feat: 阅读周报 + 工作日志(日报/周报) 报告生成脚本

analyzers/daily_work_report.py

@@ -0,0 +1,155 @@
+"""Daily work report generator - extracts work-related Memos and generates a daily work log."""
+
+import logging
+import os
+import sys
+from datetime import datetime, timezone, timedelta
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from tools.config import get_output_dir
+from tools.memos_client import MemosClient
+from tools.config import load_secrets
+
+logger = logging.getLogger(__name__)
+
+TZ_BEIJING = timezone(timedelta(hours=8))
+
+# Tags that indicate work-related memos
+WORK_TAGS = {"工作", "会议", "项目", "出差", "培训", "汇报", "材料", "接待", "调研"}
+
+
+def is_work_memo(tags):
+    """Check if a memo's tags indicate work content."""
+    work_tags = set(tags) & WORK_TAGS
+    return len(work_tags) > 0
+
+
+def generate_daily_work_report(target_date=None):
+    """Generate a daily work report from Memos.
+
+    Args:
+        target_date: datetime.date or None (defaults to today in Beijing)
+    """
+    if target_date is None:
+        target_date = datetime.now(TZ_BEIJING).date()
+    else:
+        if hasattr(target_date, 'date'):
+            target_date = target_date.date()
+    
+    date_str = target_date.strftime("%Y-%m-%d")
+    logger.info(f"Generating daily work report for {date_str}")
+
+    # Connect to Memos
+    secrets = load_secrets()
+    client = MemosClient(secrets["memos_url"], secrets["memos_token"])
+
+    # Fetch today's memos
+    start_dt = datetime.combine(target_date, datetime.min.time(), tzinfo=TZ_BEIJING)
+    end_dt = datetime.combine(target_date, datetime.max.time(), tzinfo=TZ_BEIJING)
+    memos = client.list_all_memos_from_range(start_dt, end_dt)
+
+    # Filter work-related
+    work_memos = [m for m in memos if is_work_memo(m.get("tags", []))]
+
+    logger.info(f"Total memos: {len(memos)}, work-related: {len(work_memos)}")
+
+    if not work_memos:
+        return _write_empty_report(date_str)
+
+    # Build report
+    report = _build_report(date_str, work_memos)
+    _write_report(date_str, report)
+
+    return date_str
+
+
+def _build_report(date_str, work_memos):
+    """Build markdown report from work memos."""
+    lines = [
+        f"# 工作日志 · {date_str}",
+        "",
+        f"> 共 {len(work_memos)} 条工作记录 · 数据来源：Memos",
+        "",
+    ]
+
+    # Group by tag
+    tag_groups = {}
+    for m in work_memos:
+        for tag in m.get("tags", []):
+            if tag in WORK_TAGS:
+                if tag not in tag_groups:
+                    tag_groups[tag] = []
+                tag_groups[tag].append(m)
+
+    # Summary
+    lines.append("## 概览")
+    lines.append("")
+    for tag, items in sorted(tag_groups.items(), key=lambda x: -len(x[1])):
+        lines.append(f"- **{tag}**：{len(items)} 条")
+    lines.append("")
+
+    # Detailed entries
+    lines.append("## 详情")
+    lines.append("")
+
+    for m in work_memos:
+        content = m.get("content", "").strip()
+        tags = m.get("tags", [])
+        work_tags = [t for t in tags if t in WORK_TAGS]
+        tag_str = " · ".join(f"#{t}" for t in work_tags)
+
+        # Remove tag markers from display content for cleaner reading
+        # Keep the raw content but show tags separately
+        display_content = content
+
+        created = m.get("created_at", "")
+        try:
+            dt = datetime.fromisoformat(created.replace("Z", "+00:00"))
+            time_str = dt.astimezone(TZ_BEIJING).strftime("%H:%M")
+        except (ValueError, AttributeError):
+            time_str = ""
+
+        lines.append(f"### {time_str}  {tag_str}")
+        lines.append("")
+        lines.append(display_content)
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def _write_report(date_str, report_text):
+    """Write report to ai-insights/daily/YYYY-MM-DD/work/"""
+    output_root = get_output_dir("daily")
+    work_dir = os.path.join(output_root, date_str, "work")
+    os.makedirs(work_dir, exist_ok=True)
+
+    filepath = os.path.join(work_dir, f"工作日志_{date_str}.md")
+    with open(filepath, "w", encoding="utf-8") as f:
+        f.write(report_text)
+
+    logger.info(f"Work report saved to {filepath}")
+
+
+def _write_empty_report(date_str):
+    """Write a minimal report when no work memos found."""
+    report = f"# 工作日志 · {date_str}\n\n> 今日无工作相关 Memos\n"
+    _write_report(date_str, report)
+    return date_str
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--date", type=str, help="Target date YYYY-MM-DD, defaults to today")
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+
+    if args.date:
+        target = datetime.strptime(args.date, "%Y-%m-%d").date()
+    else:
+        target = None
+
+    generate_daily_work_report(target)
+    print("DONE")

analyzers/weekly_reading_report.py

@@ -0,0 +1,328 @@
+"""Weekly reading report synthesizer - reads daily reading reports and generates weekly analysis."""
+
+import logging
+import os
+import re
+import sys
+from datetime import datetime, timezone, timedelta
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from tools.config import load_secrets
+from tools.llm import DeepSeekClient
+
+logger = logging.getLogger(__name__)
+
+TZ_BEIJING = timezone(timedelta(hours=8))
+PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+# Reading reports live in weread-notes repo, sibling to inspiration-collector
+WEREAD_DIR = os.path.join(os.path.dirname(PROJECT_DIR), "weread-notes")
+
+READING_WEEKLY_SYSTEM_PROMPT = """你是一个私人阅读分析师。你的任务是阅读用户过去一周的每日阅读报告，生成一篇有深度的周度阅读分析。
+
+## 核心原则
+
+1. **统计是基础，分析是核心**：先给出本周阅读的量化概览，然后深入分析阅读行为背后的认知模式。
+
+2. **追踪阅读主题演变**：本周读了哪些书？它们之间有什么关联？阅读焦点在周初和周尾有什么变化？
+
+3. **识别认知跃迁**：本周有没有哪本书/哪个概念让用户的思考发生了质变？在哪一天？是什么触发的？
+
+4. **评估阅读质量**：不是读了多久、划了多少线，而是划线背后反映了什么思考模式？是精读还是泛读？是验证已有认知还是拓展新领域？
+
+5. **建议下周方向**：基于本周的阅读轨迹，下周建议继续深耕哪本书？或者是否需要补充某个缺失的领域？
+
+## 输出格式
+
+纯 Markdown，不要代码块包裹。
+
+### 1. 本周统计
+
+用表格呈现：
+| 维度 | 数值 |
+|:---|:---|
+| 阅读天数 | X/7 |
+| 累计阅读时长 | X小时Y分钟 |
+| 日均阅读时长 | X分钟 |
+| 本周在读书籍 | X本 |
+| 本周新增划线 | X条 |
+| 本周新增想法 | X条 |
+| 最投入的书 | 《XXX》· X小时 |
+| 划线最多的书 | 《XXX》· X条 |
+
+### 2. 阅读主题追踪
+
+本周的阅读覆盖了哪些主题领域（如投资、哲学、历史、技术……）？每个主题在读哪些书？占了多少时间？哪些主题之间有交叉？
+
+### 3. 深入分析
+
+按主题组织，融会贯通地写：
+- 本周阅读的主线是什么（不是逐日罗列，是提炼共同指向）
+- 认知跃迁的关键时刻（哪天的什么划线让你意识到用户在想什么）
+- 跨书关联（本周读的几本书之间有没有隐含的对话）
+- 阅读节奏评价（是持续深耕一本书，还是多线并行？深度如何？）
+- 本周最值得注意的一个阅读习惯（比如：划线密度突然变化、某个时段的阅读特别集中）
+
+### 4. 本周金句
+
+从本周的划线和批注中，选出 3-5 条最具代表性的原文或用户批注。每条注明来自哪本书、哪一天。
+
+### 5. 下周阅读建议
+
+基于本周轨迹，给出 1-3 条具体的阅读方向建议。要写到"继续读哪本书的哪个章节"或"补充哪类书"的颗粒度。
+
+---
+
+## 补充说明
+
+- 如果某天没有阅读报告（用户当天没读书），在统计中注明，但分析中跳过。
+- 统计数字必须从日报原文中提取，不要编造。
+- 分析要有观点，不要求和稀泥。如果用户本周阅读质量明显下降，直接指出。
+- 篇幅不设上限，深度优先。"""
+
+
+def extract_stats_from_daily(daily_text, date_str):
+    """Extract reading statistics from a daily report.
+    
+    Returns dict with: date, books, highlights_count, notes_count, 
+    reading_time_minutes, book_list
+    """
+    stats = {
+        "date": date_str,
+        "has_data": False,
+        "reading_time_minutes": 0,
+        "highlights_count": 0,
+        "notes_count": 0,
+        "books": [],
+    }
+    
+    if not daily_text or len(daily_text.strip()) < 50:
+        return stats
+    
+    # Check if there was any reading today
+    if "今日没有阅读记录" in daily_text or "暂无阅读" in daily_text:
+        return stats
+    
+    stats["has_data"] = True
+    
+    # Extract reading time - look for patterns like "X小时Y分钟", "X分钟"
+    time_patterns = [
+        r'(\d+)\s*小时\s*(\d+)\s*分钟',
+        r'(\d+)\s*小时',
+        r'(\d+)\s*分钟',
+    ]
+    for pattern in time_patterns:
+        match = re.search(pattern, daily_text)
+        if match:
+            if len(match.groups()) == 2:
+                stats["reading_time_minutes"] = int(match.group(1)) * 60 + int(match.group(2))
+            elif "小时" in pattern:
+                stats["reading_time_minutes"] = int(match.group(1)) * 60
+            else:
+                stats["reading_time_minutes"] = int(match.group(1))
+            break
+    
+    # Extract highlight/note counts - look for patterns
+    highlight_match = re.search(r'划线.*?(\d+)\s*条', daily_text)
+    if highlight_match:
+        stats["highlights_count"] = int(highlight_match.group(1))
+    
+    notes_match = re.search(r'想法.*?(\d+)\s*条', daily_text)
+    if notes_match:
+        stats["notes_count"] = int(notes_match.group(1))
+    
+    # Extract book titles - look for 《书名》 patterns
+    book_titles = re.findall(r'《([^》]+)》', daily_text)
+    # De-duplicate while preserving order
+    seen = set()
+    unique_books = []
+    for title in book_titles:
+        if title not in seen and len(title) > 1:
+            seen.add(title)
+            unique_books.append(title)
+    stats["books"] = unique_books[:10]  # Top 10 to avoid noise
+    
+    return stats
+
+
+def read_week_daily_reports(week_start, week_end):
+    """Read all daily reading reports from week_start to week_end.
+    
+    Args:
+        week_start, week_end: datetime objects in Beijing time
+    
+    Returns:
+        tuple of (accumulated_stats, full_texts)
+    """
+    daily_dir = os.path.join(WEREAD_DIR, "daily")
+    all_stats = []
+    all_texts = []
+    
+    current = week_start
+    while current <= week_end:
+        date_str = current.strftime("%Y-%m-%d")
+        # Daily reports are named: 每日阅读_YYYY-MM-DD.md
+        filename = f"每日阅读_{date_str}.md"
+        filepath = os.path.join(daily_dir, filename)
+        
+        if os.path.exists(filepath):
+            with open(filepath, "r", encoding="utf-8") as f:
+                text = f.read()
+            stats = extract_stats_from_daily(text, date_str)
+            all_stats.append(stats)
+            all_texts.append((date_str, text))
+            logger.info(f"  ✓ {date_str}: {len(text)} chars, {len(stats['books'])} books")
+        else:
+            all_stats.append({"date": date_str, "has_data": False, "books": []})
+            logger.info(f"  ✗ {date_str}: no report")
+        
+        current += timedelta(days=1)
+    
+    return all_stats, all_texts
+
+
+def aggregate_weekly_stats(all_stats):
+    """Aggregate daily stats into weekly totals."""
+    total = {
+        "reading_days": 0,
+        "total_minutes": 0,
+        "total_highlights": 0,
+        "total_notes": 0,
+        "all_books": [],
+        "daily_detail": [],
+    }
+    
+    seen_books = set()
+    for s in all_stats:
+        if s.get("has_data"):
+            total["reading_days"] += 1
+            total["total_minutes"] += s.get("reading_time_minutes", 0)
+            total["total_highlights"] += s.get("highlights_count", 0)
+            total["total_notes"] += s.get("notes_count", 0)
+            for book in s.get("books", []):
+                if book not in seen_books:
+                    seen_books.add(book)
+                    total["all_books"].append(book)
+        
+        total["daily_detail"].append({
+            "date": s["date"],
+            "has_data": s.get("has_data", False),
+            "minutes": s.get("reading_time_minutes", 0),
+            "books": s.get("books", []),
+        })
+    
+    return total
+
+
+def build_stats_table(weekly_stats):
+    """Build the statistics markdown table."""
+    avg_minutes = weekly_stats["total_minutes"] // max(weekly_stats["reading_days"], 1)
+    hours = weekly_stats["total_minutes"] // 60
+    minutes = weekly_stats["total_minutes"] % 60
+    avg_h = avg_minutes // 60
+    avg_m = avg_minutes % 60
+    
+    # Find most engaged book by looking at daily detail
+    book_mentions = {}
+    for d in weekly_stats["daily_detail"]:
+        for book in d.get("books", []):
+            book_mentions[book] = book_mentions.get(book, 0) + 1
+    top_book = max(book_mentions, key=book_mentions.get) if book_mentions else "—"
+    
+    return f"""| 维度 | 数值 |
+|:---|:---|
+| 阅读天数 | {weekly_stats['reading_days']}/7 |
+| 累计阅读时长 | {hours}小时{minutes}分钟 |
+| 日均阅读时长 | {avg_h}小时{avg_m}分钟 |
+| 本周在读书籍 | {len(weekly_stats['all_books'])}本 |
+| 本周新增划线 | {weekly_stats['total_highlights']}条 |
+| 本周新增想法 | {weekly_stats['total_notes']}条 |
+| 最常出现的书 | 《{top_book}》|"""
+
+
+def generate_weekly_report():
+    """Main entry: generate weekly reading report."""
+    # Determine week range (past 7 days, ending yesterday)
+    now = datetime.now(TZ_BEIJING)
+    yesterday = now - timedelta(days=1)
+    week_end = yesterday.replace(hour=23, minute=59, second=59)
+    week_start = (week_end - timedelta(days=6)).replace(hour=0, minute=0, second=0)
+    
+    logger.info(f"Week range: {week_start.strftime('%Y-%m-%d')} → {week_end.strftime('%Y-%m-%d')}")
+    
+    # Step 1: Read all daily reports
+    all_stats, all_texts = read_week_daily_reports(week_start, week_end)
+    
+    if not all_texts:
+        logger.error("No daily reading reports found this week")
+        return None
+    
+    # Step 2: Aggregate statistics
+    weekly_stats = aggregate_weekly_stats(all_stats)
+    stats_md = build_stats_table(weekly_stats)
+    
+    # Step 3: Build context for LLM
+    context_parts = []
+    context_parts.append(f"## 本周阅读统计\n\n{stats_md}\n")
+    
+    context_parts.append("## 每日阅读报告原文\n")
+    for date_str, text in all_texts:
+        # Trim each daily report - keep the key analysis sections, skip repetitive formatting
+        context_parts.append(f"### {date_str}\n")
+        # Limit each day to ~3000 chars to avoid token overflow
+        context_parts.append(text[:3500])
+        context_parts.append("\n---\n")
+    
+    full_context = "\n".join(context_parts)
+    
+    # Step 4: Call DeepSeek API
+    secrets = load_secrets()
+    client = DeepSeekClient(secrets["deepseek_api_key"])
+    
+    prompt = READING_WEEKLY_SYSTEM_PROMPT
+    logger.info(f"Sending to DeepSeek... ({len(full_context)} chars)")
+    
+    result = client.chat(
+        system_prompt=prompt,
+        user_message=full_context,
+        temperature=0.5,
+    )
+    
+    # Step 5: Assemble final report
+    year = week_start.strftime("%Y")
+    week_num = week_start.isocalendar()[1]
+    week_start_str = week_start.strftime("%m%d")
+    week_end_str = week_end.strftime("%m%d")
+    
+    filename = f"每周阅读_W{week_num}_{week_start_str}-{week_end_str}.md"
+    
+    header = f"""# 每周阅读报告 · {week_start.strftime('%Y.%m.%d')} — {week_end.strftime('%Y.%m.%d')}
+
+> 本文基于 {weekly_stats['reading_days']} 天阅读数据生成 · 依托 DeepSeek API 分析
+
+"""
+    
+    final_report = header + result
+    
+    # Step 6: Write and return
+    output_dir = os.path.join(WEREAD_DIR, "weekly")
+    os.makedirs(output_dir, exist_ok=True)
+    output_path = os.path.join(output_dir, filename)
+    
+    with open(output_path, "w", encoding="utf-8") as f:
+        f.write(final_report)
+    
+    logger.info(f"Report saved to {output_path}")
+    logger.info(f"Stats: {weekly_stats['reading_days']} days, {weekly_stats['total_minutes']}min, {len(weekly_stats['all_books'])} books")
+    
+    return output_path
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+    path = generate_weekly_report()
+    if path:
+        print(f"DONE: {path}")
+    else:
+        print("FAILED: No report generated")
+        sys.exit(1)

analyzers/weekly_work_report.py

@@ -0,0 +1,209 @@
+"""Weekly work report synthesizer - reads daily work logs and generates weekly analysis."""
+
+import logging
+import os
+import sys
+from datetime import datetime, timezone, timedelta
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from tools.config import load_secrets, get_output_dir
+from tools.llm import DeepSeekClient
+
+logger = logging.getLogger(__name__)
+
+TZ_BEIJING = timezone(timedelta(hours=8))
+PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+WORK_WEEKLY_SYSTEM_PROMPT = """你是一个私人工作日志分析师。你的任务是阅读用户过去一周的每日工作日志，生成一篇有深度的周度工作分析。
+
+## 核心原则
+
+1. **概述先行**：给出本周工作的整体面貌——主要精力投向了哪些领域、总体节奏如何。
+
+2. **按主题组织，不按时间罗列**：不要"周一做了X、周二做了Y"。要提炼跨日的工作主线——哪些项目贯穿了整周？哪些任务是单日完成的？
+
+3. **识别模式和趋势**：本周的工作节奏有什么特点？是否出现了新的工作类型？哪些工作在重复发生？是否有一些事在全周的工作日志中反复出现但没有被解决？
+
+4. **产出导向**：本周有什么可量化的产出？写了什么材料？参加了什么会议？完成了什么节点？
+
+5. **前瞻建议**：基于本周工作日志，下周需要关注什么？有什么待办被遗漏了？
+
+## 输出格式
+
+纯 Markdown，不要代码块包裹。
+
+### 1. 本周工作概览
+
+一段话概括本周工作的底色——主要在忙什么、节奏如何、核心成果。
+
+### 2. 工作统计
+
+| 维度 | 数值 |
+|:---|:---|
+| 有工作记录的天数 | X/7 |
+| 工作 Memos 总数 | X 条 |
+| 涉及标签 | #工作 #会议 ... |
+| 单日最高工作量 | 周X · X条 |
+
+### 3. 深入分析
+
+按主题组织：
+- 持续性的工作主线（跨日出现的项目/主题）
+- 本周重要节点（会议、截止日期、关键决策）
+- 工作节奏评价（是否有某天特别忙？是否有连续高压？）
+- 新出现的工作类型或关注点
+- 被遗漏或被推迟的事项
+
+### 4. 下周关注
+
+基于本周工作轨迹，列出下周需要重点关注的事项。具体到事项+时间节点。
+
+---
+
+注意：
+- 如果某天没有工作日志，在统计中注明，但分析中跳过。
+- 分析要有观点——不只是"做了什么"，更要回答"这意味着什么"和"接下来该做什么"。
+- 篇幅不设上限。"""
+
+
+def read_week_work_logs(week_start, week_end):
+    """Read all daily work logs from week_start to week_end."""
+    all_texts = []
+    daily_root = get_output_dir("daily")
+    
+    current = week_start
+    while current <= week_end:
+        date_str = current.strftime("%Y-%m-%d")
+        work_file = os.path.join(daily_root, date_str, "work", f"工作日志_{date_str}.md")
+        
+        if os.path.exists(work_file):
+            with open(work_file, "r", encoding="utf-8") as f:
+                text = f.read()
+            all_texts.append((date_str, text))
+            logger.info(f"  ✓ {date_str}: {len(text)} chars")
+        else:
+            logger.info(f"  ✗ {date_str}: no work log")
+        
+        current += timedelta(days=1)
+    
+    return all_texts
+
+
+def extract_weekly_work_stats(all_texts):
+    """Extract statistics from daily work logs."""
+    total_memos = 0
+    all_tags = set()
+    highest_day = ("", 0)
+    days_with_data = 0
+    
+    for date_str, text in all_texts:
+        if "今日无工作" in text:
+            continue
+        days_with_data += 1
+        
+        # Count memos (each ### header with content)
+        import re
+        memo_count = len(re.findall(r'^###\s+\d{2}:\d{2}', text, re.MULTILINE))
+        total_memos += memo_count
+        
+        if memo_count > highest_day[1]:
+            highest_day = (date_str, memo_count)
+        
+        # Extract tags
+        tag_matches = re.findall(r'#(\w+)', text)
+        work_tags = {"工作", "会议", "项目", "出差", "培训", "汇报", "材料", "接待", "调研"}
+        for tag in tag_matches:
+            if tag in work_tags:
+                all_tags.add(tag)
+    
+    return {
+        "days_with_data": days_with_data,
+        "total_memos": total_memos,
+        "top_day": highest_day,
+        "tags": sorted(all_tags),
+    }
+
+
+def generate_weekly_work_report():
+    """Main entry: generate weekly work report."""
+    now = datetime.now(TZ_BEIJING)
+    yesterday = now - timedelta(days=1)
+    week_end = yesterday.replace(hour=23, minute=59, second=59)
+    week_start = (week_end - timedelta(days=6)).replace(hour=0, minute=0, second=0)
+    
+    logger.info(f"Week range: {week_start.strftime('%Y-%m-%d')} → {week_end.strftime('%Y-%m-%d')}")
+    
+    # Step 1: Read daily work logs
+    all_texts = read_week_work_logs(week_start, week_end)
+    
+    if not all_texts:
+        logger.warning("No work logs found this week")
+        return None
+    
+    # Step 2: Extract stats
+    stats = extract_weekly_work_stats(all_texts)
+    
+    # Step 3: Build context
+    context_parts = []
+    context_parts.append(f"## 本周工作统计\n")
+    context_parts.append(f"- 有工作记录的天数：{stats['days_with_data']}/7")
+    context_parts.append(f"- 工作 Memos 总数：{stats['total_memos']} 条")
+    context_parts.append(f"- 涉及标签：{' '.join('#' + t for t in stats['tags'])}")
+    if stats['top_day'][0]:
+        context_parts.append(f"- 单日最高：{stats['top_day'][0]} · {stats['top_day'][1]}条")
+    context_parts.append("")
+    
+    context_parts.append("## 每日工作日志原文\n")
+    for date_str, text in all_texts:
+        context_parts.append(f"### {date_str}\n")
+        context_parts.append(text[:3500])  # Trim each day
+        context_parts.append("\n---\n")
+    
+    full_context = "\n".join(context_parts)
+    
+    # Step 4: Call DeepSeek API
+    secrets = load_secrets()
+    client = DeepSeekClient(secrets["deepseek_api_key"])
+    
+    logger.info(f"Sending to DeepSeek... ({len(full_context)} chars)")
+    result = client.chat(
+        system_prompt=WORK_WEEKLY_SYSTEM_PROMPT,
+        user_message=full_context,
+        temperature=0.5,
+    )
+    
+    # Step 5: Assemble and write
+    year = week_start.strftime("%Y")
+    week_num = week_start.isocalendar()[1]
+    week_start_str = week_start.strftime("%m%d")
+    week_end_str = week_end.strftime("%m%d")
+    filename = f"工作周报_W{week_num}_{week_start_str}-{week_end_str}.md"
+    
+    header = f"""# 工作周报 · {week_start.strftime('%Y.%m.%d')} — {week_end.strftime('%Y.%m.%d')}
+
+> 基于 {stats['days_with_data']}/7 天工作日志生成 · 依托 DeepSeek API 分析
+
+"""
+    
+    final_report = header + result
+    
+    output_dir = os.path.join(get_output_dir("weekly"), "work")
+    os.makedirs(output_dir, exist_ok=True)
+    output_path = os.path.join(output_dir, filename)
+    
+    with open(output_path, "w", encoding="utf-8") as f:
+        f.write(final_report)
+    
+    logger.info(f"Report saved to {output_path}")
+    return output_path
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+    path = generate_weekly_work_report()
+    if path:
+        print(f"DONE: {path}")
+    else:
+        print("FAILED: No report generated")
+        sys.exit(1)

scripts/run_daily_work_report.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+# Daily work report generator
+# Reads today's work-related Memos → generates daily work log
+# Run: daily, alongside the daily digest (22:00 Beijing = 14:00 UTC)
+
+set -e
+
+INSP_DIR="/home/ubuntu/inspiration-collector"
+LOG_FILE="/tmp/daily_work_report_$(date +%Y%m%d).log"
+
+echo "[$(date)] Starting daily work report..." | tee "$LOG_FILE"
+
+cd "$INSP_DIR"
+git pull origin main 2>&1 | tee -a "$LOG_FILE"
+
+echo "[$(date)] Running analyzer..." | tee -a "$LOG_FILE"
+python3 analyzers/daily_work_report.py 2>&1 | tee -a "$LOG_FILE"
+
+echo "[$(date)] Committing and pushing..." | tee -a "$LOG_FILE"
+git add ai-insights/daily/ 2>/dev/null || true
+
+if git diff --cached --quiet; then
+    echo "[$(date)] No changes" | tee -a "$LOG_FILE"
+else
+    git commit -m "daily: work report $(date +%Y-%m-%d)" 2>&1 | tee -a "$LOG_FILE"
+    git push origin main 2>&1 | tee -a "$LOG_FILE"
+fi
+
+echo "[$(date)] Done." | tee -a "$LOG_FILE"

scripts/run_weekly_reading_report.sh

@@ -0,0 +1,43 @@
+#!/bin/bash
+# Weekly reading report generator
+# Reads 7 daily reading reports → DeepSeek API → weekly analysis
+# Run: weekly, Sunday 16:30 Beijing time (08:30 UTC)
+#
+# NOTE: Script lives in inspiration-collector/scripts/ but output 
+# goes to weread-notes/weekly/. Both repos are siblings on the server.
+
+set -e
+
+INSP_DIR="/home/ubuntu/inspiration-collector"
+WEREAD_DIR="/home/ubuntu/weread-notes"
+LOG_FILE="/tmp/weekly_reading_report_$(date +%Y%m%d_%H%M).log"
+
+echo "[$(date)] Starting weekly reading report..." | tee "$LOG_FILE"
+
+# Pull latest in both repos
+echo "[$(date)] Pulling weread-notes..." | tee -a "$LOG_FILE"
+cd "$WEREAD_DIR"
+git pull origin main 2>&1 | tee -a "$LOG_FILE"
+
+echo "[$(date)] Pulling inspiration-collector..." | tee -a "$LOG_FILE"
+cd "$INSP_DIR"
+git pull origin main 2>&1 | tee -a "$LOG_FILE"
+
+# Run the analyzer (it reads from weread-notes/daily/ and writes to weread-notes/weekly/)
+echo "[$(date)] Running analyzer..." | tee -a "$LOG_FILE"
+cd "$INSP_DIR"
+python3 analyzers/weekly_reading_report.py 2>&1 | tee -a "$LOG_FILE"
+
+# Commit and push weread-notes
+echo "[$(date)] Committing weread-notes..." | tee -a "$LOG_FILE"
+cd "$WEREAD_DIR"
+git add weekly/ 2>/dev/null || true
+
+if git diff --cached --quiet; then
+    echo "[$(date)] No changes to commit in weread-notes" | tee -a "$LOG_FILE"
+else
+    git commit -m "weekly: reading report $(date +%Y-%m-%d)" 2>&1 | tee -a "$LOG_FILE"
+    git push origin main 2>&1 | tee -a "$LOG_FILE"
+fi
+
+echo "[$(date)] Done." | tee -a "$LOG_FILE"

scripts/run_weekly_work_report.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+# Weekly work report generator
+# Reads 7 daily work logs → DeepSeek API → weekly work analysis
+# Run: weekly, Sunday 17:00 Beijing time (09:00 UTC)
+
+set -e
+
+INSP_DIR="/home/ubuntu/inspiration-collector"
+LOG_FILE="/tmp/weekly_work_report_$(date +%Y%m%d_%H%M).log"
+
+echo "[$(date)] Starting weekly work report..." | tee "$LOG_FILE"
+
+cd "$INSP_DIR"
+git pull origin main 2>&1 | tee -a "$LOG_FILE"
+
+echo "[$(date)] Running analyzer..." | tee -a "$LOG_FILE"
+python3 analyzers/weekly_work_report.py 2>&1 | tee -a "$LOG_FILE"
+
+echo "[$(date)] Committing and pushing..." | tee -a "$LOG_FILE"
+git add ai-insights/weekly/ 2>/dev/null || true
+
+if git diff --cached --quiet; then
+    echo "[$(date)] No changes" | tee -a "$LOG_FILE"
+else
+    git commit -m "weekly: work report $(date +%Y-%m-%d)" 2>&1 | tee -a "$LOG_FILE"
+    git push origin main 2>&1 | tee -a "$LOG_FILE"
+fi
+
+echo "[$(date)] Done." | tee -a "$LOG_FILE"