diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/__init__.py b/backend/crawler_copy/jd_pc_search/competitor_report/__init__.py
deleted file mode 100644
index 96c4459..0000000
--- a/backend/crawler_copy/jd_pc_search/competitor_report/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""竞品报告脚本共享：表头常量、CSV 辅助、报告调参解析。"""
diff --git a/backend/crawler_copy/jd_pc_search/jd_competitor_report.py b/backend/crawler_copy/jd_pc_search/jd_competitor_report.py
index 6b30677..c6b6d6f 100644
--- a/backend/crawler_copy/jd_pc_search/jd_competitor_report.py
+++ b/backend/crawler_copy/jd_pc_search/jd_competitor_report.py
@@ -1,1387 +1,17 @@
 # -*- coding: utf-8 -*-
 """
-关键词 → 调用 ``jd_keyword_pipeline`` 全链路采集 → 生成 **标准化竞品分析报告**（Markdown）。
+兼容入口（历史路径）：竞品报告实现已迁至 ``pipeline.jd_competitor_report``。
 
-报告结构对齐常见竞品分析框架：研究范围与方法、执行摘要、**整体市场观察（列表可见度参考）**、
-市场与竞争结构、**按细分类目分组的竞品对比矩阵**、价格分析（含规则化价差/活动信号与可选 **细类价盘·促销** 大模型归纳）、**按细分类目的消费者反馈与用户画像**、**策略与机会提示**（以大模型归纳为主，可选）与附录；并明确数据边界。
-若运行配置中提供了外部市场规模摘录（``EXTERNAL_MARKET_TABLE_ROWS``），则追加对应表格小节；否则不输出占位行。
-
-依赖：全量抓取时与 ``jd_keyword_pipeline.py`` 相同（Node、h5st、Playwright、``common/jd_cookie.txt``）。
-**仅复用已有目录生成报告时**不需要跑浏览器，只需该目录下已有 CSV / ``run_meta.json``。
-
-用法：
-
-- **重新抓取并出报告**：``EXISTING_RUN_DIR = None``，配置 ``KEYWORD``（及可选 ``OVERRIDE_*``），执行 ``python jd_competitor_report.py``。
-- **只分析已有批次**：将 ``EXISTING_RUN_DIR`` 设为 ``pipeline_runs/<时间戳>_<关键词>/`` 的绝对或相对路径（相对当前工作目录），
-  再执行同一命令；**不重新抓取**。关键词优先用本文件 ``KEYWORD``，否则读 ``run_meta.json`` 的 ``keyword``，再否则从目录名
-  ``YYYYMMDD_HHMMSS_<词>`` 推断。
-
-流水线其余参数（评论分页、延迟等）仍在 ``jd_keyword_pipeline.py`` 顶部配置。
-
-输出：在对应运行目录下覆盖写入 ``competitor_analysis.md``。
+- 推荐：在 ``backend`` 目录执行 ``python -m pipeline.jd_competitor_report``
+- 本文件：将 ``backend`` 加入 ``sys.path`` 后转发至 ``pipeline`` 模块，便于仍在爬虫目录下执行 ``python jd_competitor_report.py`` 的旧习惯。
 """
-
 from __future__ import annotations
 
-import hashlib
-import json
-import math
-import random
-import re
 import sys
-from collections import Counter
 from pathlib import Path
-from typing import Any
 
-_ROOT = Path(__file__).resolve().parent
-if str(_ROOT) not in sys.path:
-    sys.path.insert(0, str(_ROOT))
+_backend = Path(__file__).resolve().parents[2]
+if str(_backend) not in sys.path:
+    sys.path.insert(0, str(_backend))
 
-_BACKEND_ROOT = Path(__file__).resolve().parents[2]
-if str(_BACKEND_ROOT) not in sys.path:
-    sys.path.insert(0, str(_BACKEND_ROOT))
-
-import jd_keyword_pipeline as kpl  # noqa: E402
-from pipeline.csv_schema import (  # noqa: E402
-    COMMENT_CSV_COLUMNS,
-    JD_SEARCH_CSV_HEADERS,
-    MERGED_FIELD_TO_CSV_HEADER,
-    merged_csv_effective_total_sales,
-)
-
-from competitor_report.config import *  # noqa: F403
-from competitor_report.constants import *  # noqa: F403
-from competitor_report.csv_io import *  # noqa: F403
-from competitor_report.price_promo import (  # noqa: E402
-    _analyze_price_promotions,
-    _markdown_price_promotion_section,
-)
-from competitor_report.comment_sentiment import (  # noqa: E402
-    build_comment_sentiment_llm_payload,
-    _comment_keyword_hits,
-    _comment_sentiment_lexicon,
-    _iter_comment_text_units,
-    _iter_comment_text_units_and_scores,
-    _merge_comment_previews,
-    _parse_comment_score,
-)
-from competitor_report.llm_group_payloads import (  # noqa: E402
-    build_comment_groups_llm_payload,
-    build_matrix_groups_llm_payload,
-    build_price_groups_llm_payload,
-    build_promo_groups_llm_payload,
-    build_scenario_groups_llm_payload,
-    _comment_scenario_counts,
-    _group_keyword_hits,
-    _text_hits_scenario_triggers,
-)
-from competitor_report.matrix_group import (  # noqa: E402
-    _category_mix,
-    _competitor_matrix_group_key,
-    _merged_rows_grouped_for_matrix,
-)
-from competitor_report.price_stats import _price_stats_extended  # noqa: E402
-from competitor_report.consumer_feedback import (  # noqa: E402
-    _comment_lines_with_product_context,
-    _consumer_feedback_by_matrix_group,
-    _sku_to_matrix_group_map,
-)
-from competitor_report.list_mix import (  # noqa: E402
-    _brand_cr,
-    _counter_mix_top_rows_with_remainder,
-    _search_list_proxies,
-    _structure_brands,
-    _structure_names_for_pie_counter,
-    _structure_shops,
-)
-from competitor_report.matrix_md import (  # noqa: E402
-    _competitor_matrix_md_line,
-    _matrix_ingredients_cell,
-)
-from competitor_report.report_md_helpers import (  # noqa: E402
-    _embed_chart,
-    _focus_scenario_combo_bar_filename,
-    _lines_4_reading_brand,
-    _lines_4_reading_shop,
-    _matrix_prices_sales_chart_filename,
-    _mermaid_pie_focus_keywords,
-    _scenario_group_asset_slug,
-    _scenario_summary_bullets,
-    _strategy_hints,
-)
-from competitor_report.run_context import (  # noqa: E402
-    _infer_keyword,
-    _pc_search_result_count_from_raw,
-    _resolve_existing_run_dir,
-    _run_batch_label,
-)
-
-# ---------------------------------------------------------------------------
-# 运行配置（按需改这里；与 competitor_report.constants 中默认关注词等配合使用）
-# ---------------------------------------------------------------------------
-# KEYWORD：京东 PC 搜索词；全量抓取时必填。「仅已有目录」模式下可留空，改从 run_meta / 目录名推断。
-KEYWORD = "低GI"
-# 已有流水线目录（含 keyword_pipeline_merged.csv 等）时设为路径则**不重新抓取**，只生成 competitor_analysis.md。
-EXISTING_RUN_DIR = None
-# EXISTING_RUN_DIR = r"data\JD\pipeline_runs\20260408_144606_低GI"  # 相对数据根或绝对路径
-# 以下非 None 时仅本次运行临时覆盖 jd_keyword_pipeline 中同名变量（不改 pipeline 文件）
-OVERRIDE_MAX_SKUS: int | None = None
-OVERRIDE_PAGE_START: int | None = None
-OVERRIDE_PAGE_TO: int | None = None
-
-
-def build_competitor_markdown(
-    *,
-    run_dir: Path,
-    keyword: str,
-    merged_rows: list[dict[str, str]],
-    search_export_rows: list[dict[str, str]],
-    comment_rows: list[dict[str, str]],
-    meta: dict[str, Any] | None,
-    report_config: dict[str, Any] | None = None,
-    llm_sentiment_section_md: str | None = None,
-    llm_matrix_section_md: str | None = None,
-    llm_price_groups_section_md: str | None = None,
-    llm_promo_groups_section_md: str | None = None,
-    llm_scenario_groups_section_md: str | None = None,
-    llm_comment_groups_section_md: str | None = None,
-    llm_strategy_opportunities_section_md: str | None = None,
-    chapter8_text_mining_probe_section_md: str | None = None,
-) -> str:
-    focus_words, scenario_groups, external_rows = resolve_report_tuning(report_config)
-    _ch8_probe_sec = (chapter8_text_mining_probe_section_md or "").strip()
-    sku_header = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
-    title_h = MERGED_FIELD_TO_CSV_HEADER["title"]
-    batch = _run_batch_label(run_dir)
-    n_sku = len(merged_rows)
-    n_cmt = len(comment_rows)
-    n_sku_pathed = sum(1 for r in merged_rows if _detail_category_path_cell(r))
-    n_sku_matrix = sum(1 for r in merged_rows if _competitor_matrix_group_key(r))
-
-    list_export = len(search_export_rows) > 0
-    structure_rows = search_export_rows if list_export else merged_rows
-    n_structure = len(structure_rows)
-    shops_s = _structure_shops(structure_rows, list_export=list_export)
-    brands_s = _structure_brands(structure_rows, list_export=list_export)
-    shops_for_cr = _structure_names_for_pie_counter(shops_s)
-    brands_for_cr = _structure_names_for_pie_counter(brands_s)
-    cr1_shop, cr3_shop, top_shop_s, _ = _brand_cr(shops_for_cr)
-    cr1_list_brand, cr3_list_brand, top_list_brand, _ = _brand_cr(brands_for_cr)
-    # §4.3 类目分布：深入合并表（与 §5 竞品矩阵同一细类划分，非搜索列表行）
-    cm_structure = _category_mix(merged_rows, top_k=12)
-    min_brand_rows = max(5, int(0.02 * n_structure)) if n_structure else 5
-
-    brands_deep = [
-        _cell(r, MERGED_FIELD_TO_CSV_HEADER["detail_brand"], "detail_brand")
-        for r in merged_rows
-        if _cell(r, MERGED_FIELD_TO_CSV_HEADER["detail_brand"], "detail_brand")
-    ]
-    cr1_deep, cr3_deep, top_brand_deep, _top_share_deep = _brand_cr(brands_deep)
-    cr1_hints = (
-        cr1_shop if list_export and cr1_shop is not None else cr1_deep
-    )
-
-    pst_merged = _price_stats_extended(_collect_prices(merged_rows))
-    pst_list = (
-        _price_stats_extended(_collect_prices(search_export_rows))
-        if list_export
-        else {}
-    )
-    # 价格分析（§2 要点、§6、策略提示）：优先「列表全量」；无列表或无解析价时再用合并表深入样本
-    pst = (
-        pst_list
-        if list_export and pst_list.get("n", 0) > 0
-        else pst_merged
-    )
-    price_analysis_basis_cn = (
-        f"PC 搜索列表导出共 **{len(search_export_rows)}** 行中的展示价（标价/券后等）"
-        if list_export and pst_list.get("n", 0) > 0
-        else f"已深入抓取的 **{n_sku}** 个 SKU 合并数据中的展示价"
-    )
-    promo_rows = (
-        search_export_rows
-        if list_export and pst_list.get("n", 0) > 0
-        else merged_rows
-    )
-    promo_sig = _analyze_price_promotions(promo_rows)
-
-    hits = _comment_keyword_hits(comment_rows, focus_words)
-    if not hits:
-        blob = _merge_comment_previews(merged_rows)
-        for w in focus_words:
-            if len(w) < 2:
-                continue
-            n = blob.count(w)
-            if n:
-                hits[w] += n
-
-    comment_texts, comment_scores = _iter_comment_text_units_and_scores(
-        comment_rows, merged_rows
-    )
-    sentiment_lex = _comment_sentiment_lexicon(comment_texts, comment_scores)
-    scen_counts, scen_n_texts = _comment_scenario_counts(
-        comment_texts, scenario_groups
-    )
-
-    feedback_groups = _consumer_feedback_by_matrix_group(
-        merged_rows=merged_rows,
-        comment_rows=comment_rows,
-        sku_header=sku_header,
-    )
-    matrix_groups_for_exec = _merged_rows_grouped_for_matrix(merged_rows)
-    multi_feedback_cat = len(matrix_groups_for_exec) >= 2
-
-    (
-        api_rc,
-        api_list_kw,
-        api_rc_uniques,
-        api_raw_json_n,
-        api_rc_n_values,
-    ) = _pc_search_result_count_from_raw(run_dir)
-
-    has_external_market = bool(external_rows)
-
-    lines: list[str] = [
-        f"# 竞品分析报告（京东 PC 渠道）",
-        "",
-        f"> **监测主题**：{keyword}  ",
-        f"> **数据批次**：{batch}  ",
-        f"> **报告生成**：自动化草稿，**仅供内部研讨**，不构成市场承诺或投资建议。",
-        "",
-        "---",
-        "",
-        "## 一、研究范围、数据来源与局限",
-        "",
-        "### 1.1 研究范围",
-        "",
-        f"- **搜索关键词**：「{keyword}」",
-        f"- **分析对象**：本次采集流程选取的 **{n_sku}** 个 SKU（搜索排序靠前子样本，非全站普查）。",
-    ]
-    if n_sku:
-        n_sku_nop = n_sku - n_sku_pathed
-        n_sku_unparsed = n_sku_pathed - n_sku_matrix
-        lines.append(
-            f"- **细类分析范围**：**{n_sku_matrix}** 个 SKU 具备参与**第五至第八章**分析所需的**商品详情页类目路径**"
-            f"（且能读出常见细类名称，如饼干、挂面等）；另有 **{n_sku_nop}** 个商品缺少该信息、"
-            f"**{n_sku_unparsed}** 个虽有路径但读不出细类名称，**未纳入**细类矩阵与按细类的评价统计。"
-        )
-    if meta:
-        lines.append(
-            f"- **搜索列表页**：逻辑第 **{meta.get('page_start')}** 页至第 **{meta.get('page_to')}** 页；"
-            f"搜索导出共 **{meta.get('pc_search_export_rows', '—')}** 行（含未深入拉详情的商品）。"
-        )
-    lines.extend(
-        [
-            "",
-            "### 1.2 数据来源",
-            "",
-            "- **渠道**：京东 PC 端公开商品列表、商详与评价等可访问数据。",
-            "- **可追溯**：原始表格与接口响应保存在本批次任务输出目录，供内部复核；对外分享请脱敏。",
-            "",
-            "### 1.3 方法说明（指标含义）",
-            "",
-            "- **价格**：自页面「标价 / 券后价 / 详情价」等抽取的**展示价**，含促销与规格差异，**不等于**出厂价或成本。**第六章** 在具备可用的搜索列表导出时，优先以**列表全量**统计；否则使用**已深入 SKU** 的合并数据；**第六章第一节** 归纳标价与券后价差等**列表侧展示价差信号**（不对卖点/腰带字段做预设关键词扫描）。",
-            "- **品牌/店铺集中度（第四章）**：有列表全量时按列表行计店铺与品牌占比；无列表导出时按深入 SKU 合并表估算。",
-            "- **评价主题词**：对评价正文做**预设词表子串计数**，非分词主题模型，适合扫方向，**需抽样人工验证**。",
-            "- **用途/场景**：对每条评价独立判断是否命中预设场景词；一条可计入多个场景，统计的是「提及该场景的评价条数」而非用户数。",
-            (
-                "- **用户画像（第八章）**：正负面粗判含**口语短语**级摘录；**第八章第三节**另用分词、词频与主题模型等对评论做**补充分析**（与**第八章第二节**条形图口径不同、互为补充），可选词云并由大模型归纳要点。"
-                if _ch8_probe_sec
-                else "- **用户画像（第八章）**：正负面粗判含**口语短语**级摘录；关注词与场景**仅按细类**以**同图左右并列**展示（左为关注词命中次数，右为场景占有效文本 **%**）；见**第八章第三节**。"
-            ),
-            "- **细类划分（第五至第八章）**：**仅**依据合并表中的**商品详情页类目路径**；该信息缺失或无法读出细类名称的 SKU **不参与**竞品矩阵与按细类评价统计（相关评价条亦**不进入**按细类图表）。",
-            "- **检索结果规模**：来自京东 PC 搜索返回的「结果条数」类指标，表示平台侧申报的匹配数量级，**不等于**动销、库存或独立 SKU 数。",
-            "",
-            "### 1.4 主要局限",
-            "",
-            "- 仅覆盖 **京东 PC**，不含天猫、抖音、线下、B2B 原料端。",
-            "- 样本量由本次抓取上限与搜索页数决定，**结论外推需谨慎**。",
-            "- 详情配料与宣称以页面展示为准，**与真实配方可能不一致**（合规与实测另议）。",
-            (
-                "- **行业零售额、TAM、CAGR 等**：无法从本批次数据推导；本报告已纳入任务中配置的第三方摘录，见 **第三章第五节**。"
-                if has_external_market
-                else "- **行业零售额、TAM、CAGR 等**：无法从本批次数据推导；本报告未纳入外部摘录（可在任务报告调参中维护市场信息表）。"
-            ),
-            "",
-            "---",
-            "",
-            "## 二、执行摘要（要点）",
-            "",
-        ]
-    )
-
-    exec_bullets: list[str] = []
-    exec_bullets.append(
-        f"在关键词「{keyword}」下，本次深入分析 **{n_sku}** 个 SKU，关联评价文本 **{n_cmt}** 条。"
-    )
-    if list_export and cr1_shop is not None and top_shop_s:
-        src = f"列表全量 **{n_structure}** 行"
-        if cr3_shop is not None:
-            exec_bullets.append(
-                f"竞争结构（{src}，第四章）：**店铺** 第一大店铺份额 ≈ **{100 * cr1_shop:.1f}%**（「{top_shop_s}」），"
-                f"前三店铺合计份额 ≈ **{100 * cr3_shop:.1f}%**（按列表行计，同一 SKU 多行会重复计）。"
-            )
-        else:
-            exec_bullets.append(
-                f"竞争结构（{src}，第四章）：**店铺** 第一大店铺份额 ≈ **{100 * cr1_shop:.1f}%**（「{top_shop_s}」）。"
-            )
-    elif not list_export and cr1_deep is not None and top_brand_deep:
-        if cr3_deep is not None:
-            exec_bullets.append(
-                f"竞争结构（无列表导出，第四章用深入合并表）：**品牌** 第一大品牌份额 ≈ **{100 * cr1_deep:.1f}%**（「{top_brand_deep}」），"
-                f"前三品牌合计份额 ≈ **{100 * cr3_deep:.1f}%**。"
-            )
-        else:
-            exec_bullets.append(
-                f"竞争结构（无列表导出，第四章用深入合并表）：**品牌** 第一大品牌份额 ≈ **{100 * cr1_deep:.1f}%**（「{top_brand_deep}」）。"
-            )
-    if (
-        list_export
-        and len(brands_for_cr) >= min_brand_rows
-        and cr1_list_brand is not None
-        and top_list_brand
-    ):
-        if cr3_list_brand is not None:
-            exec_bullets.append(
-                f"同批列表中**品牌信息有效** **{len(brands_for_cr)}** 条：**品牌** 第一大品牌份额 ≈ **{100 * cr1_list_brand:.1f}%**（「{top_list_brand}」），"
-                f"前三品牌合计份额 ≈ **{100 * cr3_list_brand:.1f}%**。"
-            )
-        else:
-            exec_bullets.append(
-                f"同批列表中**品牌信息有效** **{len(brands_for_cr)}** 条：**品牌** 第一大品牌份额 ≈ **{100 * cr1_list_brand:.1f}%**（「{top_list_brand}」）。"
-            )
-    elif list_export and cr1_deep is not None and top_brand_deep and not brands_for_cr:
-        exec_bullets.append(
-            f"列表导出缺少品牌标题字段，**深入 {n_sku} SKU** 商详品牌第一大品牌份额 ≈ **{100 * cr1_deep:.1f}%**（「{top_brand_deep}」），供与第五章矩阵对照。"
-        )
-    if pst:
-        price_src_short = (
-            "（列表全量）"
-            if list_export and pst_list.get("n", 0) > 0
-            else "（深入样本）"
-        )
-        exec_bullets.append(
-            f"展示价格{price_src_short}：可解析价格 **{pst['n']}** 个观测，区间约 **{pst['min']:.2f}～{pst['max']:.2f}** 元，"
-            f"中位数 **{pst.get('median', pst['mean']):.2f}** 元。"
-        )
-        wb = int(promo_sig.get("rows_with_both_list_and_coupon") or 0)
-        sh = promo_sig.get("share_coupon_below_list_when_both")
-        med = promo_sig.get("median_discount_pct_when_coupon_below")
-        if wb >= 3 and isinstance(sh, (int, float)) and sh >= 0.08 and med is not None:
-            exec_bullets.append(
-                f"列表侧约 **{100.0 * float(sh):.0f}%** 可对齐行呈现「券后/到手」**低于**「标价」，展示价差中位数约 **{float(med):.1f}%**（**第六章第一节** 活动与话术摘录）。"
-            )
-    if multi_feedback_cat and (hits or scen_n_texts > 0):
-        exec_bullets.append(
-            "评价侧写（关注词、用途/场景）已按**第五章同一细类划分**分节，见**第八章第三节**（同图并列）。"
-        )
-    elif hits:
-        top3 = "、".join(f"「{w}」({n})" for w, n in hits.most_common(3))
-        exec_bullets.append(f"评价侧写（词频）：{top3}。")
-    if scen_n_texts > 0 and scen_counts and not multi_feedback_cat:
-        top_s = scen_counts.most_common(4)
-        frag = "；".join(f"{lbl} **{n}** 条" for lbl, n in top_s)
-        exec_bullets.append(f"用途/场景（评价自述，可多选）：{frag}（有效文本 **{scen_n_texts}** 条）。")
-    if api_rc is not None:
-        exec_bullets.append(
-            f"PC 搜索返回的检索结果规模约 **{api_rc:,}**（站内匹配条数量级，见第三章第二节；**不是**零售额或动销统计）。"
-        )
-    for b in exec_bullets:
-        lines.append(f"- {b}")
-    if not exec_bullets:
-        lines.append("- 当前批次可汇总要点较少（以正文各节实际输出为准）。")
-
-    proxy = _search_list_proxies(search_export_rows) if search_export_rows else {}
-    lines.extend(["", "---", "", "## 三、整体市场观察（渠道可见度参考，非官方市场规模）", ""])
-    lines.extend(
-        [
-            "### 3.1 与「市场规模」的区别",
-            "",
-            "- **官方/行业市场规模**（如全国零售额、品类增速、渗透率）通常来自 **Euromonitor、行业协会、上市公司年报、券商研报** 等；**不能**用京东搜索返回条数或 SKU 数直接等同。",
-            "- **第三章第二节** 使用搜索接口返回的**结果条数**；**第三章第三、四节** 描述本次导出的列表行、去重 SKU/店铺及列表价，仅作**参照**，外推全市场需谨慎。",
-            "",
-            "### 3.2 接口返回的检索规模",
-            "",
-        ]
-    )
-    if api_rc is not None:
-        lines.append(
-            f"- 根据本批次保存的搜索原始响应解析：监测词「**{keyword}**」下，平台申报的检索匹配规模约 **{api_rc:,}**。"
-        )
-        if api_list_kw:
-            lines.append(
-                f"- 同批响应中的列表关键词：**{api_list_kw}**（可与监测词对照是否一致）。"
-            )
-        if len(api_rc_uniques) > 1:
-            nums = "、".join(f"{u:,}" for u in api_rc_uniques)
-            lines.append(
-                f"- 注：多份原始响应中该规模字段曾出现不同取值（{nums}），正文取**众数** **{api_rc:,}**（共 {api_rc_n_values} 次有效读取）。"
-            )
-        elif api_raw_json_n > 0:
-            lines.append(
-                f"- 已扫描 **{api_raw_json_n}** 份原始响应并完成读取。"
-            )
-        lines.extend(
-            [
-                "- **含义**：平台对该关键词给出的**检索匹配条数量级**，用于感受站内商品池「宽度」；可能含不同类目/规格条目，**不等于**独立 SKU 数、动销或 GMV，且会随索引与运营策略变化。",
-                "",
-            ]
-        )
-    else:
-        lines.append(
-            "*未能从本批次搜索原始响应中解析到有效的检索规模字段（目录缺失、无可用响应或字段为空）。*"
-        )
-        lines.append("")
-
-    lines.extend(["### 3.3 搜索列表规模（本次抓取范围内的可见 SKU / 店铺）", ""])
-    if proxy.get("total_rows", 0) > 0:
-        pmin, pmax = proxy.get("page_span") or (None, None)
-        span_txt = (
-            f"页码（去重）约 **{pmin}～{pmax}** 页"
-            if pmin is not None and pmax is not None
-            else "页码字段缺失或无法解析"
-        )
-        lines.extend(
-            [
-                f"- **列表导出行数**：**{proxy['total_rows']}** 行。",
-                f"- **去重 SKU 数**：**{proxy['unique_skus']}**；**去重店铺数**：**{proxy['unique_shops']}**；{span_txt}。",
-                f"- **列表中去重叶子类目代码/片段数**（粗略）：**{proxy['unique_leaf_cats']}**（同一关键词下品类宽度的参考）。",
-                "",
-            ]
-        )
-        lpst = proxy.get("list_price_stats") or {}
-        lines.extend(["### 3.4 列表端展示价（全导出，非仅深入样本）", ""])
-        if lpst:
-            lines.extend(
-                [
-                    f"- 自列表「标价 / 券后价」解析到 **{lpst['n']}** 个数值价；"
-                    f"区间约 **{lpst['min']:.2f}～{lpst['max']:.2f}** 元，"
-                    f"中位数 **{float(lpst.get('median', lpst['mean'])):.2f}** 元。",
-                    "- **说明**：第六章价格统计表已与上表同源（均为列表全量，条件满足时）；若正文第六章标注为合并表样本，则因无可用列表价而退化。深入 SKU 的详情价可与列表价对照。",
-                    "",
-                ]
-            )
-        else:
-            lines.append("*列表导出中未能解析出数值价格。*")
-            lines.append("")
-    else:
-        lines.append(
-            "*未读到可用的搜索列表导出或文件为空；第三章第三、四节无列表侧数据。*"
-        )
-        lines.append("")
-        lines.extend(["### 3.4 列表端展示价（全导出）", "", "*无列表数据。*", ""])
-
-    if external_rows:
-        lines.extend(
-            [
-                "### 3.5 外部市场规模与行业信息（运行配置摘录）",
-                "",
-                "以下为本次任务报告调参中维护的**第三方市场摘录**，可与第三章第二节检索规模及第三章第三、四节列表参照对照使用；**指标含义与真实性以原出处为准**。",
-                "",
-                "| 指标 | 数值与说明 | 来源 | 年份 |",
-                "| --- | --- | --- | --- |",
-            ]
-        )
-        for a, b, c, d in external_rows:
-            lines.append(
-                f"| {_md_cell(a, 40)} | {_md_cell(b, 48)} | {_md_cell(c, 36)} | {_md_cell(d, 12)} |"
-            )
-        lines.append("")
-
-    ch4_heading = (
-        "## 四、市场与竞争结构（PC 搜索列表全量）"
-        if list_export
-        else "## 四、市场与竞争结构（深入合并表 · 无列表导出）"
-    )
-    lines.extend(["", "---", "", ch4_heading, ""])
-    if list_export:
-        lines.append(
-            f"基于**搜索列表导出**共 **{n_structure}** 行，与第三章第三节一致；"
-            f"集中度按**列表行**计数（同一 SKU 多次曝光则重复计）。"
-        )
-    else:
-        lines.append(
-            f"*未读到可用列表全量行，以下退化为**深入 SKU 合并样本** **{n_structure}** 行。*"
-        )
-    lines.append("")
-
-    lines.extend(["### 4.1 品牌分布与集中度", ""])
-    brand_rows_n = len(brands_for_cr)
-    show_list_brand_cr = list_export and brand_rows_n >= min_brand_rows
-    show_merged_brand_cr = not list_export and brand_rows_n > 0
-    if (show_list_brand_cr or show_merged_brand_cr) and cr1_list_brand is not None:
-        lines.extend(
-            _embed_chart(
-                run_dir,
-                "chart_brand_rows_pie.png",
-                "品牌列表曝光占比（扇形图；按整理后的品牌名计数，与结构化摘要中的品牌占比统计一致；"
-                "长尾并入「（其余品牌）」；扇形内再合并为「其他」）",
-            )
-        )
-        lines.extend(
-            _lines_4_reading_brand(
-                cr1=cr1_list_brand,
-                cr3=cr3_list_brand,
-                top=top_list_brand or "",
-                brand_rows_n=brand_rows_n,
-                n_structure=n_structure,
-            )
-        )
-        lines.append(
-            "*更细的品牌行数分布见本任务「结构化摘要」数据包。*"
-        )
-    elif list_export:
-        lines.append(
-            f"*列表导出中店铺/品牌标题有效 **{brand_rows_n}** 条，"
-            f"低于建议阈值（≥{min_brand_rows}），品牌集中度未展开。**店铺结构见第四章第二节**；"
-            f"商详品牌在**第五章**。*"
-        )
-    else:
-        lines.append("*深入子样本无可用品牌字段。*")
-    lines.append("")
-
-    lines.extend(["### 4.2 店铺分布与集中度", ""])
-    shop_rows_n = len(shops_for_cr)
-    if shop_rows_n:
-        lines.extend(
-            _embed_chart(
-                run_dir,
-                "chart_shop_rows_pie.png",
-                "店铺列表曝光占比（扇形图；按整理后的店铺名计数，与结构化摘要中的店铺占比统计一致；"
-                "长尾并入「（其余店铺）」；扇形内再合并为「其他」）",
-            )
-        )
-        lines.extend(
-            _lines_4_reading_shop(
-                cr1=cr1_shop,
-                cr3=cr3_shop,
-                top=top_shop_s or "",
-                shop_rows_n=shop_rows_n,
-                n_structure=n_structure,
-            )
-        )
-        lines.append(
-            "*更细的店铺行数分布见本任务「结构化摘要」数据包。*"
-        )
-    else:
-        lines.append("*无店铺字段。*")
-    lines.append("")
-
-    lines.extend(["### 4.3 细分类目分布（深入合并表 · 与第五章矩阵同一细类划分）", ""])
-    if cm_structure and n_sku_matrix > 0:
-        lines.extend(
-            _embed_chart(
-                run_dir,
-                "chart_category_mix_pie.png",
-                "细类标签分布（扇形图；依据合并表中的商品详情页类目路径，与第五章一致；"
-                "Top 12 以外的细类在统计时并入「（其余细类）」；扇形图内再合并为「其他」）",
-            )
-        )
-        lines.append(
-            "*完整类目分布见界面「数据摘要」或简报包中的数据文件。*"
-        )
-    else:
-        lines.append(
-            "*深入合并表中无具备可解析商品详情页类目路径的 SKU，本小节不展示扇形图；请核对商详抓取与合并字段。*"
-        )
-    lines.append("")
-
-    lines.extend(
-        [
-            "---",
-            "",
-            "## 五、竞品对比矩阵（按细分类目分组）",
-            "",
-            "分组**仅**依据合并表中的**商品详情页类目路径**（京东商详中的类目层级）：**三级路径**取中间一段（如 … > **饼干** > 粗粮饼干），"
-            "**四级及以上**取倒数第二段（如 … > **面条** > 挂面）。**路径缺失**或各段均为内部编码、**读不出常见细类名称**的 SKU **不进入**本矩阵，亦**不参与**第八章按细类的评价统计。",
-            "",
-            "**读图方式**：每个细类下为**并列横向条形图**（左：**展示价**（元）；右：**销量**（来自搜索列表页「已售」等销量文案，如「已售50万+」计为 **50 万**）），"
-            "纵轴为**产品标题**（与本节各附图一致）。**SKU、店铺、配料与评价摘要等明细不列入正文**，详见本批次导出的合并数据表。",
-            "",
-        ]
-    )
-    grouped_matrix = _merged_rows_grouped_for_matrix(merged_rows)
-    if not grouped_matrix:
-        if merged_rows:
-            lines.append(
-                "*深入合并表有条目，但均无可用商品详情页类目路径（或路径无法解析为可读细类），故无法生成细类矩阵；"
-                "第五至第八章中依赖矩阵的按细类统计相应为空。请核对商详抓取与合并字段。*"
-            )
-        else:
-            lines.append("*无合并表 SKU。*")
-        lines.append("")
-    for gi, (gname, grows) in enumerate(grouped_matrix):
-        lines.append(f"### {gname}（**{len(grows)}** 款）")
-        lines.append("")
-        mx_chart = _matrix_prices_sales_chart_filename(gname, gi)
-        lines.extend(
-            _embed_chart(
-                run_dir,
-                mx_chart,
-                f"「{_md_cell(gname, 20)}」· 展示价与销量（页面「已售」销量文案）；纵轴为产品标题。",
-            )
-        )
-        if not (run_dir / "report_assets" / mx_chart).is_file():
-            lines.append(
-                f"*（尚未生成 ``report_assets/{mx_chart}``：请确认已执行报告出图流程，或重新生成报告。）*"
-            )
-            lines.append("")
-        lines.append("")
-
-    _llm_mx = (llm_matrix_section_md or "").strip()
-    if _llm_mx:
-        lines.extend(
-            [
-                "",
-                "#### 细类要点归纳（大模型，与上文条形图互补）",
-                "",
-                "> **说明**：与第五章相同的细类划分下归纳卖点与配料共性；**具体 SKU、价格与条形图以正文为准**，SKU 级明细见合并表 CSV。",
-                "",
-                _llm_mx,
-                "",
-            ]
-        )
-
-    ch6_price_title = (
-        "## 六、价格分析（PC 搜索列表全量）"
-        if list_export and pst_list.get("n", 0) > 0
-        else "## 六、价格分析（深入 SKU 合并表 · 无可用列表价或未导出列表）"
-    )
-    lines.extend(["---", "", ch6_price_title, ""])
-    lines.append(f"- **统计基础**：{price_analysis_basis_cn}。")
-    if (
-        list_export
-        and pst_list.get("n", 0) > 0
-        and pst_merged.get("n", 0) > 0
-    ):
-        lines.append(
-            f"- **对照**：合并表深入样本可解析价 **{pst_merged['n']}** 个观测，中位数约 **{float(pst_merged.get('median', pst_merged['mean'])):.2f}** 元（与上表样本范围不同，仅作对照）。"
-        )
-    lines.append("")
-    if pst:
-        price_tbl = [
-            "| 统计量 | 数值（元） | 说明 |",
-            "| --- | --- | --- |",
-            f"| 样本量 | {pst['n']} | 与统计基础一致 |",
-            f"| 最小值 | {pst['min']:.2f} | |",
-        ]
-        if "q1" in pst:
-            price_tbl.append(f"| 下四分位 Q1 | {float(pst['q1']):.2f} | |")
-        else:
-            price_tbl.append("| 下四分位 Q1 | — | 样本不足 4 个 |")
-        price_tbl.append(
-            f"| 中位数 | {float(pst.get('median', pst['mean'])):.2f} | |"
-        )
-        if "q3" in pst:
-            price_tbl.append(f"| 上四分位 Q3 | {float(pst['q3']):.2f} | |")
-        else:
-            price_tbl.append("| 上四分位 Q3 | — | 样本不足 4 个 |")
-        price_tbl.extend(
-            [
-                f"| 最大值 | {pst['max']:.2f} | |",
-                f"| 均值 | {pst['mean']:.2f} | |",
-            ]
-        )
-        if "stdev" in pst:
-            price_tbl.append(f"| 标准差 | {pst['stdev']:.2f} | 离散程度 |")
-        lines.extend(price_tbl)
-        lines.append("")
-        lines.append(
-            "**解读提示**：价差大通常反映规格、组合装、品牌溢价或促销差异；B 端定价策略需结合成本与渠道单独建模。"
-        )
-        lines.append("")
-        lines.extend(_markdown_price_promotion_section(promo_sig))
-    else:
-        lines.append("*当前样本无可用数值价格，本节不展开统计表。*")
-        lines.append("")
-        lines.extend(_markdown_price_promotion_section(promo_sig))
-    lines.append("")
-
-    _llm_pr = (llm_price_groups_section_md or "").strip()
-    if _llm_pr:
-        lines.extend(
-            [
-                "",
-                "#### 细类价盘要点归纳（大模型，与第六章量化表互补）",
-                "",
-                "> **说明**：侧重价带与标价/券后关系的可读叙述；**数值以正文分位数表为准**。",
-                "",
-                _llm_pr,
-                "",
-            ]
-        )
-
-    _llm_po = (llm_promo_groups_section_md or "").strip()
-    if _llm_po:
-        lines.extend(
-            [
-                "",
-                "#### 细类促销与活动要点归纳（大模型，与第六章第一节及价盘互补）",
-                "",
-                "> **说明**：依据合并表「促销摘要」及榜单相关字段（如「榜单排名」「榜单类文案」）等**页面展示摘录**；"
-                "不采用列表「卖点/腰带」类字段作归纳依据（多为固定词表匹配，口径偏粗）。归纳券/补贴/新人/榜单曝光等活动形态，**不**替代第五章的配料/宣称归纳。**具体以页面与 CSV 为准**。",
-                "",
-                _llm_po,
-                "",
-            ]
-        )
-
-    _sm_score = sentiment_lex.get("method") == "score_then_lexeme"
-    _sec82_title = (
-        "### 8.2 评价正负面粗判（评分优先 + 关键词回退）"
-        if _sm_score
-        else "### 8.2 评价正负面粗判（关键词规则）"
-    )
-    _sec82_block: list[str] = [
-        "---",
-        "",
-        "## 八、消费者反馈与用户画像（按细分类目）",
-        "",
-        "### 8.1 方法",
-        "",
-        "- **细类划分**：与**第五章「竞品矩阵」**相同，**仅**依据合并表中的**商品详情页类目路径**解析为「饼干 / 西式糕点 / …」等（规则见第五章开头说明）。",
-        "- **归因**：每条评价按其 SKU 对应到深入样本，再映射到该 SKU 所属细类；SKU 不在合并表中的评价单独归入说明性分组；**在合并表中但该 SKU 缺少类目路径或读不出细类名称的，该评价不进入按细类统计**（与第五章**同一条排除规则**）。",
-        "- **正负面粗判（第八章第二节）**：若评价含有效「评分」列则**先按星级**粗分正负与中评，再在对应子集内统计口语短语；无评分时仍按关键词子串；若任务开启**大模型评价情感分析**，可附**大模型对抽样原文的主题归因**，与条形图互补。",
-        (
-            "- **文本补充分析（第八章第三节）**：本任务已用中文分词与统计工具做了开放词表分析（词频、关键词突出度、词对共现、主题归纳等，可选词云），与**第八章第二节**规则词表条形图**不同**、**互补**；**不再**输出原「关注词次数 + 场景占比」左右并列条图。"
-            if _ch8_probe_sec
-            else "- **关注词与使用场景（第八章第三节）**：对组内评价正文做关注词子串计数（左栏条形图）；对每条有效文本独立扫描**本次任务生效的场景词组**（来自报告调参或系统默认），一条可属多场景，右栏为**占该细类有效文本比例 %**（多标签下可相加 **>** 100%）。二者在 **同一张图左右并列**，与第五章矩阵细类一一对应。"
-        ),
-        "",
-        _sec82_title,
-        "",
-        f"- **有效文本条数**：{sentiment_lex.get('text_units', 0)}（与第八章第一节**归因规则**一致）。",
-    ]
-    if _sm_score:
-        _sec82_block.append(
-            "- **正负面粗判规模**：本批存在有效「评分」时——**1～2 星**计为偏负向，**4～5 星**计为偏正向，**3 星**计为中评，**空文本**计为中性；"
-            "无评分的条仍按关键词子串划分；「混合」仅在**无评分**且同条兼含正/负关键词时出现。"
-        )
-    _sec82_block.extend(
-        [
-            f"- **偏正向**：{sentiment_lex.get('positive_only', 0)} 条"
-            + ("（主要为 4～5 星）" if _sm_score else "（仅命中正向词表）")
-            + "；"
-            f"**偏负向**：{sentiment_lex.get('negative_only', 0)} 条"
-            + ("（主要为 1～2 星）" if _sm_score else "（仅命中负向词表）")
-            + "；"
-            f"**混合**：{sentiment_lex.get('mixed_positive_and_negative', 0)} 条"
-            + ("（无评分且同条兼含正/负关键词）" if _sm_score else "（同条兼含正/负词）")
-            + "；"
-            f"**中性或空文本**：{sentiment_lex.get('neutral_or_empty', 0)} 条"
-            + ("（含 3 星中评及无关键词命中）" if _sm_score else "")
-            + "。",
-            "- **说明**："
-            + (
-                "星级与正文可能不一致（如五星长文吐槽）；口语短语条形图仅在对应星级子集内统计；正式结论请**人工抽样**阅读原文。"
-                if _sm_score
-                else "词表为方向性粗判，讽刺、省略与错别字会导致误判；正式结论请**人工抽样**阅读原文。"
-            ),
-        ]
-    )
-    lines.extend(_sec82_block)
-    _scope = (sentiment_lex.get("lexeme_scope_note") or "").strip()
-    if _scope:
-        lines.append(f"- **词根统计说明**：{_scope}")
-    lines.extend(["", ""])
-    lines.extend(
-        _embed_chart(
-            run_dir,
-            "chart_sentiment_overview_pie.png",
-            "评价正负面粗判规模（扇形图；与上表条数一致）",
-        )
-    )
-    lines.extend(
-        _embed_chart(
-            run_dir,
-            "chart_positive_lexemes_bar.png",
-            (
-                "正向评价里**最常出现的口语短语**（在 **4～5 星** 评价条内统计；条形图）"
-                if _sm_score
-                else "正向评价里**最常出现的口语短语**（在偏正向或混合评价条内统计；条形图）"
-            ),
-        )
-    )
-    lines.extend(
-        _embed_chart(
-            run_dir,
-            "chart_negative_lexemes_bar.png",
-            (
-                "负向评价里**最常出现的口语短语**（在 **1～2 星** 评价条内统计；条形图）"
-                if _sm_score
-                else "负向评价里**最常出现的口语短语**（在偏负向或混合评价条内统计；条形图）"
-            ),
-        )
-    )
-    pos_h = sentiment_lex.get("positive_tone_lexeme_hits") or []
-    neg_h = sentiment_lex.get("negative_tone_lexeme_hits") or []
-    if pos_h:
-        frag = "；".join(
-            f"「{x.get('word', '')}」{x.get('texts_matched', 0)} 条"
-            for x in pos_h[:6]
-            if isinstance(x, dict)
-        )
-        lines.append(f"- **正向语境高频短语（摘要）**：{frag}。")
-    if neg_h:
-        frag_n = "；".join(
-            f"「{x.get('word', '')}」{x.get('texts_matched', 0)} 条"
-            for x in neg_h[:6]
-            if isinstance(x, dict)
-        )
-        lines.append(f"- **负向语境高频短语（摘要）**：{frag_n}。")
-    _llm_s = (llm_sentiment_section_md or "").strip()
-    if _llm_s:
-        lines.extend(
-            [
-                "",
-                "#### 大模型深入解读（主题归因，与词频统计互补）",
-                "",
-                "> **说明**：基于与上节**同一套评分优先或关键词归类规则**抽样的评价原文，由大模型归纳**用户在说什么**（尤其是负向的具体事由），与上列条数、条形图**互补**；引文以原评论为准。",
-                "",
-                _llm_s,
-            ]
-        )
-    lines.append("")
-    if _ch8_probe_sec:
-        lines.extend(
-            [
-                "### 8.3 评论文本补充分析（词频、关键词与共现、主题归纳）",
-                "",
-                "> **说明**：与**第八章第二节**口语短语条形图（规则词表）**口径不同**、**互补**；**不再**输出本章原「关注词 + 场景」左右并列条图；插图位于本批次报告附图文件夹中。",
-                "",
-                _ch8_probe_sec,
-                "",
-            ]
-        )
-    else:
-        # 仅当未嵌入第八章第三节补充分析（_ch8_probe_sec 为空）时：原「关注词 + 场景」条图与逐细类段落
-        lines.extend(
-            [
-                "### 8.3 关注词与使用场景（按细类）",
-                "",
-                "每细类一张**左右并列图**（与报告附图文件夹中的 ``chart_focus_and_scenarios_bar__*.png`` 同源）："
-                "**左**为配置关注词子串命中次数（同一评价可出现多次，为次数而非去重条数）；"
-                "**右**为预设场景词组命中占该细类有效文本比例 %（一条可属多场景；多柱比例可相加 **>** 100%）。"
-                "统计均基于评价正文（或兜底预览）子串规则，**不等于**购买动机调研结论。",
-                "",
-            ]
-        )
-        if not feedback_groups:
-            lines.append("*无评价数据可归组。*")
-            lines.append("")
-        else:
-            for gi, (gname, cr_g, texts_g) in enumerate(feedback_groups):
-                n_flat = len(cr_g)
-                lines.append(f"#### {gname}")
-                lines.append("")
-                lines.append(
-                    f"- **本细类逐条评价**：{n_flat} 条；**用于统计的有效文本条数**：{len(texts_g)}。"
-                )
-                lines.append("")
-                hits_g = _group_keyword_hits(cr_g, texts_g, focus_words=focus_words)
-                scen_g, scen_ng = _comment_scenario_counts(texts_g, scenario_groups)
-                has_focus = any(n > 0 for n in hits_g.values()) if hits_g else False
-                has_scen = scen_ng > 0 and any(n > 0 for n in scen_g.values())
-                if scen_ng <= 0:
-                    lines.append("*该细类下无可用评价正文。*")
-                    lines.append("")
-                    continue
-                if has_focus or has_scen:
-                    cap = (
-                        f"「{_md_cell(gname, 24)}」细类 · 关注词与使用场景（左：关注词命中次数；右：场景占有效文本 %；"
-                        f"有效文本 **{scen_ng}** 条）"
-                    )
-                    lines.extend(
-                        _embed_chart(
-                            run_dir,
-                            _focus_scenario_combo_bar_filename(gname, gi),
-                            cap,
-                        )
-                    )
-                else:
-                    lines.append("*该细类无关注词命中且未命中预设场景词组。*")
-                    lines.append("")
-                if has_scen:
-                    for para in _scenario_summary_bullets(scen_g, scen_ng):
-                        lines.append(para)
-                        lines.append("")
-                elif scen_ng > 0:
-                    lines.append("*未命中预设场景词组。*")
-                    lines.append("")
-
-        _llm_sg = (llm_scenario_groups_section_md or "").strip()
-        if _llm_sg:
-            lines.extend(
-                [
-                    "",
-                    "#### 使用场景要点归纳（大模型，与第八章第三节右栏图表互补）",
-                    "",
-                    "> **说明**：与第八章第三节**相同**的预设场景词组与子串命中规则；**各场景条数与占比以正文图右栏为准**。",
-                    "",
-                    _llm_sg,
-                    "",
-                ]
-            )
-
-        _llm_cg = (llm_comment_groups_section_md or "").strip()
-        if _llm_cg:
-            lines.extend(
-                [
-                    "",
-                    "#### 细类评价与关注词要点归纳（大模型，与第八章第三节左栏图表互补）",
-                    "",
-                    "> **说明**：归纳各细类反馈主题与配置关注词命中；**次数与第八章第三节图左栏以正文为准**。",
-                    "",
-                    _llm_cg,
-                    "",
-                ]
-            )
-
-    lines.extend(["---", "", "## 九、策略与机会提示（假设清单，待验证）", ""])
-    _llm_st = (llm_strategy_opportunities_section_md or "").strip()
-    if _llm_st:
-        lines.extend(
-            [
-                "基于本任务结构化摘要（价盘、集中度、评价与场景、促销信号等）的**假设性策略归纳**；数字与明细以前文及 CSV 为准，定稿前请结合贵司成本、渠道与合规复核。",
-                "",
-                "#### 策略与机会建议（大模型）",
-                "",
-                _llm_st,
-                "",
-            ]
-        )
-    else:
-        lines.extend(
-            [
-                "未生成本节大模型正文：请在任务 `report_config` 中开启 `llm_strategy_opportunities` 并重跑产物，或检查 run 目录下 `strategy_opportunities_llm.json` 是否报错。",
-                "",
-            ]
-        )
-
-    lines.extend(
-        [
-            "---",
-            "",
-            "## 附录 A：数据留存说明",
-            "",
-            "- 本批次**任务输出目录**内保存：搜索列表导出、深入 SKU 合并表、商详与评价相关表格，以及搜索/商详原始响应与运行参数快照，供内部复核与复算。",
-            "- 对外演示或转发前请按公司规范做**脱敏**处理。",
-            "",
-            "---",
-            "",
-            "*本报告由系统自动汇总生成；定稿前请业务交叉核对数据与结论。*",
-            "",
-        ]
-    )
-    return "\n".join(lines)
-
-
-def _sanitize_json_numbers(obj: Any) -> Any:
-    """浮点 NaN/Inf 无法 JSON 序列化，统一转 None 或圆角。"""
-    if isinstance(obj, float):
-        if math.isnan(obj) or math.isinf(obj):
-            return None
-        return round(obj, 6)
-    if isinstance(obj, dict):
-        return {k: _sanitize_json_numbers(v) for k, v in obj.items()}
-    if isinstance(obj, list):
-        return [_sanitize_json_numbers(x) for x in obj]
-    return obj
-
-
-def build_competitor_brief(
-    *,
-    run_dir: Path,
-    keyword: str,
-    merged_rows: list[dict[str, str]],
-    search_export_rows: list[dict[str, str]],
-    comment_rows: list[dict[str, str]],
-    meta: dict[str, Any] | None,
-    report_config: dict[str, Any] | None = None,
-) -> dict[str, Any]:
-    """
-    与 ``build_competitor_markdown`` 共用**同一套统计规则**，输出可 JSON 序列化的结构化竞品摘要（**规则驱动**，无 LLM）。
-    """
-    focus_words, scenario_groups, _ext = resolve_report_tuning(report_config)
-    sku_header = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
-    title_h = MERGED_FIELD_TO_CSV_HEADER["title"]
-    batch = _run_batch_label(run_dir)
-    n_sku = len(merged_rows)
-    n_cmt = len(comment_rows)
-    n_sku_matrix = sum(1 for r in merged_rows if _competitor_matrix_group_key(r))
-
-    list_export = len(search_export_rows) > 0
-    structure_rows = search_export_rows if list_export else merged_rows
-    n_structure = len(structure_rows)
-    shops_s = _structure_shops(structure_rows, list_export=list_export)
-    brands_s = _structure_brands(structure_rows, list_export=list_export)
-    shops_for_cr = _structure_names_for_pie_counter(shops_s)
-    brands_for_cr = _structure_names_for_pie_counter(brands_s)
-    cr1_shop, cr3_shop, top_shop_s, top_shop_share = _brand_cr(shops_for_cr)
-    cr1_list_brand, cr3_list_brand, top_list_brand, _ = _brand_cr(brands_for_cr)
-    cm_structure = _category_mix(merged_rows, top_k=12)
-    min_brand_rows = max(5, int(0.02 * n_structure)) if n_structure else 5
-
-    brands_deep = [
-        _cell(r, MERGED_FIELD_TO_CSV_HEADER["detail_brand"], "detail_brand")
-        for r in merged_rows
-        if _cell(r, MERGED_FIELD_TO_CSV_HEADER["detail_brand"], "detail_brand")
-    ]
-    cr1_deep, cr3_deep, top_brand_deep, top_brand_deep_share = _brand_cr(
-        brands_deep
-    )
-    cr1_hints = cr1_shop if list_export and cr1_shop is not None else cr1_deep
-
-    pst_merged = _price_stats_extended(_collect_prices(merged_rows))
-    pst_list = (
-        _price_stats_extended(_collect_prices(search_export_rows))
-        if list_export
-        else {}
-    )
-    pst = (
-        pst_list
-        if list_export and pst_list.get("n", 0) > 0
-        else pst_merged
-    )
-    price_stats_source = (
-        "pc_search_export_all_rows"
-        if list_export and pst_list.get("n", 0) > 0
-        else "keyword_pipeline_merged"
-    )
-    promo_rows_brief = (
-        search_export_rows
-        if list_export and pst_list.get("n", 0) > 0
-        else merged_rows
-    )
-    price_promotion_signals = _analyze_price_promotions(promo_rows_brief)
-
-    hits = _comment_keyword_hits(comment_rows, focus_words)
-    if not hits:
-        blob = _merge_comment_previews(merged_rows)
-        for w in focus_words:
-            if len(w) < 2:
-                continue
-            n = blob.count(w)
-            if n:
-                hits[w] += n
-
-    comment_texts, comment_scores = _iter_comment_text_units_and_scores(
-        comment_rows, merged_rows
-    )
-    comment_sentiment_lexicon = _comment_sentiment_lexicon(
-        comment_texts, comment_scores
-    )
-    scen_counts, scen_n_texts = _comment_scenario_counts(
-        comment_texts, scenario_groups
-    )
-
-    (
-        api_rc,
-        api_list_kw,
-        api_rc_uniques,
-        api_raw_json_n,
-        _api_rc_n_values,
-    ) = _pc_search_result_count_from_raw(run_dir)
-
-    proxy = _search_list_proxies(search_export_rows) if search_export_rows else {}
-
-    hints = _strategy_hints(
-        cr1=cr1_hints,
-        pst=pst,
-        hits=hits,
-        n_comments=n_cmt,
-        scen_counts=scen_counts,
-        scen_n_texts=scen_n_texts,
-    )
-
-    matrix_groups: list[dict[str, Any]] = []
-    for gname, mrows in _merged_rows_grouped_for_matrix(merged_rows):
-        items: list[dict[str, str]] = []
-        for row in mrows:
-            items.append(
-                {
-                    "sku_id": _cell(row, sku_header),
-                    "title": _cell(row, title_h),
-                    "brand": _cell(
-                        row,
-                        MERGED_FIELD_TO_CSV_HEADER["detail_brand"],
-                        "detail_brand",
-                    ),
-                    "list_price_show": _cell(
-                        row, *_LIST_SHOW_PRICE_CELL_KEYS
-                    ),
-                    "coupon_or_detail_price": _cell(
-                        row,
-                        _COUPON_SHOW_PRICE_KEY,
-                        _LEGACY_COUPON_SHOW_PRICE_KEY,
-                    ),
-                    "detail_price_final": _cell(row, *_DETAIL_PRICE_FINAL_CSV_KEYS),
-                    "shop": _cell(row, *_MERGED_SHOP_CELL_KEYS),
-                    "category": _detail_category_path_cell(row),
-                    "selling_point": _cell(
-                        row, _SELLING_POINT_KEY, _LEGACY_SELLING_POINT_KEY
-                    )[:240],
-                    "comment_fuzzy": _cell(row, *_COMMENT_FUZZ_KEYS),
-                    "total_sales": merged_csv_effective_total_sales(row),
-                }
-            )
-        matrix_groups.append(
-            {"group": gname, "sku_count": len(items), "skus": items}
-        )
-
-    feedback_by_group: list[dict[str, Any]] = []
-    usage_scenarios_by_matrix_group: list[dict[str, Any]] = []
-    for gi, (gname, cr, tu) in enumerate(
-        _consumer_feedback_by_matrix_group(
-            merged_rows=merged_rows,
-            comment_rows=comment_rows,
-            sku_header=sku_header,
-        )
-    ):
-        gh = _group_keyword_hits(cr, tu, focus_words=focus_words)
-        scen_g, scen_n_g = _comment_scenario_counts(tu, scenario_groups)
-        slug_fb = _scenario_group_asset_slug(gname, gi)
-        feedback_by_group.append(
-            {
-                "group": gname,
-                "matrix_group_index": gi,
-                "chart_slug": slug_fb,
-                "comment_rows": len(cr),
-                "effective_comment_text_units": len(tu),
-                "focus_keyword_hits": [
-                    {"word": w, "count": n} for w, n in gh.most_common(24)
-                ],
-                "scenarios_top": [
-                    {
-                        "scenario": s,
-                        "count": n,
-                        "share_of_text_units": (
-                            n / scen_n_g if scen_n_g else 0.0
-                        ),
-                    }
-                    for s, n in scen_g.most_common(6)
-                ]
-                if scen_n_g
-                else [],
-            }
-        )
-        if scen_n_g > 0 and scen_g:
-            usage_scenarios_by_matrix_group.append(
-                {
-                    "group": gname,
-                    "matrix_group_index": gi,
-                    "chart_slug": slug_fb,
-                    "effective_text_units": scen_n_g,
-                    "scenarios": [
-                        {
-                            "scenario": s,
-                            "count": int(n),
-                            "share_of_text_units": (
-                                float(n) / scen_n_g if scen_n_g else 0.0
-                            ),
-                        }
-                        for s, n in scen_g.most_common()
-                        if n > 0
-                    ],
-                }
-            )
-
-    meta_slice: dict[str, Any] = {}
-    if meta:
-        for k in (
-            "page_start",
-            "page_to",
-            "max_skus_config",
-            "pc_search_export_rows",
-            "merged_rows",
-            "scenario_filter_enabled",
-            "merged_csv_mode",
-        ):
-            if k in meta:
-                meta_slice[k] = meta[k]
-
-    list_brand_block: dict[str, Any] | None
-    if len(brands_for_cr) >= min_brand_rows:
-        list_brand_block = {
-            "first_share": cr1_list_brand,
-            "top_three_combined_share": cr3_list_brand,
-            "top_label": top_list_brand,
-        }
-    else:
-        list_brand_block = None
-
-    out: dict[str, Any] = {
-        "schema_version": 1,
-        "keyword": keyword,
-        "batch_label": batch,
-        "run_dir": str(run_dir.resolve()),
-        "scope": {
-            "merged_sku_count": n_sku,
-            "comment_flat_rows": n_cmt,
-            "structure_source_rows": n_structure,
-            "uses_pc_search_list_export": list_export,
-            "category_mix_source": "keyword_pipeline_merged",
-            "category_mix_valid_matrix_sku_count": n_sku_matrix,
-        },
-        "meta": meta_slice or None,
-        "pc_search_raw": {
-            "result_count_consensus": api_rc,
-            "list_keyword": api_list_kw or None,
-            "result_count_uniques": api_rc_uniques,
-            "raw_json_files_scanned": api_raw_json_n,
-        },
-        "list_visibility_proxy": proxy,
-        "concentration": {
-            "shops_from_list": {
-                "first_share": cr1_shop,
-                "top_three_combined_share": cr3_shop,
-                "top_label": top_shop_s,
-                "top_share_pct": top_shop_share,
-            },
-            "list_brand_field": list_brand_block,
-            "detail_brand_among_merged": {
-                "first_share": cr1_deep,
-                "top_three_combined_share": cr3_deep,
-                "top_label": top_brand_deep,
-                "top_share_pct": top_brand_deep_share,
-            },
-        },
-        "category_mix_top": [
-            {"label": lbl, "count": cnt} for lbl, cnt in cm_structure
-        ],
-        "list_brand_mix_top": [
-            {"label": k, "count": v}
-            for k, v in _counter_mix_top_rows_with_remainder(
-                brands_s,
-                top_n=24,
-                remainder_label="（其余品牌）",
-            )
-        ],
-        "list_shop_mix_top": [
-            {"label": k, "count": v}
-            for k, v in _counter_mix_top_rows_with_remainder(
-                shops_s,
-                top_n=24,
-                remainder_label="（其余店铺）",
-            )
-        ],
-        "price_stats": pst,
-        "price_stats_source": price_stats_source,
-        "price_stats_merged_sample": pst_merged,
-        "price_stats_list_export": pst_list if list_export else {},
-        "price_promotion_signals": price_promotion_signals,
-        "comment_focus_keywords": [
-            {"word": w, "count": n} for w, n in hits.most_common(24)
-        ],
-        "usage_scenarios": [
-            {
-                "scenario": lbl,
-                "count": n,
-                "share_of_text_units": (
-                    n / scen_n_texts if scen_n_texts else 0.0
-                ),
-            }
-            for lbl, n in scen_counts.most_common(16)
-        ],
-        "usage_scenarios_denominator": scen_n_texts,
-        "usage_scenarios_by_matrix_group": usage_scenarios_by_matrix_group,
-        "strategy_hints": hints,
-        "matrix_by_group": matrix_groups,
-        "consumer_feedback_by_matrix_group": feedback_by_group,
-        "comment_sentiment_lexicon": comment_sentiment_lexicon,
-        "notes": [
-            "与在线分析报告各章**计数规则**一致；关注词与场景以任务中的分析规则为准（子串命中统计，非深度主题模型）。",
-            "价格来自页面展示字段抽取，含促销与规格差异；促销与标价对齐等为启发式摘录，仅供对照。",
-            "评价语气为关键词粗判，非深度学习情感模型。",
-            "「集中度」中：最大一家占比、前三名合计占比为小数（如 0.12 表示约 12%），对应列表或深入样本中的相关行。",
-        ],
-    }
-    return _sanitize_json_numbers(out)
-
-
-def main() -> None:
-    try:
-        if hasattr(sys.stdout, "reconfigure"):
-            sys.stdout.reconfigure(encoding="utf-8", errors="replace")
-        if hasattr(sys.stderr, "reconfigure"):
-            sys.stderr.reconfigure(encoding="utf-8", errors="replace")
-    except Exception:
-        pass
-
-    existing = _resolve_existing_run_dir(EXISTING_RUN_DIR)
-    meta_path_early = (existing / kpl.FILE_RUN_META_JSON) if existing else None
-    meta_early: dict[str, Any] | None = None
-    if meta_path_early and meta_path_early.is_file():
-        try:
-            meta_early = json.loads(meta_path_early.read_text(encoding="utf-8"))
-        except json.JSONDecodeError:
-            meta_early = None
-
-    if existing:
-        if not existing.is_dir():
-            print(f"[竞品报告] EXISTING_RUN_DIR 不是目录: {existing}", file=sys.stderr)
-            sys.exit(2)
-        kw = (KEYWORD or "").strip() or _infer_keyword(existing, meta_early)
-        if not kw:
-            print(
-                "[竞品报告] 仅分析已有目录时，请配置 KEYWORD，或保留 run_meta.json 的 keyword，"
-                "或使目录名为 YYYYMMDD_HHMMSS_关键词",
-                file=sys.stderr,
-            )
-            sys.exit(2)
-        run_dir = existing
-        print(f"[竞品报告] 使用已有目录（不抓取）: {run_dir}", file=sys.stderr)
-    else:
-        kw = (KEYWORD or "").strip()
-        if not kw:
-            print("[竞品报告] 全量抓取时请在本文件顶部配置 KEYWORD", file=sys.stderr)
-            sys.exit(2)
-
-        backup: dict[str, Any] = {}
-        try:
-            if OVERRIDE_MAX_SKUS is not None:
-                backup["MAX_SKUS"] = kpl.MAX_SKUS
-                kpl.MAX_SKUS = max(1, int(OVERRIDE_MAX_SKUS))
-            if OVERRIDE_PAGE_START is not None:
-                backup["PAGE_START"] = kpl.PAGE_START
-                kpl.PAGE_START = max(1, int(OVERRIDE_PAGE_START))
-            if OVERRIDE_PAGE_TO is not None:
-                backup["PAGE_TO"] = kpl.PAGE_TO
-                kpl.PAGE_TO = max(1, int(OVERRIDE_PAGE_TO))
-
-            print(f"[竞品报告] 关键词={kw!r}，开始流水线…", file=sys.stderr)
-            run_dir = kpl.main(keyword=kw)
-        finally:
-            for name, val in backup.items():
-                setattr(kpl, name, val)
-
-    merged_path = run_dir / kpl.FILE_MERGED_CSV
-    comments_path = run_dir / kpl.FILE_COMMENTS_FLAT_CSV
-    meta_path = run_dir / kpl.FILE_RUN_META_JSON
-
-    _, merged_rows = _read_csv_rows(merged_path)
-    _, search_export_rows = _read_csv_rows(run_dir / kpl.FILE_PC_SEARCH_CSV)
-    _, comment_rows = _read_csv_rows(comments_path)
-    meta: dict[str, Any] | None = meta_early if existing else None
-    if meta is None and meta_path.is_file():
-        try:
-            meta = json.loads(meta_path.read_text(encoding="utf-8"))
-        except json.JSONDecodeError:
-            meta = None
-
-    md = build_competitor_markdown(
-        run_dir=run_dir,
-        keyword=kw,
-        merged_rows=merged_rows,
-        search_export_rows=search_export_rows,
-        comment_rows=comment_rows,
-        meta=meta,
-    )
-    out_md = run_dir / "competitor_analysis.md"
-    out_md.write_text(md, encoding="utf-8")
-    print(f"[竞品报告] 运行目录: {run_dir}", file=sys.stderr)
-    print(f"[竞品报告] 已写: {out_md}", file=sys.stderr)
-
-
-if __name__ == "__main__":
-    main()
+from pipeline.jd_competitor_report import *  # noqa: F403
diff --git a/backend/crawler_copy/jd_pc_search/jd_keyword_pipeline.py b/backend/crawler_copy/jd_pc_search/jd_keyword_pipeline.py
index ea1324b..20af7dc 100644
--- a/backend/crawler_copy/jd_pc_search/jd_keyword_pipeline.py
+++ b/backend/crawler_copy/jd_pc_search/jd_keyword_pipeline.py
@@ -25,7 +25,7 @@ PC 搜索导出 CSV、评价扁平 CSV、详情汇总 CSV（``detail_ware_export
 默认启用 **应用场景筛选**（``brief_content.txt`` 4.1 中式面点/主食 + 4.2 烘焙）：仅命中关键词的 SKU 进入详情与评论队列；词表见 ``scenario_filter.py``。``SCENARIO_FILTER_ENABLED=False`` 可关闭；``SCENARIO_FILTER_PC_SEARCH_CSV="filtered"`` 可使导出 CSV 与筛选后列表一致。
 各 SKU 完整接口 JSON 仍在 ``detail/ware_{sku}_response.json``。
 
-端到端竞品速览 Markdown：配置 ``jd_competitor_report.py`` 顶部 ``KEYWORD`` 后执行 ``python jd_competitor_report.py``（内部调用本模块 ``main(keyword=...)``）。
+端到端竞品速览 Markdown：在 ``backend`` 下配置 ``pipeline.jd_competitor_report`` 顶部 ``KEYWORD`` 后执行 ``python -m pipeline.jd_competitor_report``；或执行本目录兼容入口 ``python jd_competitor_report.py``（内部调用本模块 ``main(keyword=...)``）。
 """
 
 from __future__ import annotations
@@ -317,7 +317,7 @@ def main(keyword: str | None = None) -> Path:
     """
     跑完整条流水线。``keyword`` 非空时覆盖文件内 ``KEYWORD``；返回本次运行目录。
 
-    供 ``jd_competitor_report`` 等脚本 ``import`` 调用；命令行仍执行 ``main()`` 无参。
+    供 ``pipeline.jd_competitor_report`` 等脚本 ``import`` 调用；命令行仍执行 ``main()`` 无参。
     """
     try:
         if hasattr(sys.stdout, "reconfigure"):
diff --git a/backend/pipeline/competitor_report/__init__.py b/backend/pipeline/competitor_report/__init__.py
new file mode 100644
index 0000000..4bcfaa2
--- /dev/null
+++ b/backend/pipeline/competitor_report/__init__.py
@@ -0,0 +1 @@
+"""京东竞品分析报告：CSV 解析、统计与 Markdown/简报（归属 ``pipeline``，与爬虫采集分离）。"""
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/comment_sentiment.py b/backend/pipeline/competitor_report/comment_sentiment.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/comment_sentiment.py
rename to backend/pipeline/competitor_report/comment_sentiment.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/config.py b/backend/pipeline/competitor_report/config.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/config.py
rename to backend/pipeline/competitor_report/config.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/constants.py b/backend/pipeline/competitor_report/constants.py
similarity index 98%
rename from backend/crawler_copy/jd_pc_search/competitor_report/constants.py
rename to backend/pipeline/competitor_report/constants.py
index 81fcf78..7b2c199 100644
--- a/backend/crawler_copy/jd_pc_search/competitor_report/constants.py
+++ b/backend/pipeline/competitor_report/constants.py
@@ -1,4 +1,4 @@
-"""CSV 表头键、运行默认调参与关注词/场景配置（与 ``jd_competitor_report`` 顶层一致）。"""
+"""CSV 表头键、运行默认调参与关注词/场景配置（与 ``pipeline.jd_competitor_report`` 顶层一致）。"""
 from __future__ import annotations
 
 from pipeline.csv_schema import (
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/consumer_feedback.py b/backend/pipeline/competitor_report/consumer_feedback.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/consumer_feedback.py
rename to backend/pipeline/competitor_report/consumer_feedback.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/csv_io.py b/backend/pipeline/competitor_report/csv_io.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/csv_io.py
rename to backend/pipeline/competitor_report/csv_io.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/ingredients.py b/backend/pipeline/competitor_report/ingredients.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/ingredients.py
rename to backend/pipeline/competitor_report/ingredients.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/list_mix.py b/backend/pipeline/competitor_report/list_mix.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/list_mix.py
rename to backend/pipeline/competitor_report/list_mix.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/llm_group_payloads.py b/backend/pipeline/competitor_report/llm_group_payloads.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/llm_group_payloads.py
rename to backend/pipeline/competitor_report/llm_group_payloads.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/matrix_group.py b/backend/pipeline/competitor_report/matrix_group.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/matrix_group.py
rename to backend/pipeline/competitor_report/matrix_group.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/matrix_md.py b/backend/pipeline/competitor_report/matrix_md.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/matrix_md.py
rename to backend/pipeline/competitor_report/matrix_md.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/price_promo.py b/backend/pipeline/competitor_report/price_promo.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/price_promo.py
rename to backend/pipeline/competitor_report/price_promo.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/price_stats.py b/backend/pipeline/competitor_report/price_stats.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/price_stats.py
rename to backend/pipeline/competitor_report/price_stats.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/report_md_helpers.py b/backend/pipeline/competitor_report/report_md_helpers.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/report_md_helpers.py
rename to backend/pipeline/competitor_report/report_md_helpers.py
diff --git a/backend/crawler_copy/jd_pc_search/competitor_report/run_context.py b/backend/pipeline/competitor_report/run_context.py
similarity index 100%
rename from backend/crawler_copy/jd_pc_search/competitor_report/run_context.py
rename to backend/pipeline/competitor_report/run_context.py
diff --git a/backend/pipeline/demos/chapter8_text_mining_probe.py b/backend/pipeline/demos/chapter8_text_mining_probe.py
index 05ee841..0212b48 100644
--- a/backend/pipeline/demos/chapter8_text_mining_probe.py
+++ b/backend/pipeline/demos/chapter8_text_mining_probe.py
@@ -43,7 +43,7 @@ JCR_ROOT = BACKEND_ROOT / "crawler_copy" / "jd_pc_search"
 if str(JCR_ROOT) not in sys.path:
     sys.path.insert(0, str(JCR_ROOT))
 
-import jd_competitor_report as jcr  # noqa: E402
+from pipeline import jd_competitor_report as jcr  # noqa: E402
 import jd_keyword_pipeline as kpl  # noqa: E402
 
 from pipeline.csv_schema import MERGED_FIELD_TO_CSV_HEADER  # noqa: E402
diff --git a/backend/pipeline/demos/run_price_groups_llm_demo.py b/backend/pipeline/demos/run_price_groups_llm_demo.py
index fc6c433..6c52244 100644
--- a/backend/pipeline/demos/run_price_groups_llm_demo.py
+++ b/backend/pipeline/demos/run_price_groups_llm_demo.py
@@ -26,7 +26,7 @@ JCR_ROOT = BACKEND_ROOT / "crawler_copy" / "jd_pc_search"
 if str(JCR_ROOT) not in sys.path:
     sys.path.insert(0, str(JCR_ROOT))
 
-import jd_competitor_report as jcr  # noqa: E402
+from pipeline import jd_competitor_report as jcr  # noqa: E402
 import jd_keyword_pipeline as kpl  # noqa: E402
 
 
diff --git a/backend/pipeline/demos/run_report_llm_chapters_demo.py b/backend/pipeline/demos/run_report_llm_chapters_demo.py
index 53560ea..e491d7e 100644
--- a/backend/pipeline/demos/run_report_llm_chapters_demo.py
+++ b/backend/pipeline/demos/run_report_llm_chapters_demo.py
@@ -37,7 +37,7 @@ JCR_ROOT = BACKEND_ROOT / "crawler_copy" / "jd_pc_search"
 if str(JCR_ROOT) not in sys.path:
     sys.path.insert(0, str(JCR_ROOT))
 
-import jd_competitor_report as jcr  # noqa: E402
+from pipeline import jd_competitor_report as jcr  # noqa: E402
 import jd_keyword_pipeline as kpl  # noqa: E402
 
 from pipeline.csv_schema import MERGED_FIELD_TO_CSV_HEADER  # noqa: E402
diff --git a/backend/pipeline/jd/runner.py b/backend/pipeline/jd/runner.py
index a127f1f..0ad756d 100644
--- a/backend/pipeline/jd/runner.py
+++ b/backend/pipeline/jd/runner.py
@@ -1,5 +1,5 @@
 """
-使用 ``crawler_copy/jd_pc_search`` 中的副本脚本执行流水线并生成竞品 Markdown。
+使用 ``crawler_copy/jd_pc_search`` 中的采集脚本执行流水线；竞品 Markdown 由 ``pipeline.jd_competitor_report`` 生成。
 依赖环境变量 ``LOW_GI_PROJECT_ROOT``（由 Django settings 从 ``market_assistant/.env`` 注入）。
 """
 from __future__ import annotations
@@ -133,13 +133,14 @@ def try_write_competitor_report_if_merged_exists(
 
 
 def _jd_crawler_modules():
+    from pipeline import jd_competitor_report as jcr  # noqa: WPS433
+
     root = Path(settings.CRAWLER_JD_ROOT)
     if not root.is_dir():
         raise FileNotFoundError(f"爬虫副本目录不存在: {root}")
     root_s = str(root.resolve())
     if root_s not in sys.path:
         sys.path.insert(0, root_s)
-    import jd_competitor_report as jcr  # noqa: WPS433
     import jd_keyword_pipeline as kpl  # noqa: WPS433
 
     return jcr, kpl
@@ -163,7 +164,7 @@ def use_chunked_group_summaries_llm(report_config: dict[str, Any] | None) -> boo
 
 
 def get_default_report_config() -> dict[str, Any]:
-    """与 ``jd_competitor_report`` 模块常量一致的默认报告调参（供前端回填）。"""
+    """与 ``pipeline.jd_competitor_report`` 模块常量一致的默认报告调参（供前端回填）。"""
     jcr, _ = _jd_crawler_modules()
     return {
         "llm_comment_sentiment": True,
diff --git a/backend/pipeline/jd_competitor_report.py b/backend/pipeline/jd_competitor_report.py
new file mode 100644
index 0000000..9a388d0
--- /dev/null
+++ b/backend/pipeline/jd_competitor_report.py
@@ -0,0 +1,1388 @@
+# -*- coding: utf-8 -*-
+"""
+关键词 → 调用 ``jd_keyword_pipeline`` 全链路采集 → 生成 **标准化竞品分析报告**（Markdown）。
+
+报告结构对齐常见竞品分析框架：研究范围与方法、执行摘要、**整体市场观察（列表可见度参考）**、
+市场与竞争结构、**按细分类目分组的竞品对比矩阵**、价格分析（含规则化价差/活动信号与可选 **细类价盘·促销** 大模型归纳）、**按细分类目的消费者反馈与用户画像**、**策略与机会提示**（以大模型归纳为主，可选）与附录；并明确数据边界。
+若运行配置中提供了外部市场规模摘录（``EXTERNAL_MARKET_TABLE_ROWS``），则追加对应表格小节；否则不输出占位行。
+
+依赖：全量抓取时与 ``crawler_copy/jd_pc_search/jd_keyword_pipeline.py`` 相同（Node、h5st、Playwright、``common/jd_cookie.txt``）。
+**仅复用已有目录生成报告时**不需要跑浏览器，只需该目录下已有 CSV / ``run_meta.json``。
+
+本模块位于 ``pipeline``（解析与报告）；爬虫实现仅在 ``crawler_copy/jd_pc_search``。
+
+用法：
+
+- **重新抓取并出报告**：``EXISTING_RUN_DIR = None``，配置 ``KEYWORD``（及可选 ``OVERRIDE_*``），在 ``backend`` 目录下执行
+  ``python -m pipeline.jd_competitor_report``；或沿用爬虫目录下的兼容入口 ``python jd_competitor_report.py``（见该文件说明）。
+- **只分析已有批次**：将 ``EXISTING_RUN_DIR`` 设为 ``pipeline_runs/<时间戳>_<关键词>/`` 的绝对或相对路径（相对当前工作目录），
+  再执行同一命令；**不重新抓取**。关键词优先用本文件 ``KEYWORD``，否则读 ``run_meta.json`` 的 ``keyword``，再否则从目录名
+  ``YYYYMMDD_HHMMSS_<词>`` 推断。
+
+流水线其余参数（评论分页、延迟等）仍在 ``jd_keyword_pipeline.py`` 顶部配置。
+
+输出：在对应运行目录下覆盖写入 ``competitor_analysis.md``。
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+import random
+import re
+import sys
+from collections import Counter
+from pathlib import Path
+from typing import Any
+
+# 竞品报告依赖爬虫副本中的 ``jd_keyword_pipeline``（采集）；本文件归属 pipeline（解析与成稿）。
+_BACKEND = Path(__file__).resolve().parent.parent
+_CRAWLER_JD = _BACKEND / "crawler_copy" / "jd_pc_search"
+if str(_CRAWLER_JD) not in sys.path:
+    sys.path.insert(0, str(_CRAWLER_JD))
+
+import jd_keyword_pipeline as kpl  # noqa: E402
+from pipeline.csv_schema import (  # noqa: E402
+    COMMENT_CSV_COLUMNS,
+    JD_SEARCH_CSV_HEADERS,
+    MERGED_FIELD_TO_CSV_HEADER,
+    merged_csv_effective_total_sales,
+)
+
+from pipeline.competitor_report.config import *  # noqa: F403
+from pipeline.competitor_report.constants import *  # noqa: F403
+from pipeline.competitor_report.csv_io import *  # noqa: F403
+from pipeline.competitor_report.price_promo import (  # noqa: E402
+    _analyze_price_promotions,
+    _markdown_price_promotion_section,
+)
+from pipeline.competitor_report.comment_sentiment import (  # noqa: E402
+    build_comment_sentiment_llm_payload,
+    _comment_keyword_hits,
+    _comment_sentiment_lexicon,
+    _iter_comment_text_units,
+    _iter_comment_text_units_and_scores,
+    _merge_comment_previews,
+    _parse_comment_score,
+)
+from pipeline.competitor_report.llm_group_payloads import (  # noqa: E402
+    build_comment_groups_llm_payload,
+    build_matrix_groups_llm_payload,
+    build_price_groups_llm_payload,
+    build_promo_groups_llm_payload,
+    build_scenario_groups_llm_payload,
+    _comment_scenario_counts,
+    _group_keyword_hits,
+    _text_hits_scenario_triggers,
+)
+from pipeline.competitor_report.matrix_group import (  # noqa: E402
+    _category_mix,
+    _competitor_matrix_group_key,
+    _merged_rows_grouped_for_matrix,
+)
+from pipeline.competitor_report.price_stats import _price_stats_extended  # noqa: E402
+from pipeline.competitor_report.consumer_feedback import (  # noqa: E402
+    _comment_lines_with_product_context,
+    _consumer_feedback_by_matrix_group,
+    _sku_to_matrix_group_map,
+)
+from pipeline.competitor_report.list_mix import (  # noqa: E402
+    _brand_cr,
+    _counter_mix_top_rows_with_remainder,
+    _search_list_proxies,
+    _structure_brands,
+    _structure_names_for_pie_counter,
+    _structure_shops,
+)
+from pipeline.competitor_report.matrix_md import (  # noqa: E402
+    _competitor_matrix_md_line,
+    _matrix_ingredients_cell,
+)
+from pipeline.competitor_report.report_md_helpers import (  # noqa: E402
+    _embed_chart,
+    _focus_scenario_combo_bar_filename,
+    _lines_4_reading_brand,
+    _lines_4_reading_shop,
+    _matrix_prices_sales_chart_filename,
+    _mermaid_pie_focus_keywords,
+    _scenario_group_asset_slug,
+    _scenario_summary_bullets,
+    _strategy_hints,
+)
+from pipeline.competitor_report.run_context import (  # noqa: E402
+    _infer_keyword,
+    _pc_search_result_count_from_raw,
+    _resolve_existing_run_dir,
+    _run_batch_label,
+)
+
+# ---------------------------------------------------------------------------
+# 运行配置（按需改这里；与 pipeline.competitor_report.constants 中默认关注词等配合使用）
+# ---------------------------------------------------------------------------
+# KEYWORD：京东 PC 搜索词；全量抓取时必填。「仅已有目录」模式下可留空，改从 run_meta / 目录名推断。
+KEYWORD = "低GI"
+# 已有流水线目录（含 keyword_pipeline_merged.csv 等）时设为路径则**不重新抓取**，只生成 competitor_analysis.md。
+EXISTING_RUN_DIR = None
+# EXISTING_RUN_DIR = r"data\JD\pipeline_runs\20260408_144606_低GI"  # 相对数据根或绝对路径
+# 以下非 None 时仅本次运行临时覆盖 jd_keyword_pipeline 中同名变量（不改 pipeline 文件）
+OVERRIDE_MAX_SKUS: int | None = None
+OVERRIDE_PAGE_START: int | None = None
+OVERRIDE_PAGE_TO: int | None = None
+
+
+def build_competitor_markdown(
+    *,
+    run_dir: Path,
+    keyword: str,
+    merged_rows: list[dict[str, str]],
+    search_export_rows: list[dict[str, str]],
+    comment_rows: list[dict[str, str]],
+    meta: dict[str, Any] | None,
+    report_config: dict[str, Any] | None = None,
+    llm_sentiment_section_md: str | None = None,
+    llm_matrix_section_md: str | None = None,
+    llm_price_groups_section_md: str | None = None,
+    llm_promo_groups_section_md: str | None = None,
+    llm_scenario_groups_section_md: str | None = None,
+    llm_comment_groups_section_md: str | None = None,
+    llm_strategy_opportunities_section_md: str | None = None,
+    chapter8_text_mining_probe_section_md: str | None = None,
+) -> str:
+    focus_words, scenario_groups, external_rows = resolve_report_tuning(report_config)
+    _ch8_probe_sec = (chapter8_text_mining_probe_section_md or "").strip()
+    sku_header = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
+    title_h = MERGED_FIELD_TO_CSV_HEADER["title"]
+    batch = _run_batch_label(run_dir)
+    n_sku = len(merged_rows)
+    n_cmt = len(comment_rows)
+    n_sku_pathed = sum(1 for r in merged_rows if _detail_category_path_cell(r))
+    n_sku_matrix = sum(1 for r in merged_rows if _competitor_matrix_group_key(r))
+
+    list_export = len(search_export_rows) > 0
+    structure_rows = search_export_rows if list_export else merged_rows
+    n_structure = len(structure_rows)
+    shops_s = _structure_shops(structure_rows, list_export=list_export)
+    brands_s = _structure_brands(structure_rows, list_export=list_export)
+    shops_for_cr = _structure_names_for_pie_counter(shops_s)
+    brands_for_cr = _structure_names_for_pie_counter(brands_s)
+    cr1_shop, cr3_shop, top_shop_s, _ = _brand_cr(shops_for_cr)
+    cr1_list_brand, cr3_list_brand, top_list_brand, _ = _brand_cr(brands_for_cr)
+    # §4.3 类目分布：深入合并表（与 §5 竞品矩阵同一细类划分，非搜索列表行）
+    cm_structure = _category_mix(merged_rows, top_k=12)
+    min_brand_rows = max(5, int(0.02 * n_structure)) if n_structure else 5
+
+    brands_deep = [
+        _cell(r, MERGED_FIELD_TO_CSV_HEADER["detail_brand"], "detail_brand")
+        for r in merged_rows
+        if _cell(r, MERGED_FIELD_TO_CSV_HEADER["detail_brand"], "detail_brand")
+    ]
+    cr1_deep, cr3_deep, top_brand_deep, _top_share_deep = _brand_cr(brands_deep)
+    cr1_hints = (
+        cr1_shop if list_export and cr1_shop is not None else cr1_deep
+    )
+
+    pst_merged = _price_stats_extended(_collect_prices(merged_rows))
+    pst_list = (
+        _price_stats_extended(_collect_prices(search_export_rows))
+        if list_export
+        else {}
+    )
+    # 价格分析（§2 要点、§6、策略提示）：优先「列表全量」；无列表或无解析价时再用合并表深入样本
+    pst = (
+        pst_list
+        if list_export and pst_list.get("n", 0) > 0
+        else pst_merged
+    )
+    price_analysis_basis_cn = (
+        f"PC 搜索列表导出共 **{len(search_export_rows)}** 行中的展示价（标价/券后等）"
+        if list_export and pst_list.get("n", 0) > 0
+        else f"已深入抓取的 **{n_sku}** 个 SKU 合并数据中的展示价"
+    )
+    promo_rows = (
+        search_export_rows
+        if list_export and pst_list.get("n", 0) > 0
+        else merged_rows
+    )
+    promo_sig = _analyze_price_promotions(promo_rows)
+
+    hits = _comment_keyword_hits(comment_rows, focus_words)
+    if not hits:
+        blob = _merge_comment_previews(merged_rows)
+        for w in focus_words:
+            if len(w) < 2:
+                continue
+            n = blob.count(w)
+            if n:
+                hits[w] += n
+
+    comment_texts, comment_scores = _iter_comment_text_units_and_scores(
+        comment_rows, merged_rows
+    )
+    sentiment_lex = _comment_sentiment_lexicon(comment_texts, comment_scores)
+    scen_counts, scen_n_texts = _comment_scenario_counts(
+        comment_texts, scenario_groups
+    )
+
+    feedback_groups = _consumer_feedback_by_matrix_group(
+        merged_rows=merged_rows,
+        comment_rows=comment_rows,
+        sku_header=sku_header,
+    )
+    matrix_groups_for_exec = _merged_rows_grouped_for_matrix(merged_rows)
+    multi_feedback_cat = len(matrix_groups_for_exec) >= 2
+
+    (
+        api_rc,
+        api_list_kw,
+        api_rc_uniques,
+        api_raw_json_n,
+        api_rc_n_values,
+    ) = _pc_search_result_count_from_raw(run_dir)
+
+    has_external_market = bool(external_rows)
+
+    lines: list[str] = [
+        f"# 竞品分析报告（京东 PC 渠道）",
+        "",
+        f"> **监测主题**：{keyword}  ",
+        f"> **数据批次**：{batch}  ",
+        f"> **报告生成**：自动化草稿，**仅供内部研讨**，不构成市场承诺或投资建议。",
+        "",
+        "---",
+        "",
+        "## 一、研究范围、数据来源与局限",
+        "",
+        "### 1.1 研究范围",
+        "",
+        f"- **搜索关键词**：「{keyword}」",
+        f"- **分析对象**：本次采集流程选取的 **{n_sku}** 个 SKU（搜索排序靠前子样本，非全站普查）。",
+    ]
+    if n_sku:
+        n_sku_nop = n_sku - n_sku_pathed
+        n_sku_unparsed = n_sku_pathed - n_sku_matrix
+        lines.append(
+            f"- **细类分析范围**：**{n_sku_matrix}** 个 SKU 具备参与**第五至第八章**分析所需的**商品详情页类目路径**"
+            f"（且能读出常见细类名称，如饼干、挂面等）；另有 **{n_sku_nop}** 个商品缺少该信息、"
+            f"**{n_sku_unparsed}** 个虽有路径但读不出细类名称，**未纳入**细类矩阵与按细类的评价统计。"
+        )
+    if meta:
+        lines.append(
+            f"- **搜索列表页**：逻辑第 **{meta.get('page_start')}** 页至第 **{meta.get('page_to')}** 页；"
+            f"搜索导出共 **{meta.get('pc_search_export_rows', '—')}** 行（含未深入拉详情的商品）。"
+        )
+    lines.extend(
+        [
+            "",
+            "### 1.2 数据来源",
+            "",
+            "- **渠道**：京东 PC 端公开商品列表、商详与评价等可访问数据。",
+            "- **可追溯**：原始表格与接口响应保存在本批次任务输出目录，供内部复核；对外分享请脱敏。",
+            "",
+            "### 1.3 方法说明（指标含义）",
+            "",
+            "- **价格**：自页面「标价 / 券后价 / 详情价」等抽取的**展示价**，含促销与规格差异，**不等于**出厂价或成本。**第六章** 在具备可用的搜索列表导出时，优先以**列表全量**统计；否则使用**已深入 SKU** 的合并数据；**第六章第一节** 归纳标价与券后价差等**列表侧展示价差信号**（不对卖点/腰带字段做预设关键词扫描）。",
+            "- **品牌/店铺集中度（第四章）**：有列表全量时按列表行计店铺与品牌占比；无列表导出时按深入 SKU 合并表估算。",
+            "- **评价主题词**：对评价正文做**预设词表子串计数**，非分词主题模型，适合扫方向，**需抽样人工验证**。",
+            "- **用途/场景**：对每条评价独立判断是否命中预设场景词；一条可计入多个场景，统计的是「提及该场景的评价条数」而非用户数。",
+            (
+                "- **用户画像（第八章）**：正负面粗判含**口语短语**级摘录；**第八章第三节**另用分词、词频与主题模型等对评论做**补充分析**（与**第八章第二节**条形图口径不同、互为补充），可选词云并由大模型归纳要点。"
+                if _ch8_probe_sec
+                else "- **用户画像（第八章）**：正负面粗判含**口语短语**级摘录；关注词与场景**仅按细类**以**同图左右并列**展示（左为关注词命中次数，右为场景占有效文本 **%**）；见**第八章第三节**。"
+            ),
+            "- **细类划分（第五至第八章）**：**仅**依据合并表中的**商品详情页类目路径**；该信息缺失或无法读出细类名称的 SKU **不参与**竞品矩阵与按细类评价统计（相关评价条亦**不进入**按细类图表）。",
+            "- **检索结果规模**：来自京东 PC 搜索返回的「结果条数」类指标，表示平台侧申报的匹配数量级，**不等于**动销、库存或独立 SKU 数。",
+            "",
+            "### 1.4 主要局限",
+            "",
+            "- 仅覆盖 **京东 PC**，不含天猫、抖音、线下、B2B 原料端。",
+            "- 样本量由本次抓取上限与搜索页数决定，**结论外推需谨慎**。",
+            "- 详情配料与宣称以页面展示为准，**与真实配方可能不一致**（合规与实测另议）。",
+            (
+                "- **行业零售额、TAM、CAGR 等**：无法从本批次数据推导；本报告已纳入任务中配置的第三方摘录，见 **第三章第五节**。"
+                if has_external_market
+                else "- **行业零售额、TAM、CAGR 等**：无法从本批次数据推导；本报告未纳入外部摘录（可在任务报告调参中维护市场信息表）。"
+            ),
+            "",
+            "---",
+            "",
+            "## 二、执行摘要（要点）",
+            "",
+        ]
+    )
+
+    exec_bullets: list[str] = []
+    exec_bullets.append(
+        f"在关键词「{keyword}」下，本次深入分析 **{n_sku}** 个 SKU，关联评价文本 **{n_cmt}** 条。"
+    )
+    if list_export and cr1_shop is not None and top_shop_s:
+        src = f"列表全量 **{n_structure}** 行"
+        if cr3_shop is not None:
+            exec_bullets.append(
+                f"竞争结构（{src}，第四章）：**店铺** 第一大店铺份额 ≈ **{100 * cr1_shop:.1f}%**（「{top_shop_s}」），"
+                f"前三店铺合计份额 ≈ **{100 * cr3_shop:.1f}%**（按列表行计，同一 SKU 多行会重复计）。"
+            )
+        else:
+            exec_bullets.append(
+                f"竞争结构（{src}，第四章）：**店铺** 第一大店铺份额 ≈ **{100 * cr1_shop:.1f}%**（「{top_shop_s}」）。"
+            )
+    elif not list_export and cr1_deep is not None and top_brand_deep:
+        if cr3_deep is not None:
+            exec_bullets.append(
+                f"竞争结构（无列表导出，第四章用深入合并表）：**品牌** 第一大品牌份额 ≈ **{100 * cr1_deep:.1f}%**（「{top_brand_deep}」），"
+                f"前三品牌合计份额 ≈ **{100 * cr3_deep:.1f}%**。"
+            )
+        else:
+            exec_bullets.append(
+                f"竞争结构（无列表导出，第四章用深入合并表）：**品牌** 第一大品牌份额 ≈ **{100 * cr1_deep:.1f}%**（「{top_brand_deep}」）。"
+            )
+    if (
+        list_export
+        and len(brands_for_cr) >= min_brand_rows
+        and cr1_list_brand is not None
+        and top_list_brand
+    ):
+        if cr3_list_brand is not None:
+            exec_bullets.append(
+                f"同批列表中**品牌信息有效** **{len(brands_for_cr)}** 条：**品牌** 第一大品牌份额 ≈ **{100 * cr1_list_brand:.1f}%**（「{top_list_brand}」），"
+                f"前三品牌合计份额 ≈ **{100 * cr3_list_brand:.1f}%**。"
+            )
+        else:
+            exec_bullets.append(
+                f"同批列表中**品牌信息有效** **{len(brands_for_cr)}** 条：**品牌** 第一大品牌份额 ≈ **{100 * cr1_list_brand:.1f}%**（「{top_list_brand}」）。"
+            )
+    elif list_export and cr1_deep is not None and top_brand_deep and not brands_for_cr:
+        exec_bullets.append(
+            f"列表导出缺少品牌标题字段，**深入 {n_sku} SKU** 商详品牌第一大品牌份额 ≈ **{100 * cr1_deep:.1f}%**（「{top_brand_deep}」），供与第五章矩阵对照。"
+        )
+    if pst:
+        price_src_short = (
+            "（列表全量）"
+            if list_export and pst_list.get("n", 0) > 0
+            else "（深入样本）"
+        )
+        exec_bullets.append(
+            f"展示价格{price_src_short}：可解析价格 **{pst['n']}** 个观测，区间约 **{pst['min']:.2f}～{pst['max']:.2f}** 元，"
+            f"中位数 **{pst.get('median', pst['mean']):.2f}** 元。"
+        )
+        wb = int(promo_sig.get("rows_with_both_list_and_coupon") or 0)
+        sh = promo_sig.get("share_coupon_below_list_when_both")
+        med = promo_sig.get("median_discount_pct_when_coupon_below")
+        if wb >= 3 and isinstance(sh, (int, float)) and sh >= 0.08 and med is not None:
+            exec_bullets.append(
+                f"列表侧约 **{100.0 * float(sh):.0f}%** 可对齐行呈现「券后/到手」**低于**「标价」，展示价差中位数约 **{float(med):.1f}%**（**第六章第一节** 活动与话术摘录）。"
+            )
+    if multi_feedback_cat and (hits or scen_n_texts > 0):
+        exec_bullets.append(
+            "评价侧写（关注词、用途/场景）已按**第五章同一细类划分**分节，见**第八章第三节**（同图并列）。"
+        )
+    elif hits:
+        top3 = "、".join(f"「{w}」({n})" for w, n in hits.most_common(3))
+        exec_bullets.append(f"评价侧写（词频）：{top3}。")
+    if scen_n_texts > 0 and scen_counts and not multi_feedback_cat:
+        top_s = scen_counts.most_common(4)
+        frag = "；".join(f"{lbl} **{n}** 条" for lbl, n in top_s)
+        exec_bullets.append(f"用途/场景（评价自述，可多选）：{frag}（有效文本 **{scen_n_texts}** 条）。")
+    if api_rc is not None:
+        exec_bullets.append(
+            f"PC 搜索返回的检索结果规模约 **{api_rc:,}**（站内匹配条数量级，见第三章第二节；**不是**零售额或动销统计）。"
+        )
+    for b in exec_bullets:
+        lines.append(f"- {b}")
+    if not exec_bullets:
+        lines.append("- 当前批次可汇总要点较少（以正文各节实际输出为准）。")
+
+    proxy = _search_list_proxies(search_export_rows) if search_export_rows else {}
+    lines.extend(["", "---", "", "## 三、整体市场观察（渠道可见度参考，非官方市场规模）", ""])
+    lines.extend(
+        [
+            "### 3.1 与「市场规模」的区别",
+            "",
+            "- **官方/行业市场规模**（如全国零售额、品类增速、渗透率）通常来自 **Euromonitor、行业协会、上市公司年报、券商研报** 等；**不能**用京东搜索返回条数或 SKU 数直接等同。",
+            "- **第三章第二节** 使用搜索接口返回的**结果条数**；**第三章第三、四节** 描述本次导出的列表行、去重 SKU/店铺及列表价，仅作**参照**，外推全市场需谨慎。",
+            "",
+            "### 3.2 接口返回的检索规模",
+            "",
+        ]
+    )
+    if api_rc is not None:
+        lines.append(
+            f"- 根据本批次保存的搜索原始响应解析：监测词「**{keyword}**」下，平台申报的检索匹配规模约 **{api_rc:,}**。"
+        )
+        if api_list_kw:
+            lines.append(
+                f"- 同批响应中的列表关键词：**{api_list_kw}**（可与监测词对照是否一致）。"
+            )
+        if len(api_rc_uniques) > 1:
+            nums = "、".join(f"{u:,}" for u in api_rc_uniques)
+            lines.append(
+                f"- 注：多份原始响应中该规模字段曾出现不同取值（{nums}），正文取**众数** **{api_rc:,}**（共 {api_rc_n_values} 次有效读取）。"
+            )
+        elif api_raw_json_n > 0:
+            lines.append(
+                f"- 已扫描 **{api_raw_json_n}** 份原始响应并完成读取。"
+            )
+        lines.extend(
+            [
+                "- **含义**：平台对该关键词给出的**检索匹配条数量级**，用于感受站内商品池「宽度」；可能含不同类目/规格条目，**不等于**独立 SKU 数、动销或 GMV，且会随索引与运营策略变化。",
+                "",
+            ]
+        )
+    else:
+        lines.append(
+            "*未能从本批次搜索原始响应中解析到有效的检索规模字段（目录缺失、无可用响应或字段为空）。*"
+        )
+        lines.append("")
+
+    lines.extend(["### 3.3 搜索列表规模（本次抓取范围内的可见 SKU / 店铺）", ""])
+    if proxy.get("total_rows", 0) > 0:
+        pmin, pmax = proxy.get("page_span") or (None, None)
+        span_txt = (
+            f"页码（去重）约 **{pmin}～{pmax}** 页"
+            if pmin is not None and pmax is not None
+            else "页码字段缺失或无法解析"
+        )
+        lines.extend(
+            [
+                f"- **列表导出行数**：**{proxy['total_rows']}** 行。",
+                f"- **去重 SKU 数**：**{proxy['unique_skus']}**；**去重店铺数**：**{proxy['unique_shops']}**；{span_txt}。",
+                f"- **列表中去重叶子类目代码/片段数**（粗略）：**{proxy['unique_leaf_cats']}**（同一关键词下品类宽度的参考）。",
+                "",
+            ]
+        )
+        lpst = proxy.get("list_price_stats") or {}
+        lines.extend(["### 3.4 列表端展示价（全导出，非仅深入样本）", ""])
+        if lpst:
+            lines.extend(
+                [
+                    f"- 自列表「标价 / 券后价」解析到 **{lpst['n']}** 个数值价；"
+                    f"区间约 **{lpst['min']:.2f}～{lpst['max']:.2f}** 元，"
+                    f"中位数 **{float(lpst.get('median', lpst['mean'])):.2f}** 元。",
+                    "- **说明**：第六章价格统计表已与上表同源（均为列表全量，条件满足时）；若正文第六章标注为合并表样本，则因无可用列表价而退化。深入 SKU 的详情价可与列表价对照。",
+                    "",
+                ]
+            )
+        else:
+            lines.append("*列表导出中未能解析出数值价格。*")
+            lines.append("")
+    else:
+        lines.append(
+            "*未读到可用的搜索列表导出或文件为空；第三章第三、四节无列表侧数据。*"
+        )
+        lines.append("")
+        lines.extend(["### 3.4 列表端展示价（全导出）", "", "*无列表数据。*", ""])
+
+    if external_rows:
+        lines.extend(
+            [
+                "### 3.5 外部市场规模与行业信息（运行配置摘录）",
+                "",
+                "以下为本次任务报告调参中维护的**第三方市场摘录**，可与第三章第二节检索规模及第三章第三、四节列表参照对照使用；**指标含义与真实性以原出处为准**。",
+                "",
+                "| 指标 | 数值与说明 | 来源 | 年份 |",
+                "| --- | --- | --- | --- |",
+            ]
+        )
+        for a, b, c, d in external_rows:
+            lines.append(
+                f"| {_md_cell(a, 40)} | {_md_cell(b, 48)} | {_md_cell(c, 36)} | {_md_cell(d, 12)} |"
+            )
+        lines.append("")
+
+    ch4_heading = (
+        "## 四、市场与竞争结构（PC 搜索列表全量）"
+        if list_export
+        else "## 四、市场与竞争结构（深入合并表 · 无列表导出）"
+    )
+    lines.extend(["", "---", "", ch4_heading, ""])
+    if list_export:
+        lines.append(
+            f"基于**搜索列表导出**共 **{n_structure}** 行，与第三章第三节一致；"
+            f"集中度按**列表行**计数（同一 SKU 多次曝光则重复计）。"
+        )
+    else:
+        lines.append(
+            f"*未读到可用列表全量行，以下退化为**深入 SKU 合并样本** **{n_structure}** 行。*"
+        )
+    lines.append("")
+
+    lines.extend(["### 4.1 品牌分布与集中度", ""])
+    brand_rows_n = len(brands_for_cr)
+    show_list_brand_cr = list_export and brand_rows_n >= min_brand_rows
+    show_merged_brand_cr = not list_export and brand_rows_n > 0
+    if (show_list_brand_cr or show_merged_brand_cr) and cr1_list_brand is not None:
+        lines.extend(
+            _embed_chart(
+                run_dir,
+                "chart_brand_rows_pie.png",
+                "品牌列表曝光占比（扇形图；按整理后的品牌名计数，与结构化摘要中的品牌占比统计一致；"
+                "长尾并入「（其余品牌）」；扇形内再合并为「其他」）",
+            )
+        )
+        lines.extend(
+            _lines_4_reading_brand(
+                cr1=cr1_list_brand,
+                cr3=cr3_list_brand,
+                top=top_list_brand or "",
+                brand_rows_n=brand_rows_n,
+                n_structure=n_structure,
+            )
+        )
+        lines.append(
+            "*更细的品牌行数分布见本任务「结构化摘要」数据包。*"
+        )
+    elif list_export:
+        lines.append(
+            f"*列表导出中店铺/品牌标题有效 **{brand_rows_n}** 条，"
+            f"低于建议阈值（≥{min_brand_rows}），品牌集中度未展开。**店铺结构见第四章第二节**；"
+            f"商详品牌在**第五章**。*"
+        )
+    else:
+        lines.append("*深入子样本无可用品牌字段。*")
+    lines.append("")
+
+    lines.extend(["### 4.2 店铺分布与集中度", ""])
+    shop_rows_n = len(shops_for_cr)
+    if shop_rows_n:
+        lines.extend(
+            _embed_chart(
+                run_dir,
+                "chart_shop_rows_pie.png",
+                "店铺列表曝光占比（扇形图；按整理后的店铺名计数，与结构化摘要中的店铺占比统计一致；"
+                "长尾并入「（其余店铺）」；扇形内再合并为「其他」）",
+            )
+        )
+        lines.extend(
+            _lines_4_reading_shop(
+                cr1=cr1_shop,
+                cr3=cr3_shop,
+                top=top_shop_s or "",
+                shop_rows_n=shop_rows_n,
+                n_structure=n_structure,
+            )
+        )
+        lines.append(
+            "*更细的店铺行数分布见本任务「结构化摘要」数据包。*"
+        )
+    else:
+        lines.append("*无店铺字段。*")
+    lines.append("")
+
+    lines.extend(["### 4.3 细分类目分布（深入合并表 · 与第五章矩阵同一细类划分）", ""])
+    if cm_structure and n_sku_matrix > 0:
+        lines.extend(
+            _embed_chart(
+                run_dir,
+                "chart_category_mix_pie.png",
+                "细类标签分布（扇形图；依据合并表中的商品详情页类目路径，与第五章一致；"
+                "Top 12 以外的细类在统计时并入「（其余细类）」；扇形图内再合并为「其他」）",
+            )
+        )
+        lines.append(
+            "*完整类目分布见界面「数据摘要」或简报包中的数据文件。*"
+        )
+    else:
+        lines.append(
+            "*深入合并表中无具备可解析商品详情页类目路径的 SKU，本小节不展示扇形图；请核对商详抓取与合并字段。*"
+        )
+    lines.append("")
+
+    lines.extend(
+        [
+            "---",
+            "",
+            "## 五、竞品对比矩阵（按细分类目分组）",
+            "",
+            "分组**仅**依据合并表中的**商品详情页类目路径**（京东商详中的类目层级）：**三级路径**取中间一段（如 … > **饼干** > 粗粮饼干），"
+            "**四级及以上**取倒数第二段（如 … > **面条** > 挂面）。**路径缺失**或各段均为内部编码、**读不出常见细类名称**的 SKU **不进入**本矩阵，亦**不参与**第八章按细类的评价统计。",
+            "",
+            "**读图方式**：每个细类下为**并列横向条形图**（左：**展示价**（元）；右：**销量**（来自搜索列表页「已售」等销量文案，如「已售50万+」计为 **50 万**）），"
+            "纵轴为**产品标题**（与本节各附图一致）。**SKU、店铺、配料与评价摘要等明细不列入正文**，详见本批次导出的合并数据表。",
+            "",
+        ]
+    )
+    grouped_matrix = _merged_rows_grouped_for_matrix(merged_rows)
+    if not grouped_matrix:
+        if merged_rows:
+            lines.append(
+                "*深入合并表有条目，但均无可用商品详情页类目路径（或路径无法解析为可读细类），故无法生成细类矩阵；"
+                "第五至第八章中依赖矩阵的按细类统计相应为空。请核对商详抓取与合并字段。*"
+            )
+        else:
+            lines.append("*无合并表 SKU。*")
+        lines.append("")
+    for gi, (gname, grows) in enumerate(grouped_matrix):
+        lines.append(f"### {gname}（**{len(grows)}** 款）")
+        lines.append("")
+        mx_chart = _matrix_prices_sales_chart_filename(gname, gi)
+        lines.extend(
+            _embed_chart(
+                run_dir,
+                mx_chart,
+                f"「{_md_cell(gname, 20)}」· 展示价与销量（页面「已售」销量文案）；纵轴为产品标题。",
+            )
+        )
+        if not (run_dir / "report_assets" / mx_chart).is_file():
+            lines.append(
+                f"*（尚未生成 ``report_assets/{mx_chart}``：请确认已执行报告出图流程，或重新生成报告。）*"
+            )
+            lines.append("")
+        lines.append("")
+
+    _llm_mx = (llm_matrix_section_md or "").strip()
+    if _llm_mx:
+        lines.extend(
+            [
+                "",
+                "#### 细类要点归纳（大模型，与上文条形图互补）",
+                "",
+                "> **说明**：与第五章相同的细类划分下归纳卖点与配料共性；**具体 SKU、价格与条形图以正文为准**，SKU 级明细见合并表 CSV。",
+                "",
+                _llm_mx,
+                "",
+            ]
+        )
+
+    ch6_price_title = (
+        "## 六、价格分析（PC 搜索列表全量）"
+        if list_export and pst_list.get("n", 0) > 0
+        else "## 六、价格分析（深入 SKU 合并表 · 无可用列表价或未导出列表）"
+    )
+    lines.extend(["---", "", ch6_price_title, ""])
+    lines.append(f"- **统计基础**：{price_analysis_basis_cn}。")
+    if (
+        list_export
+        and pst_list.get("n", 0) > 0
+        and pst_merged.get("n", 0) > 0
+    ):
+        lines.append(
+            f"- **对照**：合并表深入样本可解析价 **{pst_merged['n']}** 个观测，中位数约 **{float(pst_merged.get('median', pst_merged['mean'])):.2f}** 元（与上表样本范围不同，仅作对照）。"
+        )
+    lines.append("")
+    if pst:
+        price_tbl = [
+            "| 统计量 | 数值（元） | 说明 |",
+            "| --- | --- | --- |",
+            f"| 样本量 | {pst['n']} | 与统计基础一致 |",
+            f"| 最小值 | {pst['min']:.2f} | |",
+        ]
+        if "q1" in pst:
+            price_tbl.append(f"| 下四分位 Q1 | {float(pst['q1']):.2f} | |")
+        else:
+            price_tbl.append("| 下四分位 Q1 | — | 样本不足 4 个 |")
+        price_tbl.append(
+            f"| 中位数 | {float(pst.get('median', pst['mean'])):.2f} | |"
+        )
+        if "q3" in pst:
+            price_tbl.append(f"| 上四分位 Q3 | {float(pst['q3']):.2f} | |")
+        else:
+            price_tbl.append("| 上四分位 Q3 | — | 样本不足 4 个 |")
+        price_tbl.extend(
+            [
+                f"| 最大值 | {pst['max']:.2f} | |",
+                f"| 均值 | {pst['mean']:.2f} | |",
+            ]
+        )
+        if "stdev" in pst:
+            price_tbl.append(f"| 标准差 | {pst['stdev']:.2f} | 离散程度 |")
+        lines.extend(price_tbl)
+        lines.append("")
+        lines.append(
+            "**解读提示**：价差大通常反映规格、组合装、品牌溢价或促销差异；B 端定价策略需结合成本与渠道单独建模。"
+        )
+        lines.append("")
+        lines.extend(_markdown_price_promotion_section(promo_sig))
+    else:
+        lines.append("*当前样本无可用数值价格，本节不展开统计表。*")
+        lines.append("")
+        lines.extend(_markdown_price_promotion_section(promo_sig))
+    lines.append("")
+
+    _llm_pr = (llm_price_groups_section_md or "").strip()
+    if _llm_pr:
+        lines.extend(
+            [
+                "",
+                "#### 细类价盘要点归纳（大模型，与第六章量化表互补）",
+                "",
+                "> **说明**：侧重价带与标价/券后关系的可读叙述；**数值以正文分位数表为准**。",
+                "",
+                _llm_pr,
+                "",
+            ]
+        )
+
+    _llm_po = (llm_promo_groups_section_md or "").strip()
+    if _llm_po:
+        lines.extend(
+            [
+                "",
+                "#### 细类促销与活动要点归纳（大模型，与第六章第一节及价盘互补）",
+                "",
+                "> **说明**：依据合并表「促销摘要」及榜单相关字段（如「榜单排名」「榜单类文案」）等**页面展示摘录**；"
+                "不采用列表「卖点/腰带」类字段作归纳依据（多为固定词表匹配，口径偏粗）。归纳券/补贴/新人/榜单曝光等活动形态，**不**替代第五章的配料/宣称归纳。**具体以页面与 CSV 为准**。",
+                "",
+                _llm_po,
+                "",
+            ]
+        )
+
+    _sm_score = sentiment_lex.get("method") == "score_then_lexeme"
+    _sec82_title = (
+        "### 8.2 评价正负面粗判（评分优先 + 关键词回退）"
+        if _sm_score
+        else "### 8.2 评价正负面粗判（关键词规则）"
+    )
+    _sec82_block: list[str] = [
+        "---",
+        "",
+        "## 八、消费者反馈与用户画像（按细分类目）",
+        "",
+        "### 8.1 方法",
+        "",
+        "- **细类划分**：与**第五章「竞品矩阵」**相同，**仅**依据合并表中的**商品详情页类目路径**解析为「饼干 / 西式糕点 / …」等（规则见第五章开头说明）。",
+        "- **归因**：每条评价按其 SKU 对应到深入样本，再映射到该 SKU 所属细类；SKU 不在合并表中的评价单独归入说明性分组；**在合并表中但该 SKU 缺少类目路径或读不出细类名称的，该评价不进入按细类统计**（与第五章**同一条排除规则**）。",
+        "- **正负面粗判（第八章第二节）**：若评价含有效「评分」列则**先按星级**粗分正负与中评，再在对应子集内统计口语短语；无评分时仍按关键词子串；若任务开启**大模型评价情感分析**，可附**大模型对抽样原文的主题归因**，与条形图互补。",
+        (
+            "- **文本补充分析（第八章第三节）**：本任务已用中文分词与统计工具做了开放词表分析（词频、关键词突出度、词对共现、主题归纳等，可选词云），与**第八章第二节**规则词表条形图**不同**、**互补**；**不再**输出原「关注词次数 + 场景占比」左右并列条图。"
+            if _ch8_probe_sec
+            else "- **关注词与使用场景（第八章第三节）**：对组内评价正文做关注词子串计数（左栏条形图）；对每条有效文本独立扫描**本次任务生效的场景词组**（来自报告调参或系统默认），一条可属多场景，右栏为**占该细类有效文本比例 %**（多标签下可相加 **>** 100%）。二者在 **同一张图左右并列**，与第五章矩阵细类一一对应。"
+        ),
+        "",
+        _sec82_title,
+        "",
+        f"- **有效文本条数**：{sentiment_lex.get('text_units', 0)}（与第八章第一节**归因规则**一致）。",
+    ]
+    if _sm_score:
+        _sec82_block.append(
+            "- **正负面粗判规模**：本批存在有效「评分」时——**1～2 星**计为偏负向，**4～5 星**计为偏正向，**3 星**计为中评，**空文本**计为中性；"
+            "无评分的条仍按关键词子串划分；「混合」仅在**无评分**且同条兼含正/负关键词时出现。"
+        )
+    _sec82_block.extend(
+        [
+            f"- **偏正向**：{sentiment_lex.get('positive_only', 0)} 条"
+            + ("（主要为 4～5 星）" if _sm_score else "（仅命中正向词表）")
+            + "；"
+            f"**偏负向**：{sentiment_lex.get('negative_only', 0)} 条"
+            + ("（主要为 1～2 星）" if _sm_score else "（仅命中负向词表）")
+            + "；"
+            f"**混合**：{sentiment_lex.get('mixed_positive_and_negative', 0)} 条"
+            + ("（无评分且同条兼含正/负关键词）" if _sm_score else "（同条兼含正/负词）")
+            + "；"
+            f"**中性或空文本**：{sentiment_lex.get('neutral_or_empty', 0)} 条"
+            + ("（含 3 星中评及无关键词命中）" if _sm_score else "")
+            + "。",
+            "- **说明**："
+            + (
+                "星级与正文可能不一致（如五星长文吐槽）；口语短语条形图仅在对应星级子集内统计；正式结论请**人工抽样**阅读原文。"
+                if _sm_score
+                else "词表为方向性粗判，讽刺、省略与错别字会导致误判；正式结论请**人工抽样**阅读原文。"
+            ),
+        ]
+    )
+    lines.extend(_sec82_block)
+    _scope = (sentiment_lex.get("lexeme_scope_note") or "").strip()
+    if _scope:
+        lines.append(f"- **词根统计说明**：{_scope}")
+    lines.extend(["", ""])
+    lines.extend(
+        _embed_chart(
+            run_dir,
+            "chart_sentiment_overview_pie.png",
+            "评价正负面粗判规模（扇形图；与上表条数一致）",
+        )
+    )
+    lines.extend(
+        _embed_chart(
+            run_dir,
+            "chart_positive_lexemes_bar.png",
+            (
+                "正向评价里**最常出现的口语短语**（在 **4～5 星** 评价条内统计；条形图）"
+                if _sm_score
+                else "正向评价里**最常出现的口语短语**（在偏正向或混合评价条内统计；条形图）"
+            ),
+        )
+    )
+    lines.extend(
+        _embed_chart(
+            run_dir,
+            "chart_negative_lexemes_bar.png",
+            (
+                "负向评价里**最常出现的口语短语**（在 **1～2 星** 评价条内统计；条形图）"
+                if _sm_score
+                else "负向评价里**最常出现的口语短语**（在偏负向或混合评价条内统计；条形图）"
+            ),
+        )
+    )
+    pos_h = sentiment_lex.get("positive_tone_lexeme_hits") or []
+    neg_h = sentiment_lex.get("negative_tone_lexeme_hits") or []
+    if pos_h:
+        frag = "；".join(
+            f"「{x.get('word', '')}」{x.get('texts_matched', 0)} 条"
+            for x in pos_h[:6]
+            if isinstance(x, dict)
+        )
+        lines.append(f"- **正向语境高频短语（摘要）**：{frag}。")
+    if neg_h:
+        frag_n = "；".join(
+            f"「{x.get('word', '')}」{x.get('texts_matched', 0)} 条"
+            for x in neg_h[:6]
+            if isinstance(x, dict)
+        )
+        lines.append(f"- **负向语境高频短语（摘要）**：{frag_n}。")
+    _llm_s = (llm_sentiment_section_md or "").strip()
+    if _llm_s:
+        lines.extend(
+            [
+                "",
+                "#### 大模型深入解读（主题归因，与词频统计互补）",
+                "",
+                "> **说明**：基于与上节**同一套评分优先或关键词归类规则**抽样的评价原文，由大模型归纳**用户在说什么**（尤其是负向的具体事由），与上列条数、条形图**互补**；引文以原评论为准。",
+                "",
+                _llm_s,
+            ]
+        )
+    lines.append("")
+    if _ch8_probe_sec:
+        lines.extend(
+            [
+                "### 8.3 评论文本补充分析（词频、关键词与共现、主题归纳）",
+                "",
+                "> **说明**：与**第八章第二节**口语短语条形图（规则词表）**口径不同**、**互补**；**不再**输出本章原「关注词 + 场景」左右并列条图；插图位于本批次报告附图文件夹中。",
+                "",
+                _ch8_probe_sec,
+                "",
+            ]
+        )
+    else:
+        # 仅当未嵌入第八章第三节补充分析（_ch8_probe_sec 为空）时：原「关注词 + 场景」条图与逐细类段落
+        lines.extend(
+            [
+                "### 8.3 关注词与使用场景（按细类）",
+                "",
+                "每细类一张**左右并列图**（与报告附图文件夹中的 ``chart_focus_and_scenarios_bar__*.png`` 同源）："
+                "**左**为配置关注词子串命中次数（同一评价可出现多次，为次数而非去重条数）；"
+                "**右**为预设场景词组命中占该细类有效文本比例 %（一条可属多场景；多柱比例可相加 **>** 100%）。"
+                "统计均基于评价正文（或兜底预览）子串规则，**不等于**购买动机调研结论。",
+                "",
+            ]
+        )
+        if not feedback_groups:
+            lines.append("*无评价数据可归组。*")
+            lines.append("")
+        else:
+            for gi, (gname, cr_g, texts_g) in enumerate(feedback_groups):
+                n_flat = len(cr_g)
+                lines.append(f"#### {gname}")
+                lines.append("")
+                lines.append(
+                    f"- **本细类逐条评价**：{n_flat} 条；**用于统计的有效文本条数**：{len(texts_g)}。"
+                )
+                lines.append("")
+                hits_g = _group_keyword_hits(cr_g, texts_g, focus_words=focus_words)
+                scen_g, scen_ng = _comment_scenario_counts(texts_g, scenario_groups)
+                has_focus = any(n > 0 for n in hits_g.values()) if hits_g else False
+                has_scen = scen_ng > 0 and any(n > 0 for n in scen_g.values())
+                if scen_ng <= 0:
+                    lines.append("*该细类下无可用评价正文。*")
+                    lines.append("")
+                    continue
+                if has_focus or has_scen:
+                    cap = (
+                        f"「{_md_cell(gname, 24)}」细类 · 关注词与使用场景（左：关注词命中次数；右：场景占有效文本 %；"
+                        f"有效文本 **{scen_ng}** 条）"
+                    )
+                    lines.extend(
+                        _embed_chart(
+                            run_dir,
+                            _focus_scenario_combo_bar_filename(gname, gi),
+                            cap,
+                        )
+                    )
+                else:
+                    lines.append("*该细类无关注词命中且未命中预设场景词组。*")
+                    lines.append("")
+                if has_scen:
+                    for para in _scenario_summary_bullets(scen_g, scen_ng):
+                        lines.append(para)
+                        lines.append("")
+                elif scen_ng > 0:
+                    lines.append("*未命中预设场景词组。*")
+                    lines.append("")
+
+        _llm_sg = (llm_scenario_groups_section_md or "").strip()
+        if _llm_sg:
+            lines.extend(
+                [
+                    "",
+                    "#### 使用场景要点归纳（大模型，与第八章第三节右栏图表互补）",
+                    "",
+                    "> **说明**：与第八章第三节**相同**的预设场景词组与子串命中规则；**各场景条数与占比以正文图右栏为准**。",
+                    "",
+                    _llm_sg,
+                    "",
+                ]
+            )
+
+        _llm_cg = (llm_comment_groups_section_md or "").strip()
+        if _llm_cg:
+            lines.extend(
+                [
+                    "",
+                    "#### 细类评价与关注词要点归纳（大模型，与第八章第三节左栏图表互补）",
+                    "",
+                    "> **说明**：归纳各细类反馈主题与配置关注词命中；**次数与第八章第三节图左栏以正文为准**。",
+                    "",
+                    _llm_cg,
+                    "",
+                ]
+            )
+
+    lines.extend(["---", "", "## 九、策略与机会提示（假设清单，待验证）", ""])
+    _llm_st = (llm_strategy_opportunities_section_md or "").strip()
+    if _llm_st:
+        lines.extend(
+            [
+                "基于本任务结构化摘要（价盘、集中度、评价与场景、促销信号等）的**假设性策略归纳**；数字与明细以前文及 CSV 为准，定稿前请结合贵司成本、渠道与合规复核。",
+                "",
+                "#### 策略与机会建议（大模型）",
+                "",
+                _llm_st,
+                "",
+            ]
+        )
+    else:
+        lines.extend(
+            [
+                "未生成本节大模型正文：请在任务 `report_config` 中开启 `llm_strategy_opportunities` 并重跑产物，或检查 run 目录下 `strategy_opportunities_llm.json` 是否报错。",
+                "",
+            ]
+        )
+
+    lines.extend(
+        [
+            "---",
+            "",
+            "## 附录 A：数据留存说明",
+            "",
+            "- 本批次**任务输出目录**内保存：搜索列表导出、深入 SKU 合并表、商详与评价相关表格，以及搜索/商详原始响应与运行参数快照，供内部复核与复算。",
+            "- 对外演示或转发前请按公司规范做**脱敏**处理。",
+            "",
+            "---",
+            "",
+            "*本报告由系统自动汇总生成；定稿前请业务交叉核对数据与结论。*",
+            "",
+        ]
+    )
+    return "\n".join(lines)
+
+
+def _sanitize_json_numbers(obj: Any) -> Any:
+    """浮点 NaN/Inf 无法 JSON 序列化，统一转 None 或圆角。"""
+    if isinstance(obj, float):
+        if math.isnan(obj) or math.isinf(obj):
+            return None
+        return round(obj, 6)
+    if isinstance(obj, dict):
+        return {k: _sanitize_json_numbers(v) for k, v in obj.items()}
+    if isinstance(obj, list):
+        return [_sanitize_json_numbers(x) for x in obj]
+    return obj
+
+
+def build_competitor_brief(
+    *,
+    run_dir: Path,
+    keyword: str,
+    merged_rows: list[dict[str, str]],
+    search_export_rows: list[dict[str, str]],
+    comment_rows: list[dict[str, str]],
+    meta: dict[str, Any] | None,
+    report_config: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """
+    与 ``build_competitor_markdown`` 共用**同一套统计规则**，输出可 JSON 序列化的结构化竞品摘要（**规则驱动**，无 LLM）。
+    """
+    focus_words, scenario_groups, _ext = resolve_report_tuning(report_config)
+    sku_header = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
+    title_h = MERGED_FIELD_TO_CSV_HEADER["title"]
+    batch = _run_batch_label(run_dir)
+    n_sku = len(merged_rows)
+    n_cmt = len(comment_rows)
+    n_sku_matrix = sum(1 for r in merged_rows if _competitor_matrix_group_key(r))
+
+    list_export = len(search_export_rows) > 0
+    structure_rows = search_export_rows if list_export else merged_rows
+    n_structure = len(structure_rows)
+    shops_s = _structure_shops(structure_rows, list_export=list_export)
+    brands_s = _structure_brands(structure_rows, list_export=list_export)
+    shops_for_cr = _structure_names_for_pie_counter(shops_s)
+    brands_for_cr = _structure_names_for_pie_counter(brands_s)
+    cr1_shop, cr3_shop, top_shop_s, top_shop_share = _brand_cr(shops_for_cr)
+    cr1_list_brand, cr3_list_brand, top_list_brand, _ = _brand_cr(brands_for_cr)
+    cm_structure = _category_mix(merged_rows, top_k=12)
+    min_brand_rows = max(5, int(0.02 * n_structure)) if n_structure else 5
+
+    brands_deep = [
+        _cell(r, MERGED_FIELD_TO_CSV_HEADER["detail_brand"], "detail_brand")
+        for r in merged_rows
+        if _cell(r, MERGED_FIELD_TO_CSV_HEADER["detail_brand"], "detail_brand")
+    ]
+    cr1_deep, cr3_deep, top_brand_deep, top_brand_deep_share = _brand_cr(
+        brands_deep
+    )
+    cr1_hints = cr1_shop if list_export and cr1_shop is not None else cr1_deep
+
+    pst_merged = _price_stats_extended(_collect_prices(merged_rows))
+    pst_list = (
+        _price_stats_extended(_collect_prices(search_export_rows))
+        if list_export
+        else {}
+    )
+    pst = (
+        pst_list
+        if list_export and pst_list.get("n", 0) > 0
+        else pst_merged
+    )
+    price_stats_source = (
+        "pc_search_export_all_rows"
+        if list_export and pst_list.get("n", 0) > 0
+        else "keyword_pipeline_merged"
+    )
+    promo_rows_brief = (
+        search_export_rows
+        if list_export and pst_list.get("n", 0) > 0
+        else merged_rows
+    )
+    price_promotion_signals = _analyze_price_promotions(promo_rows_brief)
+
+    hits = _comment_keyword_hits(comment_rows, focus_words)
+    if not hits:
+        blob = _merge_comment_previews(merged_rows)
+        for w in focus_words:
+            if len(w) < 2:
+                continue
+            n = blob.count(w)
+            if n:
+                hits[w] += n
+
+    comment_texts, comment_scores = _iter_comment_text_units_and_scores(
+        comment_rows, merged_rows
+    )
+    comment_sentiment_lexicon = _comment_sentiment_lexicon(
+        comment_texts, comment_scores
+    )
+    scen_counts, scen_n_texts = _comment_scenario_counts(
+        comment_texts, scenario_groups
+    )
+
+    (
+        api_rc,
+        api_list_kw,
+        api_rc_uniques,
+        api_raw_json_n,
+        _api_rc_n_values,
+    ) = _pc_search_result_count_from_raw(run_dir)
+
+    proxy = _search_list_proxies(search_export_rows) if search_export_rows else {}
+
+    hints = _strategy_hints(
+        cr1=cr1_hints,
+        pst=pst,
+        hits=hits,
+        n_comments=n_cmt,
+        scen_counts=scen_counts,
+        scen_n_texts=scen_n_texts,
+    )
+
+    matrix_groups: list[dict[str, Any]] = []
+    for gname, mrows in _merged_rows_grouped_for_matrix(merged_rows):
+        items: list[dict[str, str]] = []
+        for row in mrows:
+            items.append(
+                {
+                    "sku_id": _cell(row, sku_header),
+                    "title": _cell(row, title_h),
+                    "brand": _cell(
+                        row,
+                        MERGED_FIELD_TO_CSV_HEADER["detail_brand"],
+                        "detail_brand",
+                    ),
+                    "list_price_show": _cell(
+                        row, *_LIST_SHOW_PRICE_CELL_KEYS
+                    ),
+                    "coupon_or_detail_price": _cell(
+                        row,
+                        _COUPON_SHOW_PRICE_KEY,
+                        _LEGACY_COUPON_SHOW_PRICE_KEY,
+                    ),
+                    "detail_price_final": _cell(row, *_DETAIL_PRICE_FINAL_CSV_KEYS),
+                    "shop": _cell(row, *_MERGED_SHOP_CELL_KEYS),
+                    "category": _detail_category_path_cell(row),
+                    "selling_point": _cell(
+                        row, _SELLING_POINT_KEY, _LEGACY_SELLING_POINT_KEY
+                    )[:240],
+                    "comment_fuzzy": _cell(row, *_COMMENT_FUZZ_KEYS),
+                    "total_sales": merged_csv_effective_total_sales(row),
+                }
+            )
+        matrix_groups.append(
+            {"group": gname, "sku_count": len(items), "skus": items}
+        )
+
+    feedback_by_group: list[dict[str, Any]] = []
+    usage_scenarios_by_matrix_group: list[dict[str, Any]] = []
+    for gi, (gname, cr, tu) in enumerate(
+        _consumer_feedback_by_matrix_group(
+            merged_rows=merged_rows,
+            comment_rows=comment_rows,
+            sku_header=sku_header,
+        )
+    ):
+        gh = _group_keyword_hits(cr, tu, focus_words=focus_words)
+        scen_g, scen_n_g = _comment_scenario_counts(tu, scenario_groups)
+        slug_fb = _scenario_group_asset_slug(gname, gi)
+        feedback_by_group.append(
+            {
+                "group": gname,
+                "matrix_group_index": gi,
+                "chart_slug": slug_fb,
+                "comment_rows": len(cr),
+                "effective_comment_text_units": len(tu),
+                "focus_keyword_hits": [
+                    {"word": w, "count": n} for w, n in gh.most_common(24)
+                ],
+                "scenarios_top": [
+                    {
+                        "scenario": s,
+                        "count": n,
+                        "share_of_text_units": (
+                            n / scen_n_g if scen_n_g else 0.0
+                        ),
+                    }
+                    for s, n in scen_g.most_common(6)
+                ]
+                if scen_n_g
+                else [],
+            }
+        )
+        if scen_n_g > 0 and scen_g:
+            usage_scenarios_by_matrix_group.append(
+                {
+                    "group": gname,
+                    "matrix_group_index": gi,
+                    "chart_slug": slug_fb,
+                    "effective_text_units": scen_n_g,
+                    "scenarios": [
+                        {
+                            "scenario": s,
+                            "count": int(n),
+                            "share_of_text_units": (
+                                float(n) / scen_n_g if scen_n_g else 0.0
+                            ),
+                        }
+                        for s, n in scen_g.most_common()
+                        if n > 0
+                    ],
+                }
+            )
+
+    meta_slice: dict[str, Any] = {}
+    if meta:
+        for k in (
+            "page_start",
+            "page_to",
+            "max_skus_config",
+            "pc_search_export_rows",
+            "merged_rows",
+            "scenario_filter_enabled",
+            "merged_csv_mode",
+        ):
+            if k in meta:
+                meta_slice[k] = meta[k]
+
+    list_brand_block: dict[str, Any] | None
+    if len(brands_for_cr) >= min_brand_rows:
+        list_brand_block = {
+            "first_share": cr1_list_brand,
+            "top_three_combined_share": cr3_list_brand,
+            "top_label": top_list_brand,
+        }
+    else:
+        list_brand_block = None
+
+    out: dict[str, Any] = {
+        "schema_version": 1,
+        "keyword": keyword,
+        "batch_label": batch,
+        "run_dir": str(run_dir.resolve()),
+        "scope": {
+            "merged_sku_count": n_sku,
+            "comment_flat_rows": n_cmt,
+            "structure_source_rows": n_structure,
+            "uses_pc_search_list_export": list_export,
+            "category_mix_source": "keyword_pipeline_merged",
+            "category_mix_valid_matrix_sku_count": n_sku_matrix,
+        },
+        "meta": meta_slice or None,
+        "pc_search_raw": {
+            "result_count_consensus": api_rc,
+            "list_keyword": api_list_kw or None,
+            "result_count_uniques": api_rc_uniques,
+            "raw_json_files_scanned": api_raw_json_n,
+        },
+        "list_visibility_proxy": proxy,
+        "concentration": {
+            "shops_from_list": {
+                "first_share": cr1_shop,
+                "top_three_combined_share": cr3_shop,
+                "top_label": top_shop_s,
+                "top_share_pct": top_shop_share,
+            },
+            "list_brand_field": list_brand_block,
+            "detail_brand_among_merged": {
+                "first_share": cr1_deep,
+                "top_three_combined_share": cr3_deep,
+                "top_label": top_brand_deep,
+                "top_share_pct": top_brand_deep_share,
+            },
+        },
+        "category_mix_top": [
+            {"label": lbl, "count": cnt} for lbl, cnt in cm_structure
+        ],
+        "list_brand_mix_top": [
+            {"label": k, "count": v}
+            for k, v in _counter_mix_top_rows_with_remainder(
+                brands_s,
+                top_n=24,
+                remainder_label="（其余品牌）",
+            )
+        ],
+        "list_shop_mix_top": [
+            {"label": k, "count": v}
+            for k, v in _counter_mix_top_rows_with_remainder(
+                shops_s,
+                top_n=24,
+                remainder_label="（其余店铺）",
+            )
+        ],
+        "price_stats": pst,
+        "price_stats_source": price_stats_source,
+        "price_stats_merged_sample": pst_merged,
+        "price_stats_list_export": pst_list if list_export else {},
+        "price_promotion_signals": price_promotion_signals,
+        "comment_focus_keywords": [
+            {"word": w, "count": n} for w, n in hits.most_common(24)
+        ],
+        "usage_scenarios": [
+            {
+                "scenario": lbl,
+                "count": n,
+                "share_of_text_units": (
+                    n / scen_n_texts if scen_n_texts else 0.0
+                ),
+            }
+            for lbl, n in scen_counts.most_common(16)
+        ],
+        "usage_scenarios_denominator": scen_n_texts,
+        "usage_scenarios_by_matrix_group": usage_scenarios_by_matrix_group,
+        "strategy_hints": hints,
+        "matrix_by_group": matrix_groups,
+        "consumer_feedback_by_matrix_group": feedback_by_group,
+        "comment_sentiment_lexicon": comment_sentiment_lexicon,
+        "notes": [
+            "与在线分析报告各章**计数规则**一致；关注词与场景以任务中的分析规则为准（子串命中统计，非深度主题模型）。",
+            "价格来自页面展示字段抽取，含促销与规格差异；促销与标价对齐等为启发式摘录，仅供对照。",
+            "评价语气为关键词粗判，非深度学习情感模型。",
+            "「集中度」中：最大一家占比、前三名合计占比为小数（如 0.12 表示约 12%），对应列表或深入样本中的相关行。",
+        ],
+    }
+    return _sanitize_json_numbers(out)
+
+
+def main() -> None:
+    try:
+        if hasattr(sys.stdout, "reconfigure"):
+            sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+        if hasattr(sys.stderr, "reconfigure"):
+            sys.stderr.reconfigure(encoding="utf-8", errors="replace")
+    except Exception:
+        pass
+
+    existing = _resolve_existing_run_dir(EXISTING_RUN_DIR)
+    meta_path_early = (existing / kpl.FILE_RUN_META_JSON) if existing else None
+    meta_early: dict[str, Any] | None = None
+    if meta_path_early and meta_path_early.is_file():
+        try:
+            meta_early = json.loads(meta_path_early.read_text(encoding="utf-8"))
+        except json.JSONDecodeError:
+            meta_early = None
+
+    if existing:
+        if not existing.is_dir():
+            print(f"[竞品报告] EXISTING_RUN_DIR 不是目录: {existing}", file=sys.stderr)
+            sys.exit(2)
+        kw = (KEYWORD or "").strip() or _infer_keyword(existing, meta_early)
+        if not kw:
+            print(
+                "[竞品报告] 仅分析已有目录时，请配置 KEYWORD，或保留 run_meta.json 的 keyword，"
+                "或使目录名为 YYYYMMDD_HHMMSS_关键词",
+                file=sys.stderr,
+            )
+            sys.exit(2)
+        run_dir = existing
+        print(f"[竞品报告] 使用已有目录（不抓取）: {run_dir}", file=sys.stderr)
+    else:
+        kw = (KEYWORD or "").strip()
+        if not kw:
+            print("[竞品报告] 全量抓取时请在本文件顶部配置 KEYWORD", file=sys.stderr)
+            sys.exit(2)
+
+        backup: dict[str, Any] = {}
+        try:
+            if OVERRIDE_MAX_SKUS is not None:
+                backup["MAX_SKUS"] = kpl.MAX_SKUS
+                kpl.MAX_SKUS = max(1, int(OVERRIDE_MAX_SKUS))
+            if OVERRIDE_PAGE_START is not None:
+                backup["PAGE_START"] = kpl.PAGE_START
+                kpl.PAGE_START = max(1, int(OVERRIDE_PAGE_START))
+            if OVERRIDE_PAGE_TO is not None:
+                backup["PAGE_TO"] = kpl.PAGE_TO
+                kpl.PAGE_TO = max(1, int(OVERRIDE_PAGE_TO))
+
+            print(f"[竞品报告] 关键词={kw!r}，开始流水线…", file=sys.stderr)
+            run_dir = kpl.main(keyword=kw)
+        finally:
+            for name, val in backup.items():
+                setattr(kpl, name, val)
+
+    merged_path = run_dir / kpl.FILE_MERGED_CSV
+    comments_path = run_dir / kpl.FILE_COMMENTS_FLAT_CSV
+    meta_path = run_dir / kpl.FILE_RUN_META_JSON
+
+    _, merged_rows = _read_csv_rows(merged_path)
+    _, search_export_rows = _read_csv_rows(run_dir / kpl.FILE_PC_SEARCH_CSV)
+    _, comment_rows = _read_csv_rows(comments_path)
+    meta: dict[str, Any] | None = meta_early if existing else None
+    if meta is None and meta_path.is_file():
+        try:
+            meta = json.loads(meta_path.read_text(encoding="utf-8"))
+        except json.JSONDecodeError:
+            meta = None
+
+    md = build_competitor_markdown(
+        run_dir=run_dir,
+        keyword=kw,
+        merged_rows=merged_rows,
+        search_export_rows=search_export_rows,
+        comment_rows=comment_rows,
+        meta=meta,
+    )
+    out_md = run_dir / "competitor_analysis.md"
+    out_md.write_text(md, encoding="utf-8")
+    print(f"[竞品报告] 运行目录: {run_dir}", file=sys.stderr)
+    print(f"[竞品报告] 已写: {out_md}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/backend/pipeline/matrix_group_label.py b/backend/pipeline/matrix_group_label.py
index 16d127e..dd825ae 100644
--- a/backend/pipeline/matrix_group_label.py
+++ b/backend/pipeline/matrix_group_label.py
@@ -1,5 +1,5 @@
 """
-与 ``competitor_report.matrix_group`` / 历史 ``jd_competitor_report`` 中路径解析逻辑同源：
+与 ``pipeline.competitor_report.matrix_group`` / 历史爬虫侧脚本中路径解析逻辑同源：
 从商详 ``detail_category_path`` 解析 §5 竞品矩阵用的类目展示名（如饼干、米）。
 """
 from __future__ import annotations
diff --git a/backend/pipeline/reporting/charts.py b/backend/pipeline/reporting/charts.py
index c8d8ebe..0f61ed9 100644
--- a/backend/pipeline/reporting/charts.py
+++ b/backend/pipeline/reporting/charts.py
@@ -545,7 +545,7 @@ def generate_report_charts(
     )
 
     def scenario_group_asset_slug(group: str, index: int) -> str:
-        """与 ``jd_competitor_report._scenario_group_asset_slug`` 保持一致。"""
+        """与 ``pipeline.jd_competitor_report`` / ``report_md_helpers._scenario_group_asset_slug`` 保持一致。"""
         raw = (group or "").strip()
         core = re.sub(r"[^\w\u4e00-\u9fff-]", "", raw)[:20]
         if not core:
diff --git a/backend/pipeline/tests/test_comment_group_summaries_llm.py b/backend/pipeline/tests/test_comment_group_summaries_llm.py
index 261f635..d89bf7a 100644
--- a/backend/pipeline/tests/test_comment_group_summaries_llm.py
+++ b/backend/pipeline/tests/test_comment_group_summaries_llm.py
@@ -2,11 +2,8 @@
 from __future__ import annotations
 
 import json
-import sys
-from pathlib import Path
 from unittest.mock import patch
 
-from django.conf import settings
 from django.test import SimpleTestCase
 
 from pipeline.llm.generate import (
@@ -16,10 +13,7 @@ from pipeline.llm.generate import (
 
 
 def _load_jcr():
-    root = Path(settings.CRAWLER_JD_ROOT).resolve()
-    if str(root) not in sys.path:
-        sys.path.insert(0, str(root))
-    import jd_competitor_report as jcr  # noqa: WPS433
+    from pipeline import jd_competitor_report as jcr  # noqa: WPS433
 
     return jcr
 
diff --git a/backend/pipeline/tests/test_competitor_brief.py b/backend/pipeline/tests/test_competitor_brief.py
index c30786d..80b3221 100644
--- a/backend/pipeline/tests/test_competitor_brief.py
+++ b/backend/pipeline/tests/test_competitor_brief.py
@@ -1,24 +1,18 @@
 """结构化竞品摘要：空样本烟测（不依赖真实 run_dir CSV）。"""
 from __future__ import annotations
 
-import sys
 import tempfile
 from pathlib import Path
 
-from django.conf import settings
 from django.test import SimpleTestCase
 
+from pipeline import jd_competitor_report as jcr
 from pipeline.csv_schema import infer_total_sales_from_sales_floor
 from pipeline.reporting.charts import _cn_volume_int
 
 
 class BuildCompetitorBriefTests(SimpleTestCase):
     def test_empty_merged_json_safe(self) -> None:
-        root = Path(settings.CRAWLER_JD_ROOT).resolve()
-        if str(root) not in sys.path:
-            sys.path.insert(0, str(root))
-        import jd_competitor_report as jcr  # noqa: WPS433
-
         with tempfile.TemporaryDirectory() as td:
             run_dir = Path(td)
             (run_dir / "pc_search_raw").mkdir(parents=True)
@@ -43,11 +37,6 @@ class BuildCompetitorBriefTests(SimpleTestCase):
         json.dumps(out)
 
     def test_comment_sentiment_llm_payload_has_semantic_pool(self) -> None:
-        root = Path(settings.CRAWLER_JD_ROOT).resolve()
-        if str(root) not in sys.path:
-            sys.path.insert(0, str(root))
-        import jd_competitor_report as jcr  # noqa: WPS433
-
         texts = ["口感软硬适中很好吃", "太差了不建议"]
         attr = [f"【细类：A｜SKU：1｜品名：x｜店铺：y】{t}" for t in texts]
         pl = jcr.build_comment_sentiment_llm_payload(
@@ -61,11 +50,6 @@ class BuildCompetitorBriefTests(SimpleTestCase):
         self.assertGreaterEqual(len(pl["sample_reviews_semantic_pool"]), 1)
 
     def test_comment_sentiment_score_then_lexeme(self) -> None:
-        root = Path(settings.CRAWLER_JD_ROOT).resolve()
-        if str(root) not in sys.path:
-            sys.path.insert(0, str(root))
-        import jd_competitor_report as jcr  # noqa: WPS433
-
         texts = ["很好吃", "太差了", "一般般"]
         scores = [5, 1, 3]
         lex = jcr._comment_sentiment_lexicon(texts, scores)
@@ -77,22 +61,12 @@ class BuildCompetitorBriefTests(SimpleTestCase):
         self.assertEqual(pl.get("sentiment_bucket_method"), "score_then_lexeme")
 
     def test_comment_sentiment_all_scores_missing_falls_back_keyword(self) -> None:
-        root = Path(settings.CRAWLER_JD_ROOT).resolve()
-        if str(root) not in sys.path:
-            sys.path.insert(0, str(root))
-        import jd_competitor_report as jcr  # noqa: WPS433
-
         texts = ["好吃推荐", "差评"]
         scores = [None, None]
         lex = jcr._comment_sentiment_lexicon(texts, scores)
         self.assertEqual(lex.get("method"), "keyword_lexicon")
 
     def test_custom_focus_words_in_report_config(self) -> None:
-        root = Path(settings.CRAWLER_JD_ROOT).resolve()
-        if str(root) not in sys.path:
-            sys.path.insert(0, str(root))
-        import jd_competitor_report as jcr  # noqa: WPS433
-
         with tempfile.TemporaryDirectory() as td:
             run_dir = Path(td)
             (run_dir / "pc_search_raw").mkdir(parents=True)
@@ -115,11 +89,6 @@ class BuildCompetitorBriefTests(SimpleTestCase):
         self.assertIn("自定义词阿尔法", words)
 
     def test_matrix_groups_require_detail_category_path(self) -> None:
-        root = Path(settings.CRAWLER_JD_ROOT).resolve()
-        if str(root) not in sys.path:
-            sys.path.insert(0, str(root))
-        import jd_competitor_report as jcr  # noqa: WPS433
-
         sku_h = "SKU(skuId)"
         merged = [
             {
@@ -149,11 +118,6 @@ class BuildCompetitorBriefTests(SimpleTestCase):
 
     def test_comment_lines_with_product_context_prefix(self) -> None:
         """评价抽样须带细类/SKU/品名前缀，便于归因。"""
-        root = Path(settings.CRAWLER_JD_ROOT).resolve()
-        if str(root) not in sys.path:
-            sys.path.insert(0, str(root))
-        import jd_competitor_report as jcr  # noqa: WPS433
-
         sku_h = "SKU(skuId)"
         title_h = "标题(wareName)"
         merged = [
@@ -179,11 +143,6 @@ class BuildCompetitorBriefTests(SimpleTestCase):
         self.assertIn("整体口感还差点意思", lines[0])
 
     def test_scenario_groups_llm_payload_matches_section_8_4_counts(self) -> None:
-        root = Path(settings.CRAWLER_JD_ROOT).resolve()
-        if str(root) not in sys.path:
-            sys.path.insert(0, str(root))
-        import jd_competitor_report as jcr  # noqa: WPS433
-
         sku_h = "SKU(skuId)"
         merged = [
             {
@@ -226,11 +185,6 @@ class BuildCompetitorBriefTests(SimpleTestCase):
 
     def test_mix_top_remainder_sums_to_all_rows(self) -> None:
         """mix_top 各 count 之和须等于 strip 后可统计行数（与扇图同源）。"""
-        root = Path(settings.CRAWLER_JD_ROOT).resolve()
-        if str(root) not in sys.path:
-            sys.path.insert(0, str(root))
-        import jd_competitor_report as jcr  # noqa: WPS433
-
         names = [f"店{i}" for i in range(30)]
         mix = jcr._counter_mix_top_rows_with_remainder(
             names, top_n=24, remainder_label="（其余店铺）"