thread(
+            lambda: self.client.table("linkedin_post_insights_posts").select("*").eq(
+                "user_id", str(user_id)
+            ).order("post_date", desc=True).execute()
+        )
+        return [LinkedInPostInsightPost(**item) for item in result.data]
+
+    async def get_post_insights_daily(self, user_id: UUID, since_date: Optional[str] = None) -> List['LinkedInPostInsightDaily']:
+        """Get daily post insights snapshots for user."""
+        from src.database.models import LinkedInPostInsightDaily
+
+        def _query():
+            q = self.client.table("linkedin_post_insights_daily").select("*").eq("user_id", str(user_id))
+            if since_date:
+                q = q.gte("snapshot_date", since_date)
+            return q.order("snapshot_date", desc=False).execute()
+
+        result = await asyncio.to_thread(_query)
+        return [LinkedInPostInsightDaily(**item) for item in result.data]
+
     async def update_post_classification(
         self,
         post_id: UUID,
@@ -768,6 +843,19 @@ class DatabaseClient:
             return LinkedInAccount(**result.data[0])
         return None
 
+    async def list_linkedin_accounts(self, active_only: bool = True) -> list['LinkedInAccount']:
+        """List LinkedIn accounts (optionally only active ones)."""
+        from src.database.models import LinkedInAccount
+
+        def _query():
+            q = self.client.table("linkedin_accounts").select("*")
+            if active_only:
+                q = q.eq("is_active", True)
+            return q.execute()
+
+        result = await asyncio.to_thread(_query)
+        return [LinkedInAccount(**item) for item in result.data]
+
     async def create_linkedin_account(self, account: 'LinkedInAccount') -> 'LinkedInAccount':
         """Create LinkedIn account connection."""
         from src.database.models import LinkedInAccount

src/database/models.py

@@ -289,6 +289,45 @@ class LinkedInPost(DBModel):
     classification_confidence: Optional[float] = None
 
 
+class LinkedInPostInsightPost(DBModel):
+    """LinkedIn post record for insights (separate from linkedin_posts)."""
+    id: Optional[UUID] = None
+    user_id: UUID
+    linkedin_account_id: Optional[UUID] = None
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+    post_urn: str
+    post_url: Optional[str] = None
+    post_text: Optional[str] = None
+    post_date: Optional[datetime] = None
+    author_username: Optional[str] = None
+
+    total_reactions: int = 0
+    likes: int = 0
+    comments: int = 0
+    shares: int = 0
+    reactions_breakdown: Dict[str, Any] = Field(default_factory=dict)
+
+    raw_data: Optional[Dict[str, Any]] = None
+    first_seen_at: Optional[datetime] = None
+
+
+class LinkedInPostInsightDaily(DBModel):
+    """Daily snapshot of LinkedIn post insights."""
+    id: Optional[UUID] = None
+    user_id: UUID
+    post_id: UUID
+    snapshot_date: date
+    total_reactions: int = 0
+    likes: int = 0
+    comments: int = 0
+    shares: int = 0
+    reactions_breakdown: Dict[str, Any] = Field(default_factory=dict)
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+
 class Topic(DBModel):
     """Topic model."""
     id: Optional[UUID] = None

src/services/post_insights_service.py

@@ -0,0 +1,409 @@
+"""Post insights scraping and aggregation service."""
+from __future__ import annotations
+
+from collections import defaultdict
+from datetime import datetime, date, timezone, timedelta
+from typing import Any, Dict, List, Optional, Tuple
+from uuid import UUID
+
+from loguru import logger
+
+from src.database.models import LinkedInPostInsightPost, LinkedInPostInsightDaily
+from src.scraper import scraper
+
+
+def _parse_post_date(raw_post: Dict[str, Any]) -> Optional[datetime]:
+    posted_at = raw_post.get("posted_at")
+    if isinstance(posted_at, dict):
+        date_str = posted_at.get("date")
+        ts = posted_at.get("timestamp")
+        if date_str:
+            for fmt in ("%Y-%m-%d %H:%M:%S", "%Y-%m-%d"):
+                try:
+                    return datetime.strptime(date_str, fmt)
+                except ValueError:
+                    continue
+            try:
+                return datetime.fromisoformat(date_str)
+            except ValueError:
+                return None
+        if ts:
+            try:
+                return datetime.fromtimestamp(int(ts) / 1000, tz=timezone.utc)
+            except Exception:
+                return None
+    return None
+
+
+def _extract_post_urn(raw_post: Dict[str, Any]) -> Optional[str]:
+    if raw_post.get("full_urn"):
+        return raw_post["full_urn"]
+    urn_obj = raw_post.get("urn") or {}
+    for key, prefix in (("activity_urn", "urn:li:activity:"), ("share_urn", "urn:li:share:"), ("ugcPost_urn", "urn:li:ugcPost:")):
+        val = urn_obj.get(key)
+        if val:
+            if str(val).startswith("urn:li:"):
+                return str(val)
+            return f"{prefix}{val}"
+    return None
+
+
+def _extract_reactions(stats: Dict[str, Any]) -> Tuple[int, Dict[str, int]]:
+    if not stats:
+        return 0, {}
+    breakdown = {
+        "like": int(stats.get("like", 0) or 0),
+        "love": int(stats.get("love", 0) or 0),
+        "insight": int(stats.get("insight", 0) or 0),
+        "celebrate": int(stats.get("celebrate", 0) or 0),
+        "support": int(stats.get("support", 0) or 0),
+        "funny": int(stats.get("funny", 0) or 0),
+    }
+    total = stats.get("total_reactions")
+    if total is None:
+        total = sum(breakdown.values())
+    return int(total or 0), breakdown
+
+
+def _extract_username_from_url(linkedin_url: Optional[str]) -> Optional[str]:
+    if not linkedin_url:
+        return None
+    import re
+    url = linkedin_url.rstrip("/")
+    match = re.search(r"/in/([^/?#]+)", url)
+    if match:
+        return match.group(1)
+    return None
+
+
+def parse_apify_posts_for_insights(
+    raw_posts: List[Dict[str, Any]],
+    user_id: str,
+    linkedin_account_id: Optional[str]
+) -> Tuple[List[LinkedInPostInsightPost], List[Dict[str, Any]]]:
+    """Parse Apify posts into insights models plus raw metadata for snapshots."""
+    parsed: List[LinkedInPostInsightPost] = []
+    snapshot_meta: List[Dict[str, Any]] = []
+
+    for post in raw_posts:
+        post_urn = _extract_post_urn(post)
+        if not post_urn:
+            continue
+
+        stats = post.get("stats") or {}
+        total_reactions, breakdown = _extract_reactions(stats)
+
+        parsed_post = LinkedInPostInsightPost(
+            user_id=user_id,
+            linkedin_account_id=linkedin_account_id,
+            post_urn=post_urn,
+            post_url=post.get("url"),
+            post_text=post.get("text"),
+            post_date=_parse_post_date(post),
+            author_username=(post.get("author") or {}).get("username"),
+            total_reactions=total_reactions,
+            likes=int(stats.get("like", 0) or 0),
+            comments=int(stats.get("comments", 0) or 0),
+            shares=int(stats.get("reposts", 0) or 0),
+            reactions_breakdown=breakdown,
+            raw_data=post,
+        )
+        parsed.append(parsed_post)
+        snapshot_meta.append(
+            {
+                "post_urn": post_urn,
+                "total_reactions": total_reactions,
+                "likes": int(stats.get("like", 0) or 0),
+                "comments": int(stats.get("comments", 0) or 0),
+                "shares": int(stats.get("reposts", 0) or 0),
+                "reactions_breakdown": breakdown,
+            }
+        )
+
+    return parsed, snapshot_meta
+
+
+async def refresh_post_insights_for_account(db, account) -> None:
+    """Scrape posts for a connected LinkedIn account and store insights snapshots."""
+    vanity_name = None
+    try:
+        profile = await db.get_profile(account.user_id)
+        vanity_name = _extract_username_from_url(profile.linkedin_url if profile else None)
+    except Exception:
+        vanity_name = None
+    if not vanity_name:
+        logger.warning(f"LinkedIn account {account.id} missing profile link username; skipping insights scrape.")
+        return
+
+    linkedin_url = f"https://www.linkedin.com/in/{vanity_name}/"
+    logger.info(f"Scraping post insights for user {account.user_id} ({vanity_name})")
+
+    raw_posts = await scraper.scrape_posts(linkedin_url, limit=50)
+    if not raw_posts:
+        logger.info(f"No posts found for user {account.user_id}")
+        return
+
+    posts, snapshot_meta = parse_apify_posts_for_insights(
+        raw_posts,
+        user_id=str(account.user_id),
+        linkedin_account_id=str(account.id),
+    )
+
+    if not posts:
+        logger.info(f"No parsable posts for user {account.user_id}")
+        return
+
+    saved_posts = await db.upsert_post_insights_posts(posts)
+    post_id_by_urn = {p.post_urn: p.id for p in saved_posts}
+
+    today = date.today()
+    snapshots: List[LinkedInPostInsightDaily] = []
+    for meta in snapshot_meta:
+        post_id = post_id_by_urn.get(meta["post_urn"])
+        if not post_id:
+            continue
+        snapshots.append(
+            LinkedInPostInsightDaily(
+                user_id=account.user_id,
+                post_id=post_id,
+                snapshot_date=today,
+                total_reactions=meta["total_reactions"],
+                likes=meta["likes"],
+                comments=meta["comments"],
+                shares=meta["shares"],
+                reactions_breakdown=meta["reactions_breakdown"],
+            )
+        )
+
+    if snapshots:
+        await db.upsert_post_insights_daily(snapshots)
+        logger.info(f"Saved {len(snapshots)} post insight snapshots for user {account.user_id}")
+
+
+async def refresh_all_post_insights(db) -> None:
+    """Run daily refresh for all active LinkedIn accounts."""
+    accounts = await db.list_linkedin_accounts(active_only=True)
+    if not accounts:
+        return
+    for account in accounts:
+        try:
+            await refresh_post_insights_for_account(db, account)
+        except Exception as exc:
+            logger.error(f"Post insights refresh failed for {account.id}: {exc}")
+
+
+def _word_count(text: Optional[str]) -> int:
+    if not text:
+        return 0
+    return len([w for w in text.split() if w.strip()])
+
+
+def _to_naive(dt: datetime) -> datetime:
+    if dt.tzinfo is not None:
+        return dt.astimezone(timezone.utc).replace(tzinfo=None)
+    return dt
+
+
+def compute_post_insights(posts: List[LinkedInPostInsightPost], daily: List[LinkedInPostInsightDaily]) -> Dict[str, Any]:
+    """Compute aggregated post insights for UI rendering."""
+    if not posts:
+        return {"has_data": False}
+
+    posts_by_id = {p.id: p for p in posts if p.id}
+
+    latest_by_post: Dict[str, LinkedInPostInsightDaily] = {}
+    daily_by_post: Dict[str, List[LinkedInPostInsightDaily]] = defaultdict(list)
+    for snap in daily:
+        if not snap.post_id:
+            continue
+        daily_by_post[str(snap.post_id)].append(snap)
+        current = latest_by_post.get(str(snap.post_id))
+        if not current or snap.snapshot_date > current.snapshot_date:
+            latest_by_post[str(snap.post_id)] = snap
+
+    # Aggregate totals using latest snapshot per post
+    total_posts = len(posts_by_id)
+    total_reactions = 0
+    total_likes = 0
+    total_comments = 0
+    total_shares = 0
+    reaction_mix = defaultdict(int)
+
+    for post_id, snap in latest_by_post.items():
+        total_reactions += snap.total_reactions or 0
+        total_likes += snap.likes or 0
+        total_comments += snap.comments or 0
+        total_shares += snap.shares or 0
+        for k, v in (snap.reactions_breakdown or {}).items():
+            reaction_mix[k] += int(v or 0)
+
+    avg_reactions = round(total_reactions / total_posts, 2) if total_posts else 0
+    avg_likes = round(total_likes / total_posts, 2) if total_posts else 0
+    avg_comments = round(total_comments / total_posts, 2) if total_posts else 0
+    avg_shares = round(total_shares / total_posts, 2) if total_posts else 0
+
+    # Daily deltas (last 30 days)
+    delta_by_date = defaultdict(lambda: {"reactions": 0, "likes": 0, "comments": 0, "shares": 0})
+    for post_id, snaps in daily_by_post.items():
+        snaps_sorted = sorted(snaps, key=lambda s: s.snapshot_date)
+        prev = None
+        for snap in snaps_sorted:
+            if prev:
+                day = snap.snapshot_date
+                delta_by_date[day]["reactions"] += max(0, (snap.total_reactions or 0) - (prev.total_reactions or 0))
+                delta_by_date[day]["likes"] += max(0, (snap.likes or 0) - (prev.likes or 0))
+                delta_by_date[day]["comments"] += max(0, (snap.comments or 0) - (prev.comments or 0))
+                delta_by_date[day]["shares"] += max(0, (snap.shares or 0) - (prev.shares or 0))
+            prev = snap
+
+    today = date.today()
+    last_7_days = [today - timedelta(days=i) for i in range(6, -1, -1)]
+    series = []
+    for day in last_7_days:
+        delta = delta_by_date.get(day, {"reactions": 0, "likes": 0, "comments": 0, "shares": 0})
+        series.append({"date": day, **delta})
+
+    max_reactions = max([d["reactions"] for d in series], default=0) or 1
+
+    last_7 = sum(d["reactions"] for d in series[-7:])
+    prev_7 = sum(d["reactions"] for d in series[-14:-7]) if len(series) >= 14 else 0
+    trend_pct = None
+    if prev_7:
+        trend_pct = round(((last_7 - prev_7) / prev_7) * 100, 1)
+
+    # Top posts by engagement score
+    top_posts = []
+    for post_id, snap in latest_by_post.items():
+        post = None
+        if post_id:
+            try:
+                post = posts_by_id.get(UUID(post_id))
+            except ValueError:
+                post = None
+        if not post:
+            continue
+        score = (snap.likes or 0) + (snap.comments or 0) * 2 + (snap.shares or 0) * 3
+        top_posts.append(
+            {
+                "post_url": post.post_url,
+                "post_date": post.post_date,
+                "text": post.post_text or "",
+                "likes": snap.likes or 0,
+                "comments": snap.comments or 0,
+                "shares": snap.shares or 0,
+                "total_reactions": snap.total_reactions or 0,
+                "engagement_score": score,
+            }
+        )
+    top_posts = sorted(top_posts, key=lambda x: x["engagement_score"], reverse=True)[:5]
+
+    # Posting cadence and weekday performance
+    weekday_stats = defaultdict(lambda: {"count": 0, "engagement": 0})
+    post_dates = []
+    for post in posts_by_id.values():
+        if not post.post_date:
+            continue
+        post_dates.append(_to_naive(post.post_date))
+        snap = latest_by_post.get(str(post.id))
+        score = 0
+        if snap:
+            score = (snap.likes or 0) + (snap.comments or 0) * 2 + (snap.shares or 0) * 3
+        weekday = post.post_date.strftime("%A")
+        weekday_stats[weekday]["count"] += 1
+        weekday_stats[weekday]["engagement"] += score
+
+    best_weekday = None
+    if weekday_stats:
+        best_weekday = max(
+            weekday_stats.items(),
+            key=lambda kv: (kv[1]["engagement"] / kv[1]["count"]) if kv[1]["count"] else 0
+        )[0]
+    weekday_breakdown = []
+    if weekday_stats:
+        order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
+        for day in order:
+            stats = weekday_stats.get(day, {"count": 0, "engagement": 0})
+            avg = round(stats["engagement"] / stats["count"], 2) if stats["count"] else 0
+            weekday_breakdown.append({"day": day, "count": stats["count"], "avg_engagement": avg})
+
+    cadence_per_week = None
+    if post_dates:
+        earliest = min(post_dates)
+        weeks = max(1, int((datetime.now() - earliest).days / 7))
+        cadence_per_week = round(len(post_dates) / weeks, 2)
+
+    # Length buckets
+    length_buckets = {
+        "0-100": {"count": 0, "avg_engagement": 0},
+        "101-200": {"count": 0, "avg_engagement": 0},
+        "201-400": {"count": 0, "avg_engagement": 0},
+        "400+": {"count": 0, "avg_engagement": 0},
+    }
+    for post in posts_by_id.values():
+        wc = _word_count(post.post_text)
+        snap = latest_by_post.get(str(post.id))
+        score = 0
+        if snap:
+            score = (snap.likes or 0) + (snap.comments or 0) * 2 + (snap.shares or 0) * 3
+        if wc <= 100:
+            bucket = "0-100"
+        elif wc <= 200:
+            bucket = "101-200"
+        elif wc <= 400:
+            bucket = "201-400"
+        else:
+            bucket = "400+"
+        length_buckets[bucket]["count"] += 1
+        length_buckets[bucket]["avg_engagement"] += score
+
+    for bucket in length_buckets.values():
+        if bucket["count"]:
+            bucket["avg_engagement"] = round(bucket["avg_engagement"] / bucket["count"], 2)
+
+    latest_snapshot_date = None
+    if daily:
+        latest_snapshot_date = max(s.snapshot_date for s in daily)
+
+    reaction_mix_total = sum(reaction_mix.values()) or 1
+    reaction_mix_pct = [
+        {
+            "name": k,
+            "count": v,
+            "pct": round((v / reaction_mix_total) * 100, 1),
+        }
+        for k, v in sorted(reaction_mix.items(), key=lambda x: x[1], reverse=True)
+    ]
+
+    series_chart = [
+        {
+            "date": item["date"].isoformat(),
+            "reactions": item["reactions"],
+            "likes": item["likes"],
+            "comments": item["comments"],
+            "shares": item["shares"],
+        }
+        for item in series
+    ]
+
+    return {
+        "has_data": True,
+        "total_posts": total_posts,
+        "total_reactions": total_reactions,
+        "avg_reactions": avg_reactions,
+        "avg_likes": avg_likes,
+        "avg_comments": avg_comments,
+        "avg_shares": avg_shares,
+        "last_7_reactions": last_7,
+        "prev_7_reactions": prev_7,
+        "trend_pct": trend_pct,
+        "best_weekday": best_weekday,
+        "weekday_breakdown": weekday_breakdown,
+        "cadence_per_week": cadence_per_week,
+        "reaction_mix": reaction_mix_pct,
+        "series": series,
+        "series_chart": series_chart,
+        "series_max": max_reactions,
+        "top_posts": top_posts,
+        "length_buckets": length_buckets,
+        "latest_snapshot_date": latest_snapshot_date,
+    }

src/services/scheduler_service.py

@@ -71,6 +71,11 @@ class SchedulerService:
                     await self.db.cleanup_expired_email_tokens()
                 except Exception as e:
                     logger.error(f"Email token cleanup error: {e}")
+                try:
+                    from src.services.post_insights_service import refresh_all_post_insights
+                    await refresh_all_post_insights(self.db)
+                except Exception as e:
+                    logger.error(f"Post insights daily job error: {e}")
 
             _tick += 1
             await asyncio.sleep(self.check_interval)

src/web/templates/user/base.html

@@ -160,6 +160,15 @@
                 <span class="sidebar-text">Unternehmensstrategie</span>
             </a>
             {% endif %}
+            {% if session and session.account_type == 'employee' %}
+            <a href="/employee/insights" class="nav-link flex items-center gap-3 px-4 py-3 rounded-lg text-gray-300 hover:bg-brand-bg-light transition-colors {% if page == 'insights' %}active{% endif %}">
+                <svg class="w-5 h-5 flex-shrink-0" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                    <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 19v-6a2 2 0 00-2-2H5a2 2 0 00-2 2v6a2 2 0 002 2h2a2 2 0 002-2zm0 0V9a2 2 0 012-2h2a2 2 0 012 2v10m-6 0a2 2 0 002 2h2a2 2 0 002-2m0 0V5a2 2 0 012-2h2a2 2 0 012 2v14a2 2 0 01-2 2h-2a2 2 0 01-2-2z"/>
+                </svg>
+                <span class="sidebar-text">Post Insights</span>
+                <span class="ml-auto text-[10px] font-semibold px-2 py-0.5 rounded-full bg-brand-highlight text-brand-bg-dark sidebar-text">Neu</span>
+            </a>
+            {% endif %}
         </nav>
 
         <div class="p-4 border-t border-gray-600 space-y-2">

src/web/templates/user/employee_dashboard.html

@@ -6,6 +6,7 @@
 <div class="max-w-4xl mx-auto">
     <h1 class="text-2xl font-bold text-white mb-6">Willkommen, {{ session.linkedin_name or session.customer_name or 'Mitarbeiter' }}!</h1>
 
+    <div>
     <!-- Company Info Banner -->
     {% if session.company_name %}
     <div class="bg-brand-highlight/10 border border-brand-highlight/30 rounded-xl p-6 mb-8">
@@ -143,5 +144,6 @@
         <a href="/create" class="inline-block mt-4 text-brand-highlight hover:underline">Ersten Post erstellen</a>
     </div>
     {% endif %}
+    </div>
 </div>
 {% endblock %}

src/web/templates/user/employee_insights.html

@@ -0,0 +1,237 @@
+{% extends "base.html" %}
+
+{% block title %}Post Insights{% endblock %}
+
+{% block head %}
+<script src="https://cdn.jsdelivr.net/npm/apexcharts"></script>
+{% endblock %}
+
+{% block content %}
+<div class="max-w-4xl mx-auto">
+    <div class="flex items-center justify-between mb-6">
+        <div>
+            <h1 class="text-2xl font-bold text-white">Post Insights</h1>
+            <p class="text-gray-400 text-sm mt-1">Tägliche Auswertung deiner LinkedIn-Posts</p>
+        </div>
+        <button id="insights-refresh-btn" class="px-4 py-2 rounded-lg text-sm font-medium bg-brand-highlight/20 text-brand-highlight hover:bg-brand-highlight/30 transition-colors">
+            Jetzt aktualisieren
+        </button>
+    </div>
+
+    {% if not linkedin_account %}
+    <div class="card-bg border rounded-xl p-6 text-center">
+        <p class="text-gray-300 mb-2">Verbinde deinen LinkedIn Account, damit wir täglich Post-Insights aktualisieren können.</p>
+        <a href="/settings" class="inline-block mt-2 text-brand-highlight hover:underline">Zum LinkedIn Login</a>
+    </div>
+    {% elif not post_insights or not post_insights.has_data %}
+    <div class="card-bg border rounded-xl p-6 text-center">
+        <p class="text-gray-300">Noch keine Insights vorhanden. Der tägliche Import läuft in den nächsten 24 Stunden.</p>
+    </div>
+    {% else %}
+    <div class="grid md:grid-cols-3 gap-6 mb-8">
+        <div class="card-bg border rounded-xl p-6">
+            <p class="text-gray-400 text-sm">Posts getrackt</p>
+            <p class="text-3xl font-bold text-white">{{ post_insights.total_posts }}</p>
+        </div>
+        <div class="card-bg border rounded-xl p-6">
+            <p class="text-gray-400 text-sm">Ø Reaktionen/Post</p>
+            <p class="text-3xl font-bold text-white">{{ post_insights.avg_reactions }}</p>
+            <p class="text-xs text-gray-500 mt-1">Likes {{ post_insights.avg_likes }} · Comments {{ post_insights.avg_comments }} · Shares {{ post_insights.avg_shares }}</p>
+        </div>
+        <div class="card-bg border rounded-xl p-6">
+            <p class="text-gray-400 text-sm">Letzte 7 Tage</p>
+            <p class="text-3xl font-bold text-white">{{ post_insights.last_7_reactions }}</p>
+            {% if post_insights.trend_pct is not none %}
+            <p class="text-xs mt-1 {% if post_insights.trend_pct >= 0 %}text-green-400{% else %}text-red-400{% endif %}">
+                {% if post_insights.trend_pct >= 0 %}+{% endif %}{{ post_insights.trend_pct }}% vs. Vorwoche
+            </p>
+            {% endif %}
+        </div>
+    </div>
+
+    <div class="grid grid-cols-1 lg:grid-cols-2 gap-6 mb-8">
+        <div class="card-bg border rounded-xl p-6">
+            <h2 class="text-lg font-semibold text-white mb-4">Engagement-Entwicklung (7 Tage)</h2>
+            <div id="chart-engagement"></div>
+        </div>
+        <div class="card-bg border rounded-xl p-6">
+            <h2 class="text-lg font-semibold text-white mb-4">Reaktions-Mix</h2>
+            <div id="chart-reactions"></div>
+        </div>
+    </div>
+
+    <div class="grid md:grid-cols-2 gap-6 mb-8">
+        <div class="card-bg border rounded-xl p-6">
+            <h3 class="text-lg font-semibold text-white mb-4">Wochentag-Performance</h3>
+            <div id="chart-weekday"></div>
+        </div>
+        <div class="card-bg border rounded-xl p-6">
+            <h3 class="text-lg font-semibold text-white mb-4">Performance-Driver</h3>
+            <div class="text-sm text-gray-400 space-y-3">
+                <div class="flex items-center justify-between">
+                    <span>Bester Wochentag</span>
+                    <span class="text-gray-200">{{ post_insights.best_weekday or 'N/A' }}</span>
+                </div>
+                <div class="flex items-center justify-between">
+                    <span>Posting-Kadenz</span>
+                    <span class="text-gray-200">{{ post_insights.cadence_per_week or 'N/A' }} Posts/Woche</span>
+                </div>
+                <div class="flex items-center justify-between">
+                    <span>Letzter Snapshot</span>
+                    <span class="text-gray-200">{{ post_insights.latest_snapshot_date.strftime('%d.%m.%Y') if post_insights.latest_snapshot_date else 'N/A' }}</span>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div class="card-bg border rounded-xl p-6 mb-8">
+        <h2 class="text-lg font-semibold text-white mb-4">Top Posts (Engagement)</h2>
+        <div class="space-y-3">
+            {% for post in post_insights.top_posts %}
+            <div class="p-4 bg-brand-bg-dark rounded-lg">
+                <p class="text-white text-sm line-clamp-2">{{ post.text[:180] }}{% if post.text|length > 180 %}...{% endif %}</p>
+                <div class="text-xs text-gray-500 mt-2 flex items-center gap-3">
+                    <span>{{ post.post_date.strftime('%d.%m.%Y') if post.post_date else 'N/A' }}</span>
+                    <span>Likes {{ post.likes }}</span>
+                    <span>Comments {{ post.comments }}</span>
+                    <span>Shares {{ post.shares }}</span>
+                    <span>Score {{ post.engagement_score }}</span>
+                    {% if post.post_url %}
+                    <a href="{{ post.post_url }}" target="_blank" class="text-brand-highlight hover:underline">Öffnen</a>
+                    {% endif %}
+                </div>
+            </div>
+            {% endfor %}
+        </div>
+    </div>
+
+    <div class="card-bg border rounded-xl p-6">
+        <h2 class="text-lg font-semibold text-white mb-4">Post-Länge vs. Engagement</h2>
+        <div id="chart-length"></div>
+    </div>
+    {% endif %}
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+document.addEventListener('DOMContentLoaded', function() {
+    const refreshBtn = document.getElementById('insights-refresh-btn');
+    if (refreshBtn) {
+        refreshBtn.addEventListener('click', async () => {
+            refreshBtn.disabled = true;
+            refreshBtn.textContent = 'Aktualisiere...';
+            try {
+                const res = await fetch('/api/insights/refresh', { method: 'POST' });
+                if (res.ok) {
+                    refreshBtn.textContent = 'Aktualisiert (heute)';
+                    setTimeout(() => window.location.reload(), 800);
+                } else if (res.status === 429) {
+                    refreshBtn.textContent = 'Heute bereits genutzt';
+                } else if (res.status === 400) {
+                    refreshBtn.textContent = 'Kein LinkedIn Login';
+                } else {
+                    refreshBtn.textContent = 'Fehler';
+                }
+            } catch (e) {
+                refreshBtn.textContent = 'Fehler';
+            } finally {
+                setTimeout(() => { refreshBtn.disabled = false; }, 2000);
+            }
+        });
+    }
+
+    {% if post_insights and post_insights.has_data %}
+    const seriesData = {{ post_insights.series_chart | tojson }};
+    const reactionMix = {{ post_insights.reaction_mix | tojson }};
+    const weekdayBreakdown = {{ post_insights.weekday_breakdown | tojson }};
+    const lengthBuckets = {{ post_insights.length_buckets | tojson }};
+
+    if (seriesData && seriesData.length) {
+        const dates = seriesData.map(d => d.date.slice(5, 10));
+        const reactions = seriesData.map(d => d.reactions);
+        const likes = seriesData.map(d => d.likes);
+        const comments = seriesData.map(d => d.comments);
+        const shares = seriesData.map(d => d.shares);
+
+        const engagementChart = new ApexCharts(document.querySelector('#chart-engagement'), {
+            chart: { type: 'area', height: 260, toolbar: { show: false }, foreColor: '#e5e7eb', background: 'transparent' },
+            stroke: { curve: 'smooth', width: 2 },
+            dataLabels: { enabled: false },
+            colors: ['#e94560', '#60a5fa', '#f59e0b', '#10b981'],
+            series: [
+                { name: 'Reaktionen', data: reactions },
+                { name: 'Likes', data: likes },
+                { name: 'Comments', data: comments },
+                { name: 'Shares', data: shares }
+            ],
+            xaxis: { categories: dates, labels: { style: { colors: '#9ca3af' } } },
+            yaxis: { labels: { style: { colors: '#9ca3af' } } },
+            grid: { borderColor: 'rgba(233,69,96,0.12)' },
+            legend: { labels: { colors: '#e5e7eb' } },
+            fill: { type: 'gradient', gradient: { opacityFrom: 0.35, opacityTo: 0.05 } },
+            tooltip: { theme: 'dark' }
+        });
+        engagementChart.render();
+    }
+
+    if (reactionMix && reactionMix.length) {
+        const labels = reactionMix.map(r => r.name);
+        const values = reactionMix.map(r => r.count);
+        const reactionsChart = new ApexCharts(document.querySelector('#chart-reactions'), {
+            chart: { type: 'donut', height: 260, foreColor: '#e5e7eb', background: 'transparent' },
+            labels,
+            series: values,
+            legend: { labels: { colors: '#e5e7eb' } },
+            dataLabels: { style: { colors: ['#111827'] } },
+            colors: ['#e94560', '#60a5fa', '#f59e0b', '#10b981', '#a855f7', '#f97316'],
+            stroke: { colors: ['#1a1a2e'] }
+        });
+        reactionsChart.render();
+    }
+
+    if (weekdayBreakdown && weekdayBreakdown.length) {
+        const dayLabels = weekdayBreakdown.map(d => d.day.slice(0, 3));
+        const avgEngagement = weekdayBreakdown.map(d => d.avg_engagement);
+        const counts = weekdayBreakdown.map(d => d.count);
+        const weekdayChart = new ApexCharts(document.querySelector('#chart-weekday'), {
+            chart: { type: 'bar', height: 240, toolbar: { show: false }, foreColor: '#e5e7eb', background: 'transparent' },
+            series: [
+                { name: 'Ø Engagement', data: avgEngagement },
+                { name: 'Posts', data: counts }
+            ],
+            plotOptions: { bar: { columnWidth: '45%', borderRadius: 4 } },
+            xaxis: { categories: dayLabels, labels: { style: { colors: '#9ca3af' } } },
+            yaxis: { labels: { style: { colors: '#9ca3af' } } },
+            grid: { borderColor: 'rgba(233,69,96,0.12)' },
+            colors: ['#e94560', '#60a5fa'],
+            legend: { labels: { colors: '#e5e7eb' } },
+            tooltip: { theme: 'dark' }
+        });
+        weekdayChart.render();
+    }
+
+    if (lengthBuckets) {
+        const bucketLabels = Object.keys(lengthBuckets);
+        const counts = bucketLabels.map(k => lengthBuckets[k].count || 0);
+        const avgScores = bucketLabels.map(k => lengthBuckets[k].avg_engagement || 0);
+        const lengthChart = new ApexCharts(document.querySelector('#chart-length'), {
+            chart: { type: 'bar', height: 240, toolbar: { show: false }, foreColor: '#e5e7eb', background: 'transparent' },
+            series: [
+                { name: 'Posts', data: counts },
+                { name: 'Ø Score', data: avgScores }
+            ],
+            plotOptions: { bar: { columnWidth: '45%', borderRadius: 4 } },
+            xaxis: { categories: bucketLabels, labels: { style: { colors: '#9ca3af' } } },
+            yaxis: { labels: { style: { colors: '#9ca3af' } } },
+            grid: { borderColor: 'rgba(233,69,96,0.12)' },
+            colors: ['#10b981', '#f59e0b'],
+            legend: { labels: { colors: '#e5e7eb' } },
+            tooltip: { theme: 'dark' }
+        });
+        lengthChart.render();
+    }
+    {% endif %}
+});
+</script>
+{% endblock %}

src/web/user/routes.py

@@ -44,6 +44,7 @@ from src.services.storage_service import storage
 from src.services.link_extractor import LinkExtractor, LinkExtractionError
 from src.services.file_extractor import FileExtractor, FileExtractionError
 from src.agents.link_topic_builder import LinkTopicBuilderAgent
+from src.services.post_insights_service import compute_post_insights, refresh_post_insights_for_account
 
 # Router for user frontend
 user_router = APIRouter(tags=["user"])
@@ -2928,7 +2929,7 @@ async def linkedin_callback(
             linkedin_vanity_name = None
             try:
                 profile_response = await client.get(
-                    "https://api.linkedin.com/v2/me",
+                    "https://api.linkedin.com/v2/me?projection=(id,vanityName,localizedFirstName,localizedLastName)",
                     headers={"Authorization": f"Bearer {access_token}"}
                 )
                 if profile_response.status_code == 200:
@@ -3016,6 +3017,41 @@ async def linkedin_disconnect(request: Request):
         raise HTTPException(status_code=500, detail=str(e))
 
 
+# ==================== POST INSIGHTS ====================
+
+@user_router.post("/api/insights/refresh")
+async def refresh_post_insights(request: Request):
+    """Manually refresh post insights (max once per day)."""
+    session = require_user_session(request)
+    if not session:
+        raise HTTPException(status_code=401, detail="Not authenticated")
+
+    try:
+        user_id = UUID(session.user_id)
+        linkedin_account = await db.get_linkedin_account(user_id)
+        if not linkedin_account:
+            raise HTTPException(status_code=400, detail="LinkedIn account not connected")
+
+        profile = await db.get_profile(user_id)
+        metadata = profile.metadata or {}
+        today = datetime.now(timezone.utc).date().isoformat()
+        last_refresh = metadata.get("post_insights_manual_refresh_date")
+        if last_refresh == today:
+            raise HTTPException(status_code=429, detail="Manual refresh already used today")
+
+        await refresh_post_insights_for_account(db, linkedin_account)
+        metadata["post_insights_manual_refresh_date"] = today
+        await db.update_profile(user_id, {"metadata": metadata})
+
+        return {"success": True, "refreshed_at": today}
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.exception(f"Failed to refresh post insights: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 # ==================== COMPANY MANAGEMENT ENDPOINTS ====================
 
 @user_router.get("/company/strategy", response_class=HTMLResponse)
@@ -3579,6 +3615,50 @@ async def employee_strategy_page(request: Request):
     })
 
 
+@user_router.get("/employee/insights", response_class=HTMLResponse)
+async def employee_insights_page(request: Request):
+    """Employee post insights page."""
+    session = require_user_session(request)
+    if not session:
+        return RedirectResponse(url="/login", status_code=302)
+
+    if session.account_type != "employee":
+        return RedirectResponse(url="/", status_code=302)
+
+    try:
+        user_id = UUID(session.user_id)
+        profile_picture = await get_user_avatar(session, user_id)
+        linkedin_account = await db.get_linkedin_account(user_id)
+
+        post_insights = {"has_data": False}
+        if linkedin_account:
+            try:
+                from datetime import date, timedelta
+                since = (date.today() - timedelta(days=90)).isoformat()
+                insights_posts = await db.get_post_insights_posts(user_id)
+                insights_daily = await db.get_post_insights_daily(user_id, since_date=since)
+                post_insights = compute_post_insights(insights_posts, insights_daily)
+            except Exception as e:
+                logger.error(f"Error computing post insights: {e}")
+
+        return templates.TemplateResponse("employee_insights.html", {
+            "request": request,
+            "page": "insights",
+            "session": session,
+            "profile_picture": profile_picture,
+            "linkedin_account": linkedin_account,
+            "post_insights": post_insights
+        })
+    except Exception as e:
+        logger.error(f"Error loading insights: {e}")
+        return templates.TemplateResponse("employee_insights.html", {
+            "request": request,
+            "page": "insights",
+            "session": session,
+            "error": str(e)
+        })
+
+
 # ============================================================================
 # EMPLOYEE POST TYPES MANAGEMENT
 # ============================================================================