{"type":"queue-operation","operation":"enqueue","timestamp":"2026-07-14T15:11:29.821Z","sessionId":"d0159b57-65bf-4ee3-8bf1-14b9ce55cb8d","content":"Review these two Python files for logic bugs causing stale links, hub/search URLs, wrong positive/neutral classification, missing article date/highlight, and no Telegram notification after report generation. Return exact issues and minimal patch recommendations.\nFILE: report_generator.py\n#!/usr/bin/env python3\n\"\"\"\nReport Generator — Brand Monitoring V1\nProduces crisis-first markdown reports in the format specified by Architecture V3.\n\"\"\"\n\nimport sqlite3\nimport datetime\nimport os\n\nDB_PATH = \"/opt/ai-os/products/ceo/brand-monitoring/data/brand-monitoring.db\"\nREPORT_DIR = \"/opt/ai-os/products/ceo/brand-monitoring/reports\"\n\ndef iso_to_vn(iso_str):\n    \"\"\"Convert ISO datetime to VN-friendly format.\"\"\"\n    if not iso_str:\n        return \"\"\n    try:\n        dt = datetime.datetime.fromisoformat(iso_str)\n        return dt.strftime(\"%H:%M %d/%m/%Y\")\n    except:\n        return iso_str[:16].replace(\"T\", \" \")\n\ndef query_mentions(period_start=None, period_end=None, limit=100):\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    cursor = conn.cursor()\n\n    where_clauses = [\"m.brand_id = 'fulbright'\"]\n    params = []\n\n    if period_start:\n        where_clauses.append(\"m.published >= ?\")\n        params.append(period_start)\n    if period_end:\n        where_clauses.append(\"m.published < ?\")\n        params.append(period_end)\n\n    where = \" AND \".join(where_clauses)\n\n    cursor.execute(f\"\"\"\n        SELECT m.*, r.source_id\n        FROM mentions m\n        JOIN raw_items r ON r.id = m.raw_item_id\n        WHERE {where}\n        ORDER BY m.is_crisis DESC, m.relevance_score DESC\n    \"\"\", params)\n    rows = cursor.fetchall()\n    conn.close()\n\n    filtered = []\n    for m in rows:\n        title = (m[\"title_clean\"] or \"\").lower()\n        url = (m[\"url_canonical\"] or \"\").lower()\n        if 'tin tức, hình ảnh, video clip mới nhất về' in title:\n            continue\n        if title.startswith('#fulbright'):\n            continue\n        if '/tag/' in url or '/search' in url or '/tim-kiem' in url:\n            continue\n        if 'google.com/rss/articles' in url and 'oc=5' in url and ('hl=' in url or 'ceid=' in url):\n            # keep article links only when title itself is article-like and not a hub title\n            if 'fulbright' not in title and 'fuv' not in title and 'fsppm' not in title:\n                continue\n        if m['relevance_score'] < 25:\n            continue\n        filtered.append(m)\n    return filtered[:limit]\n\ndef query_alerts(period_start=None, period_end=None):\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    cursor = conn.cursor()\n    where = [\"a.alert_type = 'hot_alert'\"]\n    params = []\n    if period_start:\n        where.append(\"m.published >= ?\")\n        params.append(period_start)\n    if period_end:\n        where.append(\"m.published < ?\")\n        params.append(period_end)\n    cursor.execute(f\"\"\"\n        SELECT a.*, m.title_clean, m.url_canonical, m.relevance_score, m.sentiment, \n               m.crisis_reason, r.source_id, m.published\n        FROM alerts a\n        JOIN mentions m ON m.id = a.mention_id\n        JOIN raw_items r ON r.id = m.raw_item_id\n        WHERE {' AND '.join(where)}\n        ORDER BY a.id DESC\n        LIMIT 50\n    \"\"\", params)\n    rows = cursor.fetchall()\n    conn.close()\n    return rows\n\ndef count_sentiment(rows):\n    counts = {\"positive\": 0, \"neutral\": 0, \"negative\": 0, \"ambiguous\": 0}\n    for r in rows:\n        sent = r[\"sentiment\"] or \"neutral\"\n        counts[sent] = counts.get(sent, 0) + 1\n    return counts\n\ndef _highlight(text, brand_terms=None):\n    brand_terms = brand_terms or ['Fulbright', 'FUV', 'FSPPM', 'Trường Đại học Fulbright Việt Nam', 'Trường Chính sách công và Quản lý Fulbright']\n    text = text or ''\n    snippets = []\n    lower = text.lower()\n    for term in brand_terms:\n        if term.lower() in lower:\n            snippets.append(term)\n    if snippets:\n        return ', '.join(sorted(set(snippets), key=lambda x: x.lower()))\n    return 'No direct Fulbright mention in title; check article body/snippet.'\n\n\ndef _is_hub_title(title):\n    t = (title or '').lower()\n    return ('tin tức, hình ảnh, video clip mới nhất về' in t or t.startswith('#fulbright') or '/tag/' in t)\n\n\ndef generate_report(report_type=\"weekly\", period_start=None, period_end=None):\n    os.makedirs(REPORT_DIR, exist_ok=True)\n    now = datetime.datetime.now()\n    today = now.strftime(\"%Y-%m-%d\")\n    iso_week = now.strftime(\"W%V\")\n    \n    if not period_start:\n        if report_type == \"weekly\":\n            period_start = (now - datetime.timedelta(days=7)).strftime(\"%Y-%m-%d\")\n        else:\n            period_start = today\n    if not period_end:\n        period_end = today\n    \n    mentions = query_mentions(period_start, period_end + \"T23:59:59\")\n    alerts = query_alerts(period_start, period_end + \"T23:59:59\")\n    sent_counts = count_sentiment(mentions)\n    \n    # Group mentions by sentiment\n    groups = {\"hot_alert\": [], \"negative\": [], \"neutral\": [], \"positive\": []}\n    for m in mentions:\n        sent = m[\"sentiment\"] or \"neutral\"\n        is_crisis = m[\"is_crisis\"]\n        if is_crisis and sent in (\"negative\", \"ambiguous\"):\n            groups[\"hot_alert\"].append(m)\n        elif sent == \"negative\":\n            groups[\"negative\"].append(m)\n        elif sent == \"neutral\":\n            groups[\"neutral\"].append(m)\n        else:\n            groups[\"positive\"].append(m)\n    \n    # Calculate source breakdown\n    source_counts = {}\n    for m in mentions:\n        src = m[\"source_id\"]\n        source_counts[src] = source_counts.get(src, 0) + 1\n    top_sources = sorted(source_counts.items(), key=lambda x: -x[1])[:10]\n    \n    report_filename = f\"{today}-fulbright-{report_type}.md\"\n    report_path = os.path.join(REPORT_DIR, report_filename)\n    \n    with open(report_path, \"w\", encoding=\"utf-8\") as f:\n        f.write(\"# Bao cao Brand Monitoring — Fulbright\\n\\n\")\n        f.write(f\"**Loai bao cao:** {report_type}\\n\")\n        f.write(f\"**Ky bao cao:** {period_start} → {period_end}\\n\")\n        f.write(f\"**Ngay tao:** {today}\\n\\n\")\n        f.write(\"---\\n\\n\")\n        \n        # Section 1: Crisis & Risk Summary\n        f.write(\"## 1. Crisis & Risk Summary\\n\\n\")\n        crisis_mentions = groups[\"hot_alert\"]\n        f.write(f\"- **Hot alerts triggered:** {len(alerts)}\\n\")\n        f.write(f\"- **Negative + high relevance:** {len(crisis_mentions)}\\n\")\n        f.write(f\"- **Sentiment breakdown:** \")\n        f.write(f\"Positive {sent_counts['positive']} | \")\n        f.write(f\"Neutral {sent_counts['neutral']} | \")\n        f.write(f\"Negative {sent_counts['negative']} | \")\n        f.write(f\"Ambiguous {sent_counts['ambiguous']}\\n\\n\")\n        \n        if crisis_mentions:\n            f.write(\"### Hot-alert items\\n\\n\")\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Diem | Cam xuc | Ly do |\\n\")\n            f.write(\"|---|------|-----------|---------|---------------------|------|---------|-------|\\n\")\n            for i, m in enumerate(crisis_mentions[:20], 1):\n                reason = (m[\"crisis_reason\"] or \"\")[:50]\n                title = m[\"title_clean\"][:80]\n                url = m[\"url_canonical\"]\n                highlight = _highlight(title)\n                published = (m[\"published\"] or \"\")[:10]\n                f.write(f\"| {i} | {m['source_id']} | {published} | [{title}]({url}) | {highlight} | {m['relevance_score']} | {m['sentiment']} | {reason} |\\n\")\n        \n        if alerts:\n            f.write(\"\\n### Alert log\\n\\n\")\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Trang thai |\\n\")\n            f.write(\"|---|------|-----------|---------|---------------------|------------|\\n\")\n            for i, a in enumerate(alerts[:20], 1):\n                status = a[\"status\"] or \"pending\"\n                title = a[\"title_clean\"][:80]\n                url = a[\"url_canonical\"]\n                published = (a[\"published\"] or \"\")[:10]\n                highlight = _highlight(title)\n                f.write(f\"| {i} | {a['source_id']} | {published} | [{title}]({url}) | {highlight} | `{status}` |\\n\")\n        \n        # Section 2: Negative Items\n        f.write(\"\\n## 2. Negative Coverage\\n\\n\")\n        neg_items = groups[\"negative\"]\n        if neg_items:\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Diem | Cam xuc |\\n\")\n            f.write(\"|---|-------|-----------|---------|---------------------|------|---------|\\n\")\n            for i, m in enumerate(neg_items[:20], 1):\n                title = m[\"title_clean\"][:80]\n                url = m[\"url_canonical\"]\n                highlight = _highlight(title)\n                published = (m[\"published\"] or \"\")[:10]\n                f.write(f\"| {i} | {m['source_id'][:12]} | {published} | [{title}]({url}) | {highlight} | {m['relevance_score']} | {m['sentiment']} |\\n\")\n        else:\n            f.write(\"Khong phat hien noi dung tieu cuc.\\n\")\n        \n        # Section 3: Neutral Coverage\n        f.write(\"\\n## 3. Neutral Coverage\\n\\n\")\n        neu_items = groups[\"neutral\"]\n        f.write(f\"Tong cong: {len(neu_items)} mention trung lap.\\n\\n\")\n        if neu_items:\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Diem |\\n\")\n            f.write(\"|---|-------|-----------|---------|---------------------|------|\\n\")\n            for i, m in enumerate(neu_items[:20], 1):\n                title = m[\"title_clean\"][:80]\n                url = m[\"url_canonical\"]\n                highlight = _highlight(title)\n                published = (m[\"published\"] or \"\")[:10]\n                f.write(f\"| {i} | {m['source_id'][:12]} | {published} | [{title}]({url}) | {highlight} | {m['relevance_score']} |\\n\")\n        \n        # Section 4: Positive Signals\n        f.write(\"\\n## 4. Positive Signals\\n\\n\")\n        pos_items = groups[\"positive\"]\n        if pos_items:\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Diem |\\n\")\n            f.write(\"|---|-------|-----------|---------|---------------------|------|\\n\")\n            for i, m in enumerate(pos_items[:15], 1):\n                title = m[\"title_clean\"][:80]\n                url = m[\"url_canonical\"]\n                highlight = _highlight(title)\n                published = (m[\"published\"] or \"\")[:10]\n                f.write(f\"| {i} | {m['source_id'][:12]} | {published} | [{title}]({url}) | {highlight} | {m['relevance_score']} |\\n\")\n        else:\n            f.write(\"Khong phat hien tin hieu tich cuc.\\n\")\n        \n        # Section 5: Topic Clusters (simplified - keyword grouping)\n        f.write(\"\\n## 5. Topic Clusters\\n\\n\")\n        topic_groups = {\n            \"Hoc bong & Tai tro\": [\"hoc bong\", \"tai tro\", \"hoc phi\", \"tien\"],\n            \"Chuan dau ra & Kiem dinh\": [\"kiem dinh\", \"chuan\", \"kiểm định\"],\n            \"Tuyen sinh & Dao tao\": [\"tuyen sinh\", \"sinh vien\", \"dao tao\", \"tốt nghiệp\"],\n            \"Chinh tri & Tranh cai\": [\"tranh cai\", \"chinh tri\", \"to cao\", \"cáo buộc\", \"ngoại giao\"],\n            \"Hop tac & Doanh nghiep\": [\"hop tac\", \"ky ket\", \"đối tác\", \"hợp tác\"],\n        }\n        for topic, kws in topic_groups.items():\n            count = sum(1 for m in mentions if any(kw in (m[\"title_clean\"] or \"\").lower() for kw in kws))\n            if count > 0:\n                f.write(f\"- **{topic}:** {count} mentions\\n\")\n        \n        # Section 6: Recommended Actions\n        f.write(\"\\n## 6. Recommended Actions\\n\\n\")\n        actions = []\n        if groups[\"hot_alert\"]:\n            actions.append(\"Theo doi cac hot alert can xu ly ngay.\")\n        if groups[\"negative\"]:\n            actions.append(\"Phan hoi/chuan bi phan hoi cho cac noi dung tieu cuc.\")\n        if sent_counts[\"positive\"] > sent_counts[\"negative\"]:\n            actions.append(\"Tiep tuc duy tri cac tin hieu tich cuc tren bao chi.\")\n        actions.append(\"Cap nhat keyword set neu phat hien chu de moi.\")\n        top_src_name = top_sources[0][0] if top_sources else \"N/A\"\n        actions.append(f\"Chu y nguon {top_src_name} — dang la nguon noi bat nhat.\")\n        \n        for i, action in enumerate(actions, 1):\n            f.write(f\"{i}. {action}\\n\")\n        \n        # Source breakdown\n        f.write(\"\\n---\\n\\n## Data Summary\\n\\n\")\n\nFILE: alert_dispatcher.py\n#!/usr/bin/env python3\n\"\"\"\nAlert Dispatcher — Brand Monitoring V1\nDispatches pending alerts as a single digest message to Telegram thread using report.yaml config.\nUses 'hermes send' command.\n\"\"\"\n\nimport sqlite3\nimport datetime\nimport subprocess\nimport yaml\nfrom collections import defaultdict\n\nDB_PATH = \"/opt/ai-os/products/ceo/brand-monitoring/data/brand-monitoring.db\"\nBRAND_DIR = \"/opt/ai-os/products/ceo/brand-monitoring/brands/fulbright\"\nDEFAULT_PERIOD_LABEL = \"07/2026\"\n\n\ndef load_telegram_target():\n    with open(f\"{BRAND_DIR}/report.yaml\", \"r\", encoding=\"utf-8\") as f:\n        config = yaml.safe_load(f)\n    alerts_conf = config[\"channels\"][\"alerts\"]\n    chat_id = alerts_conf[\"chat_id\"]\n    thread_id = alerts_conf.get(\"thread_id\", \"\")\n    return f\"telegram:{chat_id}:{thread_id}\" if thread_id else f\"telegram:{chat_id}\"\n\n\ndef get_pending_alerts():\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    cursor = conn.cursor()\n    cursor.execute(\"\"\"\n        SELECT a.id as alert_id, a.alert_type, a.sent_at,\n               m.id as mention_id, m.title_clean, m.url_canonical, m.published,\n               m.relevance_score, m.sentiment, m.crisis_reason,\n               m.source_tier, r.source_id\n        FROM alerts a\n        JOIN mentions m ON m.id = a.mention_id\n        JOIN raw_items r ON r.id = m.raw_item_id\n        WHERE a.status = 'pending'\n        ORDER BY m.published DESC, a.id DESC\n    \"\"\")\n    rows = cursor.fetchall()\n    conn.close()\n    return rows\n\n\ndef send_to_telegram(target, message):\n    cmd = [\"hermes\", \"send\", \"--to\", target, message]\n    try:\n        result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)\n        return result.returncode == 0, (result.stdout or result.stderr).strip()\n    except Exception as e:\n        return False, str(e)\n\n\ndef parse_day(ts):\n    if not ts:\n        return \"\"\n    try:\n        return datetime.datetime.fromisoformat(ts.replace(\"Z\", \"+00:00\")).strftime(\"%d/%m/%Y\")\n    except Exception:\n        return str(ts)[:10]\n\n\ndef highlight_title(title, reason):\n    reason = (reason or \"\").lower()\n    if not title:\n        return \"\"\n    if \"học phí\" in reason:\n        return title\n    return title[:220]\n\n\ndef sentiment_label(sentiment, relevance_score, is_crisis):\n    s = sentiment or \"neutral\"\n    if is_crisis:\n        return f\"{s} / crisis\"\n    return s\n\n\ndef build_digest(alerts):\n    if not alerts:\n        return None\n\n    period_start = \"01/07/2026\"\n    period_end = \"14/07/2026\"\n    grouped = defaultdict(list)\n    for row in alerts:\n        grouped[row[\"source_id\"]].append(row)\n\n    lines = []\n    lines.append(\"📢 *FULBRIGHT BRAND MONITORING UPDATE*\")\n    lines.append(f\"🕒 *Date period:* {period_start} → {period_end}\")\n    lines.append(f\"🧾 *Alerts:* {len(alerts)} pending\")\n    lines.append(\"\")\n\n    for source, items in grouped.items():\n        for row in items[:5]:\n            published_day = parse_day(row[\"published\"])\n            title = row[\"title_clean\"] or \"(no title)\"\n            url = row[\"url_canonical\"] or \"\"\n            score = row[\"relevance_score\"] or 0\n            sentiment = row[\"sentiment\"] or \"neutral\"\n            crisis_reason = row[\"crisis_reason\"] or \"\"\n            label = sentiment_label(sentiment, score, 1 if crisis_reason else 0)\n            highlight = highlight_title(title, crisis_reason)\n            lines.append(f\"📰 *Source:* `{source}`\")\n            lines.append(f\"📅 *Air date:* `{published_day}`\")\n            lines.append(f\"🔗 *Article:* [{highlight}]({url})\")\n            lines.append(f\"✨ *Fulbright highlight:* {highlight}\")\n            lines.append(f\"📊 *Sentiment / Brand health:* `{label}` | score `{score}`\")\n            if crisis_reason:\n                lines.append(f\"⚠️ *Discussion:* `{crisis_reason}`\")\n            lines.append(\"\")\n\n    return \"\\n\".join(lines).strip()\n\n\ndef dispatch_alerts():\n    alerts = get_pending_alerts()\n    if not alerts:\n        print(\"No pending alerts to dispatch.\")\n        return\n\n    target = load_telegram_target()\n    print(f\"Targeting: {target}\")\n    message = build_digest(alerts)\n    if not message:\n        print(\"No message built.\")\n        return\n\n    success, output = send_to_telegram(target, message)\n    conn = sqlite3.connect(DB_PATH)\n    cursor = conn.cursor()\n    if success:\n        now = datetime.datetime.now(datetime.timezone.utc).isoformat()\n        ids = [row[\"alert_id\"] for row in alerts]\n        cursor.executemany(\n            \"UPDATE alerts SET status = 'sent', sent_at = ?, channel = 'telegram' WHERE id = ?\",\n            [(now, alert_id) for alert_id in ids],\n        )\n        conn.commit()\n        conn.close()\n        print(f\"SENT digest covering {len(ids)} alerts\")\n        print(output)\n        return\n\n    now = datetime.datetime.now(datetime.timezone.utc).isoformat()\n    ids = [row[\"alert_id\"] for row in alerts]\n    cursor.executemany(\n        \"UPDATE alerts SET status = 'failed', sent_at = ? WHERE id = ?\",\n        [(now, alert_id) for alert_id in ids],\n    )\n    conn.commit()\n    conn.close()\n    print(f\"FAILED digest: {output}\")\n\n\nif __name__ == \"__main__\":\n    dispatch_alerts()\n"}
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-07-14T15:11:29.823Z","sessionId":"d0159b57-65bf-4ee3-8bf1-14b9ce55cb8d"}
{"parentUuid":null,"isSidechain":false,"promptId":"c8742549-a56f-4b43-a87e-34142797401d","type":"user","message":{"role":"user","content":"Review these two Python files for logic bugs causing stale links, hub/search URLs, wrong positive/neutral classification, missing article date/highlight, and no Telegram notification after report generation. Return exact issues and minimal patch recommendations.\nFILE: report_generator.py\n#!/usr/bin/env python3\n\"\"\"\nReport Generator — Brand Monitoring V1\nProduces crisis-first markdown reports in the format specified by Architecture V3.\n\"\"\"\n\nimport sqlite3\nimport datetime\nimport os\n\nDB_PATH = \"/opt/ai-os/products/ceo/brand-monitoring/data/brand-monitoring.db\"\nREPORT_DIR = \"/opt/ai-os/products/ceo/brand-monitoring/reports\"\n\ndef iso_to_vn(iso_str):\n    \"\"\"Convert ISO datetime to VN-friendly format.\"\"\"\n    if not iso_str:\n        return \"\"\n    try:\n        dt = datetime.datetime.fromisoformat(iso_str)\n        return dt.strftime(\"%H:%M %d/%m/%Y\")\n    except:\n        return iso_str[:16].replace(\"T\", \" \")\n\ndef query_mentions(period_start=None, period_end=None, limit=100):\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    cursor = conn.cursor()\n\n    where_clauses = [\"m.brand_id = 'fulbright'\"]\n    params = []\n\n    if period_start:\n        where_clauses.append(\"m.published >= ?\")\n        params.append(period_start)\n    if period_end:\n        where_clauses.append(\"m.published < ?\")\n        params.append(period_end)\n\n    where = \" AND \".join(where_clauses)\n\n    cursor.execute(f\"\"\"\n        SELECT m.*, r.source_id\n        FROM mentions m\n        JOIN raw_items r ON r.id = m.raw_item_id\n        WHERE {where}\n        ORDER BY m.is_crisis DESC, m.relevance_score DESC\n    \"\"\", params)\n    rows = cursor.fetchall()\n    conn.close()\n\n    filtered = []\n    for m in rows:\n        title = (m[\"title_clean\"] or \"\").lower()\n        url = (m[\"url_canonical\"] or \"\").lower()\n        if 'tin tức, hình ảnh, video clip mới nhất về' in title:\n            continue\n        if title.startswith('#fulbright'):\n            continue\n        if '/tag/' in url or '/search' in url or '/tim-kiem' in url:\n            continue\n        if 'google.com/rss/articles' in url and 'oc=5' in url and ('hl=' in url or 'ceid=' in url):\n            # keep article links only when title itself is article-like and not a hub title\n            if 'fulbright' not in title and 'fuv' not in title and 'fsppm' not in title:\n                continue\n        if m['relevance_score'] < 25:\n            continue\n        filtered.append(m)\n    return filtered[:limit]\n\ndef query_alerts(period_start=None, period_end=None):\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    cursor = conn.cursor()\n    where = [\"a.alert_type = 'hot_alert'\"]\n    params = []\n    if period_start:\n        where.append(\"m.published >= ?\")\n        params.append(period_start)\n    if period_end:\n        where.append(\"m.published < ?\")\n        params.append(period_end)\n    cursor.execute(f\"\"\"\n        SELECT a.*, m.title_clean, m.url_canonical, m.relevance_score, m.sentiment, \n               m.crisis_reason, r.source_id, m.published\n        FROM alerts a\n        JOIN mentions m ON m.id = a.mention_id\n        JOIN raw_items r ON r.id = m.raw_item_id\n        WHERE {' AND '.join(where)}\n        ORDER BY a.id DESC\n        LIMIT 50\n    \"\"\", params)\n    rows = cursor.fetchall()\n    conn.close()\n    return rows\n\ndef count_sentiment(rows):\n    counts = {\"positive\": 0, \"neutral\": 0, \"negative\": 0, \"ambiguous\": 0}\n    for r in rows:\n        sent = r[\"sentiment\"] or \"neutral\"\n        counts[sent] = counts.get(sent, 0) + 1\n    return counts\n\ndef _highlight(text, brand_terms=None):\n    brand_terms = brand_terms or ['Fulbright', 'FUV', 'FSPPM', 'Trường Đại học Fulbright Việt Nam', 'Trường Chính sách công và Quản lý Fulbright']\n    text = text or ''\n    snippets = []\n    lower = text.lower()\n    for term in brand_terms:\n        if term.lower() in lower:\n            snippets.append(term)\n    if snippets:\n        return ', '.join(sorted(set(snippets), key=lambda x: x.lower()))\n    return 'No direct Fulbright mention in title; check article body/snippet.'\n\n\ndef _is_hub_title(title):\n    t = (title or '').lower()\n    return ('tin tức, hình ảnh, video clip mới nhất về' in t or t.startswith('#fulbright') or '/tag/' in t)\n\n\ndef generate_report(report_type=\"weekly\", period_start=None, period_end=None):\n    os.makedirs(REPORT_DIR, exist_ok=True)\n    now = datetime.datetime.now()\n    today = now.strftime(\"%Y-%m-%d\")\n    iso_week = now.strftime(\"W%V\")\n    \n    if not period_start:\n        if report_type == \"weekly\":\n            period_start = (now - datetime.timedelta(days=7)).strftime(\"%Y-%m-%d\")\n        else:\n            period_start = today\n    if not period_end:\n        period_end = today\n    \n    mentions = query_mentions(period_start, period_end + \"T23:59:59\")\n    alerts = query_alerts(period_start, period_end + \"T23:59:59\")\n    sent_counts = count_sentiment(mentions)\n    \n    # Group mentions by sentiment\n    groups = {\"hot_alert\": [], \"negative\": [], \"neutral\": [], \"positive\": []}\n    for m in mentions:\n        sent = m[\"sentiment\"] or \"neutral\"\n        is_crisis = m[\"is_crisis\"]\n        if is_crisis and sent in (\"negative\", \"ambiguous\"):\n            groups[\"hot_alert\"].append(m)\n        elif sent == \"negative\":\n            groups[\"negative\"].append(m)\n        elif sent == \"neutral\":\n            groups[\"neutral\"].append(m)\n        else:\n            groups[\"positive\"].append(m)\n    \n    # Calculate source breakdown\n    source_counts = {}\n    for m in mentions:\n        src = m[\"source_id\"]\n        source_counts[src] = source_counts.get(src, 0) + 1\n    top_sources = sorted(source_counts.items(), key=lambda x: -x[1])[:10]\n    \n    report_filename = f\"{today}-fulbright-{report_type}.md\"\n    report_path = os.path.join(REPORT_DIR, report_filename)\n    \n    with open(report_path, \"w\", encoding=\"utf-8\") as f:\n        f.write(\"# Bao cao Brand Monitoring — Fulbright\\n\\n\")\n        f.write(f\"**Loai bao cao:** {report_type}\\n\")\n        f.write(f\"**Ky bao cao:** {period_start} → {period_end}\\n\")\n        f.write(f\"**Ngay tao:** {today}\\n\\n\")\n        f.write(\"---\\n\\n\")\n        \n        # Section 1: Crisis & Risk Summary\n        f.write(\"## 1. Crisis & Risk Summary\\n\\n\")\n        crisis_mentions = groups[\"hot_alert\"]\n        f.write(f\"- **Hot alerts triggered:** {len(alerts)}\\n\")\n        f.write(f\"- **Negative + high relevance:** {len(crisis_mentions)}\\n\")\n        f.write(f\"- **Sentiment breakdown:** \")\n        f.write(f\"Positive {sent_counts['positive']} | \")\n        f.write(f\"Neutral {sent_counts['neutral']} | \")\n        f.write(f\"Negative {sent_counts['negative']} | \")\n        f.write(f\"Ambiguous {sent_counts['ambiguous']}\\n\\n\")\n        \n        if crisis_mentions:\n            f.write(\"### Hot-alert items\\n\\n\")\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Diem | Cam xuc | Ly do |\\n\")\n            f.write(\"|---|------|-----------|---------|---------------------|------|---------|-------|\\n\")\n            for i, m in enumerate(crisis_mentions[:20], 1):\n                reason = (m[\"crisis_reason\"] or \"\")[:50]\n                title = m[\"title_clean\"][:80]\n                url = m[\"url_canonical\"]\n                highlight = _highlight(title)\n                published = (m[\"published\"] or \"\")[:10]\n                f.write(f\"| {i} | {m['source_id']} | {published} | [{title}]({url}) | {highlight} | {m['relevance_score']} | {m['sentiment']} | {reason} |\\n\")\n        \n        if alerts:\n            f.write(\"\\n### Alert log\\n\\n\")\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Trang thai |\\n\")\n            f.write(\"|---|------|-----------|---------|---------------------|------------|\\n\")\n            for i, a in enumerate(alerts[:20], 1):\n                status = a[\"status\"] or \"pending\"\n                title = a[\"title_clean\"][:80]\n                url = a[\"url_canonical\"]\n                published = (a[\"published\"] or \"\")[:10]\n                highlight = _highlight(title)\n                f.write(f\"| {i} | {a['source_id']} | {published} | [{title}]({url}) | {highlight} | `{status}` |\\n\")\n        \n        # Section 2: Negative Items\n        f.write(\"\\n## 2. Negative Coverage\\n\\n\")\n        neg_items = groups[\"negative\"]\n        if neg_items:\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Diem | Cam xuc |\\n\")\n            f.write(\"|---|-------|-----------|---------|---------------------|------|---------|\\n\")\n            for i, m in enumerate(neg_items[:20], 1):\n                title = m[\"title_clean\"][:80]\n                url = m[\"url_canonical\"]\n                highlight = _highlight(title)\n                published = (m[\"published\"] or \"\")[:10]\n                f.write(f\"| {i} | {m['source_id'][:12]} | {published} | [{title}]({url}) | {highlight} | {m['relevance_score']} | {m['sentiment']} |\\n\")\n        else:\n            f.write(\"Khong phat hien noi dung tieu cuc.\\n\")\n        \n        # Section 3: Neutral Coverage\n        f.write(\"\\n## 3. Neutral Coverage\\n\\n\")\n        neu_items = groups[\"neutral\"]\n        f.write(f\"Tong cong: {len(neu_items)} mention trung lap.\\n\\n\")\n        if neu_items:\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Diem |\\n\")\n            f.write(\"|---|-------|-----------|---------|---------------------|------|\\n\")\n            for i, m in enumerate(neu_items[:20], 1):\n                title = m[\"title_clean\"][:80]\n                url = m[\"url_canonical\"]\n                highlight = _highlight(title)\n                published = (m[\"published\"] or \"\")[:10]\n                f.write(f\"| {i} | {m['source_id'][:12]} | {published} | [{title}]({url}) | {highlight} | {m['relevance_score']} |\\n\")\n        \n        # Section 4: Positive Signals\n        f.write(\"\\n## 4. Positive Signals\\n\\n\")\n        pos_items = groups[\"positive\"]\n        if pos_items:\n            f.write(\"| # | Nguon | Ngay dang | Bai bao | Highlight Fulbright | Diem |\\n\")\n            f.write(\"|---|-------|-----------|---------|---------------------|------|\\n\")\n            for i, m in enumerate(pos_items[:15], 1):\n                title = m[\"title_clean\"][:80]\n                url = m[\"url_canonical\"]\n                highlight = _highlight(title)\n                published = (m[\"published\"] or \"\")[:10]\n                f.write(f\"| {i} | {m['source_id'][:12]} | {published} | [{title}]({url}) | {highlight} | {m['relevance_score']} |\\n\")\n        else:\n            f.write(\"Khong phat hien tin hieu tich cuc.\\n\")\n        \n        # Section 5: Topic Clusters (simplified - keyword grouping)\n        f.write(\"\\n## 5. Topic Clusters\\n\\n\")\n        topic_groups = {\n            \"Hoc bong & Tai tro\": [\"hoc bong\", \"tai tro\", \"hoc phi\", \"tien\"],\n            \"Chuan dau ra & Kiem dinh\": [\"kiem dinh\", \"chuan\", \"kiểm định\"],\n            \"Tuyen sinh & Dao tao\": [\"tuyen sinh\", \"sinh vien\", \"dao tao\", \"tốt nghiệp\"],\n            \"Chinh tri & Tranh cai\": [\"tranh cai\", \"chinh tri\", \"to cao\", \"cáo buộc\", \"ngoại giao\"],\n            \"Hop tac & Doanh nghiep\": [\"hop tac\", \"ky ket\", \"đối tác\", \"hợp tác\"],\n        }\n        for topic, kws in topic_groups.items():\n            count = sum(1 for m in mentions if any(kw in (m[\"title_clean\"] or \"\").lower() for kw in kws))\n            if count > 0:\n                f.write(f\"- **{topic}:** {count} mentions\\n\")\n        \n        # Section 6: Recommended Actions\n        f.write(\"\\n## 6. Recommended Actions\\n\\n\")\n        actions = []\n        if groups[\"hot_alert\"]:\n            actions.append(\"Theo doi cac hot alert can xu ly ngay.\")\n        if groups[\"negative\"]:\n            actions.append(\"Phan hoi/chuan bi phan hoi cho cac noi dung tieu cuc.\")\n        if sent_counts[\"positive\"] > sent_counts[\"negative\"]:\n            actions.append(\"Tiep tuc duy tri cac tin hieu tich cuc tren bao chi.\")\n        actions.append(\"Cap nhat keyword set neu phat hien chu de moi.\")\n        top_src_name = top_sources[0][0] if top_sources else \"N/A\"\n        actions.append(f\"Chu y nguon {top_src_name} — dang la nguon noi bat nhat.\")\n        \n        for i, action in enumerate(actions, 1):\n            f.write(f\"{i}. {action}\\n\")\n        \n        # Source breakdown\n        f.write(\"\\n---\\n\\n## Data Summary\\n\\n\")\n\nFILE: alert_dispatcher.py\n#!/usr/bin/env python3\n\"\"\"\nAlert Dispatcher — Brand Monitoring V1\nDispatches pending alerts as a single digest message to Telegram thread using report.yaml config.\nUses 'hermes send' command.\n\"\"\"\n\nimport sqlite3\nimport datetime\nimport subprocess\nimport yaml\nfrom collections import defaultdict\n\nDB_PATH = \"/opt/ai-os/products/ceo/brand-monitoring/data/brand-monitoring.db\"\nBRAND_DIR = \"/opt/ai-os/products/ceo/brand-monitoring/brands/fulbright\"\nDEFAULT_PERIOD_LABEL = \"07/2026\"\n\n\ndef load_telegram_target():\n    with open(f\"{BRAND_DIR}/report.yaml\", \"r\", encoding=\"utf-8\") as f:\n        config = yaml.safe_load(f)\n    alerts_conf = config[\"channels\"][\"alerts\"]\n    chat_id = alerts_conf[\"chat_id\"]\n    thread_id = alerts_conf.get(\"thread_id\", \"\")\n    return f\"telegram:{chat_id}:{thread_id}\" if thread_id else f\"telegram:{chat_id}\"\n\n\ndef get_pending_alerts():\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    cursor = conn.cursor()\n    cursor.execute(\"\"\"\n        SELECT a.id as alert_id, a.alert_type, a.sent_at,\n               m.id as mention_id, m.title_clean, m.url_canonical, m.published,\n               m.relevance_score, m.sentiment, m.crisis_reason,\n               m.source_tier, r.source_id\n        FROM alerts a\n        JOIN mentions m ON m.id = a.mention_id\n        JOIN raw_items r ON r.id = m.raw_item_id\n        WHERE a.status = 'pending'\n        ORDER BY m.published DESC, a.id DESC\n    \"\"\")\n    rows = cursor.fetchall()\n    conn.close()\n    return rows\n\n\ndef send_to_telegram(target, message):\n    cmd = [\"hermes\", \"send\", \"--to\", target, message]\n    try:\n        result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)\n        return result.returncode == 0, (result.stdout or result.stderr).strip()\n    except Exception as e:\n        return False, str(e)\n\n\ndef parse_day(ts):\n    if not ts:\n        return \"\"\n    try:\n        return datetime.datetime.fromisoformat(ts.replace(\"Z\", \"+00:00\")).strftime(\"%d/%m/%Y\")\n    except Exception:\n        return str(ts)[:10]\n\n\ndef highlight_title(title, reason):\n    reason = (reason or \"\").lower()\n    if not title:\n        return \"\"\n    if \"học phí\" in reason:\n        return title\n    return title[:220]\n\n\ndef sentiment_label(sentiment, relevance_score, is_crisis):\n    s = sentiment or \"neutral\"\n    if is_crisis:\n        return f\"{s} / crisis\"\n    return s\n\n\ndef build_digest(alerts):\n    if not alerts:\n        return None\n\n    period_start = \"01/07/2026\"\n    period_end = \"14/07/2026\"\n    grouped = defaultdict(list)\n    for row in alerts:\n        grouped[row[\"source_id\"]].append(row)\n\n    lines = []\n    lines.append(\"📢 *FULBRIGHT BRAND MONITORING UPDATE*\")\n    lines.append(f\"🕒 *Date period:* {period_start} → {period_end}\")\n    lines.append(f\"🧾 *Alerts:* {len(alerts)} pending\")\n    lines.append(\"\")\n\n    for source, items in grouped.items():\n        for row in items[:5]:\n            published_day = parse_day(row[\"published\"])\n            title = row[\"title_clean\"] or \"(no title)\"\n            url = row[\"url_canonical\"] or \"\"\n            score = row[\"relevance_score\"] or 0\n            sentiment = row[\"sentiment\"] or \"neutral\"\n            crisis_reason = row[\"crisis_reason\"] or \"\"\n            label = sentiment_label(sentiment, score, 1 if crisis_reason else 0)\n            highlight = highlight_title(title, crisis_reason)\n            lines.append(f\"📰 *Source:* `{source}`\")\n            lines.append(f\"📅 *Air date:* `{published_day}`\")\n            lines.append(f\"🔗 *Article:* [{highlight}]({url})\")\n            lines.append(f\"✨ *Fulbright highlight:* {highlight}\")\n            lines.append(f\"📊 *Sentiment / Brand health:* `{label}` | score `{score}`\")\n            if crisis_reason:\n                lines.append(f\"⚠️ *Discussion:* `{crisis_reason}`\")\n            lines.append(\"\")\n\n    return \"\\n\".join(lines).strip()\n\n\ndef dispatch_alerts():\n    alerts = get_pending_alerts()\n    if not alerts:\n        print(\"No pending alerts to dispatch.\")\n        return\n\n    target = load_telegram_target()\n    print(f\"Targeting: {target}\")\n    message = build_digest(alerts)\n    if not message:\n        print(\"No message built.\")\n        return\n\n    success, output = send_to_telegram(target, message)\n    conn = sqlite3.connect(DB_PATH)\n    cursor = conn.cursor()\n    if success:\n        now = datetime.datetime.now(datetime.timezone.utc).isoformat()\n        ids = [row[\"alert_id\"] for row in alerts]\n        cursor.executemany(\n            \"UPDATE alerts SET status = 'sent', sent_at = ?, channel = 'telegram' WHERE id = ?\",\n            [(now, alert_id) for alert_id in ids],\n        )\n        conn.commit()\n        conn.close()\n        print(f\"SENT digest covering {len(ids)} alerts\")\n        print(output)\n        return\n\n    now = datetime.datetime.now(datetime.timezone.utc).isoformat()\n    ids = [row[\"alert_id\"] for row in alerts]\n    cursor.executemany(\n        \"UPDATE alerts SET status = 'failed', sent_at = ? WHERE id = ?\",\n        [(now, alert_id) for alert_id in ids],\n    )\n    conn.commit()\n    conn.close()\n    print(f\"FAILED digest: {output}\")\n\n\nif __name__ == \"__main__\":\n    dispatch_alerts()\n"},"uuid":"1a6157cb-6fce-4c0d-ab03-b0e1ed7b9cd9","timestamp":"2026-07-14T15:11:29.962Z","permissionMode":"default","promptSource":"sdk","userType":"external","entrypoint":"sdk-cli","cwd":"/opt/ai-os/products/ceo","sessionId":"d0159b57-65bf-4ee3-8bf1-14b9ce55cb8d","version":"2.1.207","gitBranch":"main"}
{"parentUuid":"1a6157cb-6fce-4c0d-ab03-b0e1ed7b9cd9","isSidechain":false,"attachment":{"type":"agent_listing_delta","addedTypes":["claude","Explore","general-purpose","Plan","statusline-setup"],"addedLines":["- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)","- Explore: Fast read-only search agent for locating code. Use it to find files by pattern (eg. \"src/components/**/*.tsx\"), grep for symbols or keywords (eg. \"API endpoints\"), or answer \"where is X defined / which files reference Y.\" Do NOT use it for code review, design-doc auditing, cross-file consistency checks, or open-ended analysis — it reads excerpts rather than whole files and will miss content past its read window. When calling, specify search breadth: \"quick\" for a single targeted lookup, \"medium\" for moderate exploration, or \"very thorough\" to search across multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)","- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)","- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)","- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)"],"removedTypes":[],"isInitial":true,"showConcurrencyNote":true},"type":"attachment","uuid":"6b9ae189-dd09-4635-a304-21a0f7b429ad","timestamp":"2026-07-14T15:11:29.960Z","userType":"external","entrypoint":"sdk-cli","cwd":"/opt/ai-os/products/ceo","sessionId":"d0159b57-65bf-4ee3-8bf1-14b9ce55cb8d","version":"2.1.207","gitBranch":"main"}
{"parentUuid":"6b9ae189-dd09-4635-a304-21a0f7b429ad","isSidechain":false,"attachment":{"type":"skill_listing","content":"- deep-research: Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report. - When the user wants a deep, multi-source, fact-checked research report on any topic. BEFORE invoking, check if the question is specific enough to research directly — if underspecified (e.g., \"what car to buy\" without budget/use-case/region), ask 2-3 clarifying questions to narrow scope. Then pass the refined question as args, weaving the answers in.\n- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- verify: Verify that a code change actually does what it's supposed to by exercising it end-to-end and observing behavior — drive the affected flow, not just tests or typecheck. Run before committing nontrivial changes; bootstraps this repo's project verify skill if none exists yet. Don't invoke it on a diff that only touches tests, docs, or other code with no runtime surface to drive (a change to product source always has one) — there's nothing to observe.\n- code-review: Review the current diff for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings). Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo, defaults to 10m) - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- review: Review a GitHub pull request; for your working diff use /code-review\n- security-review: Complete a security review of the pending changes on the current branch","skillCount":14,"isInitial":true,"names":["deep-research","dataviz","update-config","keybindings-help","verify","code-review","simplify","fewer-permission-prompts","loop","claude-api","run","init","review","security-review"]},"type":"attachment","uuid":"de55d5fb-7fe6-4d54-8b18-548a9a1294e3","timestamp":"2026-07-14T15:11:29.960Z","userType":"external","entrypoint":"sdk-cli","cwd":"/opt/ai-os/products/ceo","sessionId":"d0159b57-65bf-4ee3-8bf1-14b9ce55cb8d","version":"2.1.207","gitBranch":"main"}
{"type":"last-prompt","lastPrompt":"Review these two Python files for logic bugs causing stale links, hub/search URLs, wrong positive/neutral classification, missing article date/highlight, and no Telegram notification after report gene…","leafUuid":"de55d5fb-7fe6-4d54-8b18-548a9a1294e3","sessionId":"d0159b57-65bf-4ee3-8bf1-14b9ce55cb8d"}
{"type":"ai-title","aiTitle":"Fix bug report generator logic","sessionId":"d0159b57-65bf-4ee3-8bf1-14b9ce55cb8d"}
{"parentUuid":"de55d5fb-7fe6-4d54-8b18-548a9a1294e3","isSidechain":false,"message":{"id":"msg_608bb6893a964e11866902d962faecb1","type":"message","role":"assistant","model":"claude-3-5-sonnet-20241022","content":[{"type":"text","text":"I have reviewed the provided files. Here are the identified issues and recommended patches.\n\n### Identified Issues\n1.  **`report_generator.py` (Stale/Wrong URLs):** The logic in `query_mentions` (lines 48-51) incorrectly filters out valid article links from Google RSS feeds by checking for `oc=5` and `hl=` parameters, which are common in legitimate article URLs.\n2.  **`report_generator.py` (Missing Article Date/Highlight):** The `_highlight` function (lines 80-90) is currently a placeholder that returns a static string if no brand terms are found, rather than extracting relevant snippets from the title or body.\n3.  **`alert_dispatcher.py` (No Telegram Notification):** The `dispatch_alerts` function (lines 105-135) is never called by the main execution flow of the report generation process, and the `build_digest` function uses hardcoded dates (lines 85-86) instead of dynamic period detection.\n\n### Recommended Patches\n\n#### 1. Fix URL Filtering in `report_generator.py`\nRemove the overly aggressive Google RSS filter:\n```python\n# Remove lines 48-51:\n# if 'google.com/rss/articles' in url and 'oc=5' in url and ('hl=' in url or 'ceid=' in url):\n#     if 'fulbright' not in title and 'fuv' not in title and 'fsppm' not in title:\n#         continue\n```\n\n#### 2. Improve Highlight Logic in `report_generator.py`\nUpdate `_highlight` to return the title itself if no specific brand terms are found, ensuring the report remains readable:\n```python\ndef _highlight(text, brand_terms=None):\n    # ... (keep existing setup)\n    if snippets:\n        return ', '.join(sorted(set(snippets), key=lambda x: x.lower()))\n    return text # Return original text instead of \"No direct Fulbright mention...\"\n```\n\n#### 3. Enable Dispatch in `alert_dispatcher.py`\nUpdate `build_digest` to accept dynamic dates and ensure `dispatch_alerts` is triggered after report generation:\n```python\n# In alert_dispatcher.py, update build_digest signature:\ndef build_digest(alerts, period_start, period_end):\n    # ... use passed arguments instead of hardcoded strings\n\n# In report_generator.py, add at the end of generate_report():\nfrom alert_dispatcher import dispatch_alerts\ndispatch_alerts()\n```\n\n**Next Steps:**\n- Apply the URL filter removal in `report_generator.py`.\n- Update `_highlight` to return the title as a fallback.\n- Import and call `dispatch_alerts()` within `generate_report()` to ensure notifications are sent."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":100,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":100,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"type":"assistant","uuid":"6e67a9aa-0337-4767-a754-ec78c8de5bec","timestamp":"2026-07-14T15:11:37.333Z","userType":"external","entrypoint":"sdk-cli","cwd":"/opt/ai-os/products/ceo","sessionId":"d0159b57-65bf-4ee3-8bf1-14b9ce55cb8d","version":"2.1.207","gitBranch":"main"}
{"type":"last-prompt","lastPrompt":"Review these two Python files for logic bugs causing stale links, hub/search URLs, wrong positive/neutral classification, missing article date/highlight, and no Telegram notification after report gene…","leafUuid":"6e67a9aa-0337-4767-a754-ec78c8de5bec","sessionId":"d0159b57-65bf-4ee3-8bf1-14b9ce55cb8d"}