chore(skill): 清理全部 23 条历史校验警告

- 11 个超 20KB 的 SKILL.md 拆分，大章节移入 references/details.md - 7 处非标准字段（arguments/user_invocable）移入 metadata - 修 req-dev 4 条相对链接；validate 跳过行内代码避免 ![](...) 误报 - validate-skills.py: 23 warns -> 0 warns，0 errors
2026-10-31 22:21:07 +08:00
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+# skills 详细内容
+
+## 常见对账场景
+
+### 场景1: 月末银行对账
+
+```python
+# 1. 加载数据
+bank_df = load_bank_statement('银行流水_202601.xlsx')
+system_df = load_system_records('收付款记录_202601.xlsx')
+
+# 2. 执行对账
+result = reconcile(bank_df, system_df)
+
+# 3. 查看汇总
+print(f"已匹配: {result['summary']['matched_count']} 笔")
+print(f"银行未达: {result['summary']['bank_only_count']} 笔, 金额 {result['summary']['bank_only_amount']:,.2f}")
+print(f"企业未达: {result['summary']['system_only_count']} 笔, 金额 {result['summary']['system_only_amount']:,.2f}")
+
+# 4. 生成调节表
+report = generate_bank_reconciliation_report(
+    bank_balance=150000.00,  # 银行对账单余额
+    book_balance=145000.00,  # 企业账面余额
+    bank_only=result['bank_only'],
+    system_only=result['system_only'],
+    as_of_date='2026-01-31'
+)
+print(report)
+
+# 5. 导出 Excel
+export_reconciliation_to_excel(result, '银行对账_202601.xlsx', '2026-01-31')
+```
+
+### 场景2: 客户对账
+
+```python
+# 加载客户交易明细
+ar_transactions = pd.read_excel('应收明细.xlsx')
+
+# 筛选特定客户
+customer_data = ar_transactions[ar_transactions['customer'] == '深圳XX公司']
+
+# 生成对账单
+statement = generate_ar_statement(
+    customer_name='深圳XX公司',
+    transactions=customer_data,
+    as_of_date='2026-01-31'
+)
+print(statement)
+```
+
+### 场景3: 账龄分析
+
+```python
+# 加载未收款数据
+receivables = pd.read_excel('应收账款.xlsx')
+
+# 账龄分析
+aging = aging_analysis(
+    receivables=receivables,
+    as_of_date='2026-01-31',
+    aging_buckets=[30, 60, 90, 180, 365]
+)
+print(aging)
+
+# 导出
+aging.to_excel('账龄分析_202601.xlsx')
+```
+
+### 场景4: 扫描单据OCR识别与对账匹配（诉讼证据核对）
+
+适用于：承运商/供应商提供扫描件作为诉讼证据，需要与我司对账单进行匹配核对。
+
+```python
+from pdf2image import convert_from_path
+import pytesseract
+import pandas as pd
+import re
+
+def ocr_pdf_extract_orders(pdf_path: str, dpi: int = 150) -> pd.DataFrame:
+    """
+    从 PDF 扫描件中 OCR 提取订单信息
+
+    Args:
+        pdf_path: PDF 文件路径
+        dpi: 图像分辨率，越高识别越准但速度越慢
+
+    Returns:
+        DataFrame 包含 order_no, page, tc_no 等字段
+    """
+    print(f"正在转换 PDF...")
+    images = convert_from_path(pdf_path, dpi=dpi)
+    print(f"共 {len(images)} 页，开始 OCR 识别...")
+
+    all_records = []
+    for i, img in enumerate(images, 1):
+        if i % 10 == 0:
+            print(f"  处理进度: {i}/{len(images)}")
+
+        text = pytesseract.image_to_string(img, lang='chi_sim+eng')
+
+        # 提取多种订单号格式（根据实际业务调整）
+        hm_orders = re.findall(r'(HM\d{12,15})(?:-\d+)?', text)      # 华住订单
+        hpo_orders = re.findall(r'(HPO-\d{8}-\d+)', text)            # 华住采购订单
+        fy_orders = re.findall(r'(FY\d{9,})', text)                  # 凤悦订单
+        phgco_orders = re.findall(r'(PHGCO\d{9,})', text)            # 锦江订单
+        tc_orders = re.findall(r'(TC\d{8,})', text)                  # 运单号
+
+        # 提取日期
+        dates = re.findall(r'(\d{4}-\d{2}-\d{2})', text)
+
+        all_orders = hm_orders + hpo_orders + fy_orders + phgco_orders
+        for order in all_orders:
+            all_records.append({
+                'order_no': order,
+                'page': i,
+                'tc_no': tc_orders[0] if tc_orders else None,
+                'date': dates[0] if dates else None,
+                'raw_text': text[:500]  # 保留部分原文用于核查
+            })
+
+    df = pd.DataFrame(all_records)
+    df = df.drop_duplicates(subset=['order_no'])
+    print(f"提取完成，共 {len(df)} 条唯一订单")
+    return df
+
+
+def extract_base_order_no(order_no: str) -> str:
+    """
+    提取基础订单号（移除后缀如 -1, -2）
+
+    例如: HM202505227320-1 -> HM202505227320
+    """
+    if pd.isna(order_no):
+        return None
+    order_str = str(order_no)
+    match = re.match(r'(HM\d{12,15}|HPO-\d{8}-\d+|FY\d{9,}|PHGCO\d{9,})', order_str)
+    return match.group(1) if match else order_str
+
+
+def reconcile_scanned_with_excel(
+    scanned_df: pd.DataFrame,
+    excel_path: str,
+    main_sheet: str,
+    pending_sheet: str = None,
+    order_column: str = '华住/锦江订单号'
+) -> dict:
+    """
+    扫描单据与 Excel 对账单匹配
+
+    Args:
+        scanned_df: OCR 提取的订单 DataFrame
+        excel_path: Excel 对账单路径
+        main_sheet: 主数据 Sheet 名称
+        pending_sheet: 待结算/次月结 Sheet 名称（可选）
+        order_column: 订单号列名
+
+    Returns:
+        {
+            'matched_main': 在主表中匹配的订单,
+            'matched_pending': 在待结算表中匹配的订单,
+            'not_found': 未找到的订单,
+            'summary': 汇总统计
+        }
+    """
+    # 加载主表
+    df_main = pd.read_excel(excel_path, sheet_name=main_sheet)
+    # 处理表头（如果第一行是表头）
+    if df_main.iloc[0].astype(str).str.contains(order_column).any():
+        df_main.columns = df_main.iloc[0]
+        df_main = df_main.iloc[1:].reset_index(drop=True)
+
+    # 提取基础订单号
+    main_orders = set(df_main[order_column].apply(extract_base_order_no).dropna())
+
+    # 加载待结算表（如有）
+    pending_orders = set()
+    df_pending = None
+    if pending_sheet:
+        try:
+            df_pending = pd.read_excel(excel_path, sheet_name=pending_sheet)
+            pending_orders = set(df_pending[order_column].apply(extract_base_order_no).dropna())
+        except:
+            pass
+
+    all_excel_orders = main_orders | pending_orders
+
+    # 扫描件订单号
+    scan_orders = set(scanned_df['order_no'].apply(extract_base_order_no).dropna())
+
+    # 匹配分析
+    matched_main = scan_orders & main_orders
+    matched_pending = scan_orders & pending_orders
+    not_found = scan_orders - all_excel_orders
+
+    # 汇总
+    summary = {
+        'scan_total': len(scan_orders),
+        'matched_main_count': len(matched_main),
+        'matched_pending_count': len(matched_pending),
+        'not_found_count': len(not_found),
+        'match_rate': (len(matched_main) + len(matched_pending)) / len(scan_orders) * 100 if scan_orders else 0
+    }
+
+    return {
+        'matched_main': matched_main,
+        'matched_pending': matched_pending,
+        'not_found': not_found,
+        'summary': summary,
+        'df_main': df_main,
+        'df_pending': df_pending
+    }
+
+
+def generate_litigation_reconciliation_report(
+    result: dict,
+    scanned_df: pd.DataFrame,
+    output_path: str
+):
+    """
+    生成诉讼对账分析报告
+    """
+    summary = result['summary']
+
+    with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
+        # Sheet1: 汇总
+        summary_df = pd.DataFrame({
+            '项目': [
+                '扫描件订单数',
+                '在主对账单中找到',
+                '在待结算表中找到',
+                '完全未找到',
+                '匹配率'
+            ],
+            '数值': [
+                summary['scan_total'],
+                summary['matched_main_count'],
+                summary['matched_pending_count'],
+                summary['not_found_count'],
+                f"{summary['match_rate']:.1f}%"
+            ]
+        })
+        summary_df.to_excel(writer, sheet_name='对账汇总', index=False)
+
+        # Sheet2: 未找到的订单
+        if result['not_found']:
+            not_found_df = scanned_df[
+                scanned_df['order_no'].apply(extract_base_order_no).isin(result['not_found'])
+            ][['order_no', 'page', 'tc_no', 'date']]
+            not_found_df.columns = ['订单号', 'PDF页码', '运单号', '日期']
+            not_found_df.to_excel(writer, sheet_name='未找到订单', index=False)
+
+        # Sheet3: 待结算订单详情
+        if result['matched_pending'] and result['df_pending'] is not None:
+            pending_detail = result['df_pending'][
+                result['df_pending']['华住/锦江订单号'].apply(extract_base_order_no).isin(result['matched_pending'])
+            ]
+            pending_detail.to_excel(writer, sheet_name='待结算订单', index=False)
+
+    print(f"报告已保存: {output_path}")
+```
+
+#### 使用示例
+
+```python
+# 1. 从 PDF 扫描件提取订单
+pdf_path = "承运商证据_6月业务.pdf"
+scanned_df = ocr_pdf_extract_orders(pdf_path, dpi=150)
+
+# 2. 与 Excel 对账单匹配
+result = reconcile_scanned_with_excel(
+    scanned_df=scanned_df,
+    excel_path="对账单_2025年6月.xlsx",
+    main_sheet="2025.6",
+    pending_sheet="次月结",
+    order_column="华住/锦江订单号"
+)
+
+# 3. 查看匹配结果
+print(f"匹配率: {result['summary']['match_rate']:.1f}%")
+print(f"未找到: {result['summary']['not_found_count']} 条")
+
+if result['not_found']:
+    print("\n未找到的订单:")
+    for order in result['not_found']:
+        print(f"  {order}")
+
+# 4. 生成报告
+generate_litigation_reconciliation_report(
+    result=result,
+    scanned_df=scanned_df,
+    output_path="诉讼对账分析报告.xlsx"
+)
+```
+
+#### OCR 识别注意事项
+
+1. **环境依赖**:
+   ```bash
+   # macOS
+   brew install tesseract tesseract-lang poppler
+   pip install pdf2image pytesseract pandas openpyxl
+   ```
+
+2. **识别精度**:
+   - DPI 150 适合快速扫描，DPI 200-250 适合精确识别
+   - 中文识别需要 `chi_sim` 语言包
+
+3. **订单号格式差异**:
+   - PDF 中可能是 `HM202505227320`
+   - Excel 中可能是 `HM202505227320-1`、`HM202505227320-2`（带后缀）
+   - 匹配时应使用基础订单号
+
+4. **常见问题**:
+   - 空白页（签收单背面）会被自动跳过
+   - OCR 可能误识别字符（如 5→S, 0→O），建议人工抽查
+   - 大 PDF 处理较慢，可先用低 DPI 预览
+
+5. **"次月结"/"待结算" 订单**:
+   - 这类订单通常标记为"没有报价"、"未建单"等
+   - 虽然在系统中有记录，但可能未计入正式结算金额
+   - 诉讼中需特别关注这类订单的状态
+
+6. **多承运商订单归属校验（重要风险点）**:
+   - 承运商可能将其他公司的订单混入证据，企图多收运费
+   - 必须检查PDF订单是否出现在其他承运商的对账单中
+   - 如果订单同时出现在多个承运商对账单中，则为异常订单
+
+#### 多承运商订单归属校验
+
+```python
+def check_order_ownership(
+    scanned_df: pd.DataFrame,
+    my_company_excel: str,
+    other_carrier_excels: list[str],
+    order_column: str = '华住/锦江订单号'
+) -> dict:
+    """
+    检查订单归属，识别是否有其他承运商的订单被混入
+
+    Args:
+        scanned_df: OCR 提取的订单 DataFrame
+        my_company_excel: 我司对账单路径
+        other_carrier_excels: 其他承运商对账单路径列表
+        order_column: 订单号列名
+
+    Returns:
+        {
+            'my_orders': 属于我司的订单,
+            'other_carrier_orders': 属于其他承运商的订单（异常）,
+            'disputed_orders': 同时出现在多处的争议订单,
+            'summary': 汇总
+        }
+    """
+    # 提取PDF订单
+    scan_orders = set(scanned_df['order_no'].apply(extract_base_order_no).dropna())
+
+    # 加载我司对账单订单
+    my_orders = set()
+    try:
+        xl = pd.ExcelFile(my_company_excel)
+        for sheet in xl.sheet_names:
+            df = pd.read_excel(my_company_excel, sheet_name=sheet)
+            for col in df.columns:
+                if '订单' in str(col):
+                    orders = df[col].apply(extract_base_order_no).dropna()
+                    my_orders.update(orders)
+    except Exception as e:
+        print(f"加载我司对账单失败: {e}")
+
+    # 加载其他承运商对账单订单
+    other_orders = {}  # {carrier_name: set of orders}
+    for excel_path in other_carrier_excels:
+        carrier_name = excel_path.split('/')[-1]
+        carrier_orders = set()
+        try:
+            xl = pd.ExcelFile(excel_path)
+            for sheet in xl.sheet_names:
+                df = pd.read_excel(excel_path, sheet_name=sheet)
+                for col in df.columns:
+                    if '订单' in str(col):
+                        orders = df[col].apply(extract_base_order_no).dropna()
+                        carrier_orders.update(orders)
+            other_orders[carrier_name] = carrier_orders
+        except:
+            pass
+
+    # 分析订单归属
+    all_other_orders = set()
+    for orders in other_orders.values():
+        all_other_orders.update(orders)
+
+    # 分类
+    only_mine = scan_orders & my_orders - all_other_orders  # 仅在我司
+    only_others = scan_orders & all_other_orders - my_orders  # 仅在其他承运商（异常！）
+    in_both = scan_orders & my_orders & all_other_orders  # 同时出现（争议）
+    nowhere = scan_orders - my_orders - all_other_orders  # 哪里都没有
+
+    # 识别异常订单属于哪个承运商
+    abnormal_details = []
+    for order in only_others:
+        for carrier, orders in other_orders.items():
+            if order in orders:
+                page = scanned_df[scanned_df['order_no'].apply(extract_base_order_no) == order]['page'].values
+                abnormal_details.append({
+                    'order_no': order,
+                    'belongs_to': carrier,
+                    'pdf_page': page[0] if len(page) > 0 else None
+                })
+
+    summary = {
+        'scan_total': len(scan_orders),
+        'my_orders_count': len(only_mine),
+        'other_carrier_count': len(only_others),  # 异常订单数
+        'disputed_count': len(in_both),
+        'not_found_count': len(nowhere)
+    }
+
+    return {
+        'my_orders': only_mine,
+        'other_carrier_orders': only_others,
+        'abnormal_details': abnormal_details,
+        'disputed_orders': in_both,
+        'not_found': nowhere,
+        'summary': summary
+    }
+```
+
+#### 使用示例（多承运商校验）
+
+```python
+# 检查订单归属
+result = check_order_ownership(
+    scanned_df=scanned_df,
+    my_company_excel="智慧云彩对账单_2025年6月.xlsx",
+    other_carrier_excels=[
+        "友利速运对账单_2025年6月.xlsx",
+        "其他承运商对账单.xlsx"
+    ]
+)
+
+# 检查是否有异常订单
+if result['summary']['other_carrier_count'] > 0:
+    print(f"⚠️ 发现 {result['summary']['other_carrier_count']} 个异常订单！")
+    print("这些订单属于其他承运商，不应由我司支付：")
+    for item in result['abnormal_details']:
+        print(f"  {item['order_no']} -> 属于 {item['belongs_to']} (PDF第{item['pdf_page']}页)")
+else:
+    print("✓ 未发现异常订单，所有订单归属正确")
+```
+
+---
+