feat(chat_quality_analysis): chat_quality_analysis 模块

2026-09-22 13:38:43 +00:00 · 2026-03-23 00:42:18 +08:00
parent f40243492f
commit 9a35ed9600
26 changed files with 1655 additions and 178 deletions
@@ -148,6 +148,13 @@
                "default": "",
                "hint": "专门用于金句分析的 Provider。留空则使用主 LLM Provider"
            },
+            "quality_provider_id": {
+                "type": "string",
+                "description": "聊天质量分析专用 Provider ID",
+                "_special": "select_provider",
+                "default": "",
+                "hint": "专门用于聊天质量锐评的 Provider。留空则使用主 LLM Provider"
+            },
            "llm_retries": {
                "type": "int",
                "description": "LLM 请求重试次数",
@@ -177,6 +184,12 @@
                "description": "（兼容部分提供商）用户称号分析最大 Token 数",
                "default": 4096,
                "hint": "（兼容部分提供商，实测大部分模型调整后没有明显效果）用户称号分析时 LLM 能生成的最大 token 数量。当分析内容较多或者分析提示词复杂时，建议适当调大此值以保证输出质量。"
+            },
+            "quality_max_tokens": {
+                "type": "int",
+                "description": "（兼容部分提供商）聊天质量分析最大 Token 数",
+                "default": 4096,
+                "hint": "聊天质量分析时 LLM 能生成的最大 token 数量。"
            }
        }
    },
@@ -203,6 +216,12 @@
                "default": true,
                "hint": "是否使用LLM进行金句分析"
            },
+            "chat_quality_analysis_enabled": {
+                "type": "bool",
+                "description": "启用聊天质量锐评",
+                "default": true,
+                "hint": "是否使用LLM进行聊天质量锐评（维度化分析）"
+            },
            "max_topics": {
                "type": "int",
                "description": "最大话题数量",
@@ -407,7 +426,21 @@
                        "type": "text",
                        "editor_mode": true,
                        "editor_language": "markdown",
-                        "default": "请从以下群聊记录中挑选出 **{max_golden_quotes}** 句最具冲击力、最令人惊叹的「金句」。\n\n## 金句标准：\n\n- **核心标准**：**逆天的神人发言**，即具备颠覆常识的脑洞、逻辑跳脱的表达或强烈反差感的原创内容\n- **典型特征**：包含某些争议话题元素、夸张类比、反常规结论、一本正经的「胡说八道」或突破语境的清奇思路，并且具备一定的冲击力，让人印象深刻\n\n## 对于每个金句，请提供：\n\n1. **原文内容**（完整保留发言细节）\n2. **发言人用户ID**（必须严格使用消息记录中提供的 [用户ID]）\n3. **选择理由**（具体说明其「逆天」之处，如逻辑颠覆点/脑洞角度/反差感/争议话题元素）\n\n## 严格约束：\n\n- 优先筛选 **逆天指数最高** 的内容：\n  - 发情、性压抑话题 > 争议话题 > 元素级 > 颠覆认知级 > 逻辑跳脱级 > 趣味调侃级\n  - 剔除单纯玩梗或网络热词堆砌的普通发言\n- **用户引用**：在选择理由（reason）中，如果提到了具体用户，请使用 `[用户ID]` 的格式来指代（例如 `[123456]`）。不要只写昵称。我们会自动渲染头像。\n- **身份对齐**：返回的 `sender` 字段必须是 `[用户ID]` 格式（例如 `[123456]`）。我们会根据 ID 自动还原昵称和头像。\n\n## 群聊记录格式: [HH:MM] [用户ID]: 消息内容\n\n## 群聊记录：\n\n{messages_text}\n\n---\n\n### 返回格式示例：\n\n```json\n[\n  {{\n    \"content\": \"金句原文\",\n    \"sender\": \"[123456789]\",\n    \"reason\": \"这句话太逆天了，尤其是对 [987654321] 的逻辑降维打击。\"\n  }}\n]\n```\n\n**注意**：请以纯 JSON 格式返回，不要包含 markdown 代码块标记。"
+                        "default": "请从以下群聊记录中挑选出 **{max_golden_quotes}** 句最具冲击力、最令人惊叹的「金句」。\n\n## 金句标准：\n\n- **核心标准**：**逆天的神人发言**，即具备颠覆常识的脑洞、逻辑跳脱的表达或强烈反差感的原创内容\n- **典型特征**：包含某些争议话题元素、夸张类比、反常规结论、一本正经的「胡说八道」或突破语境的清奇思路，并且具备一定的冲击力，让人印象深刻\n\n## 对于每个金句，请提供：\n\n1. **原文内容**（完整保留发言细节）\n2. **发言人用户ID**（必须严格使用消息记录中提供的 [用户ID]）\n3. **选择理由**（具体说明其「逆天」之处，如逻辑颠覆点/脑洞角度/反差感/争议话题元素）\n\n## 严格约束：\n\n- 优先筛选 **逆天指数最高** 的内容：\n  - 发情、性压抑话题 > 争议话题 > 元素级 > 颠覆认知级 > 逻辑跳脱级 > 趣味调侃级\n  - 剔除单纯玩梗或网络热词堆砌的普通发言\n- **用户引用**：在选择理由（reason）中，如果提到了具体用户，请使用 `[用户ID]` 的格式来指代（例如 `[123456]`）。不要只写昵称。我们会自动渲染头像。\n- **身份对齐**：返回的 `sender` 字段必须是 `[用户ID]` 格式（例如 `[123456]`）。我们会根据 ID 自动还原昵称和头像。\n\n## 群聊记录格式: [HH:MM] [用户ID]: 消息内容\n\n## 群聊记录：\n\n{messages_text}\n\n---\n\n### 返回格式示例：\n\n```json\n[\n  {{\n    \"content\": \"金句原文\",\n    \"sender\": \"[123456789]\",\n    \"reason\": \"这句话太逆天了，尤其是对 [987654321] 的逻辑降维打击。\"\n  }}\n]\n```\n\n**注意**：返回的内容必须是纯 JSON，不要包含 markdown 代码块标记或其他格式。"
+                    }
+                }
+            },
+            "quality_analysis_prompts": {
+                "description": "聊天质量分析提示词模板",
+                "type": "object",
+                "hint": "聊天质量分析提示词模板，可自定义修改",
+                "items": {
+                    "quality_prompt": {
+                        "description": "默认聊天质量分析提示词",
+                        "type": "text",
+                        "editor_mode": true,
+                        "editor_language": "markdown",
+                        "default": "你是一个毒舌且幽默的群聊质量分析师，请生成聊天质量锐评内容。你需要按照下面的格式返回结果。\n\n分析接下来的群聊内容，给出多个维度的分析（3-6个）和汇总内容。\n\n群聊记录：\n{messages_text}\n\n### 返回格式示例：\n\n```json\n{{\n  \"title\": \"群聊质量分析报告\",\n  \"subtitle\": \"今天的群里发生了什么？\",\n  \"dimensions\": [\n    {{\n      \"name\": \"水群闲聊\",\n      \"percentage\": 50.0,\n      \"comment\": \"群友们今天也在努力划水呢。\"\n    }}\n  ],\n  \"summary\": \"今天也是元气满满的一天。\"\n}}\n```"
                    }
                }
            }
@@ -49,17 +49,18 @@ astrbot_path.get_astrbot_data_path = lambda: Path(".")
 sys.modules["astrbot.core.utils"] = astrbot_core_utils
 sys.modules["astrbot.core.utils.astrbot_path"] = astrbot_path

-from src.domain.entities.analysis_result import (  # noqa: E402
+from src.domain.models.data_models import (  # noqa: E402
    ActivityVisualization,
    EmojiStatistics,
    GoldenQuote,
    GroupStatistics,
+    QualityDimension,
+    QualityReview,
    SummaryTopic,
    TokenUsage,
    UserTitle,
 )
 from src.infrastructure.reporting.generators import ReportGenerator  # noqa: E402
-from src.infrastructure.reporting.templates import HTMLTemplates  # noqa: E402


 class MockConfigManager:
@@ -94,6 +95,12 @@ class MockConfigManager:
    def get_browser_path(self) -> str:
        return ""

+    def get_t2i_max_concurrent(self) -> int:
+        return 4
+
+    def get_llm_max_concurrent(self) -> int:
+        return 2
+

 async def mock_get_user_avatar(user_id: str) -> str:
    # Return a known avatar URL for testing
@@ -106,12 +113,13 @@ async def debug_render(
    # 1. Setup Mock Data
    config_manager = MockConfigManager(template_name)

-    # 2. Mock Analysis Result using Entities
+    # 2. Mock Analysis Result using Data Models
    stats = GroupStatistics(
        message_count=1250,
        total_characters=45000,
        participant_count=42,
        most_active_period="20:00 - 22:00",
+        golden_quotes=[],  # Will be filled later
        emoji_count=156,
        emoji_statistics=EmojiStatistics(face_count=100, mface_count=56),
        activity_visualization=ActivityVisualization(
@@ -119,6 +127,40 @@ async def debug_render(
                i: (10 + i * 5 if i < 12 else 100 - i * 2) for i in range(24)
            }
        ),
+        token_usage=TokenUsage(
+            prompt_tokens=1500, completion_tokens=800, total_tokens=2300
+        ),
+        chat_quality_review=QualityReview(
+            title="互联网难民收容所",
+            subtitle="只要不工作，我们就是最好的朋友",
+            dimensions=[
+                QualityDimension(
+                    "水群闲聊",
+                    44.0,
+                    "这里的群友不生产代码，只生产各种表情包和废话，建议送去加个班。",
+                    "#607d8b",
+                ),
+                QualityDimension(
+                    "技术探讨",
+                    25.5,
+                    "偶尔冒出的技术术语像是在荒漠里发现绿洲，虽然很快就被废话淹没了。",
+                    "#2196f3",
+                ),
+                QualityDimension(
+                    "深夜发情",
+                    15.0,
+                    "凌晨三点的群聊内容需要打上 R18 标签，建议各位群友早点休息。",
+                    "#f44336",
+                ),
+                QualityDimension(
+                    "就业焦虑",
+                    10.5,
+                    "谈到工作时群里笼罩着一股淡淡的忧伤，大家都在比谁的工位更像牢房。",
+                    "#ff9800",
+                ),
+            ],
+            summary="今天也是充满活力（或者说充满废话）的一天，继续保持这份不求上进的快乐吧。",
+        ),
    )

    topics = [
@@ -257,9 +299,7 @@ async def debug_render(
    ]

    stats.golden_quotes = golden_quotes
-    stats.token_usage = TokenUsage(
-        prompt_tokens=1500, completion_tokens=800, total_tokens=2300
-    )
+    # token_usage already set in constructor

    analysis_result = {
        "statistics": stats,
@@ -270,6 +310,7 @@ async def debug_render(
            "987654321": {"nickname": "李四"},
            "112233445": {"nickname": "潜水员"},
        },
+        "chat_quality_review": stats.chat_quality_review,
        "analysis_date": "2026年02月11日",
        "group_id": "123456",
        "group_name": "测试群组",
@@ -285,22 +326,18 @@ async def debug_render(
    # Note: _prepare_render_data handles converting Entities to template-friendly dicts
    render_payload = await generator._prepare_render_data(analysis_result)

-    # 5. Render Main Template
-    html_templates = HTMLTemplates(config_manager)
-    # Get image template string
-    raw_template = html_templates.get_image_template()
-
-    if not raw_template:
-        print(f"[ERROR] Failed to load template for '{template_name}'")
-        return
-
-    # Use generator's internal renderer
-    final_html = generator._render_html_template(raw_template, render_payload)
+    # Use Jinja2 renderer
+    final_html = generator.html_templates.render_template(
+        "image_template.html", **render_payload
+    )

    # 6. Save to file
    output_path = Path(output_file)
    output_path.write_text(final_html, encoding="utf-8")

+    # 7. Close generator
+    await generator.close()
+
    print(
        f"Successfully rendered template '{template_name}' to {output_path.absolute()}"
    )
@@ -182,10 +182,14 @@ class AnalysisApplicationService:
            golden_quote_enabled = (
                self.config_manager.get_golden_quote_analysis_enabled()
            )
+            chat_quality_enabled = (
+                self.config_manager.get_chat_quality_analysis_enabled()
+            )

            topics = []
            user_titles = []
            golden_quotes = []
+            chat_quality_review = None
            total_token_usage = TokenUsage()

            # Note: LLMAnalyzer 目前可能只接收 legacy 格式或特定的 UnifiedMessage 适配
@@ -198,7 +202,12 @@ class AnalysisApplicationService:
                f"{platform_id}:GroupMessage:{group_id}" if platform_id else group_id
            )

-            if topic_enabled or user_title_enabled or golden_quote_enabled:
+            if (
+                topic_enabled
+                or user_title_enabled
+                or golden_quote_enabled
+                or chat_quality_enabled
+            ):
                async with self.llm_semaphore:
                    logger.debug(f"[LLM] 已进入分析队列 (群: {group_id})")
                    (
@@ -206,6 +215,7 @@ class AnalysisApplicationService:
                        user_titles,
                        golden_quotes,
                        total_token_usage,
+                        chat_quality_review,
                    ) = await self.llm_analyzer.analyze_all_concurrent(
                        legacy_messages,
                        user_activity,
@@ -214,6 +224,7 @@ class AnalysisApplicationService:
                        topic_enabled=topic_enabled,
                        user_title_enabled=user_title_enabled,
                        golden_quote_enabled=golden_quote_enabled,
+                        chat_quality_enabled=chat_quality_enabled,
                    )

            # 回填结果
@@ -225,6 +236,7 @@ class AnalysisApplicationService:
                "topics": topics,
                "user_titles": user_titles,
                "user_analysis": user_activity,
+                "chat_quality_review": chat_quality_review,
            }

            # 6. 持久化摘要 (Persistence)
@@ -355,6 +367,9 @@ class AnalysisApplicationService:
            golden_quote_enabled = (
                self.config_manager.get_golden_quote_analysis_enabled()
            )
+            chat_quality_enabled = (
+                self.config_manager.get_chat_quality_analysis_enabled()
+            )

            # 需要将 UnifiedMessage 转换为 legacy 格式供 LLM 分析器使用
            legacy_messages = self.statistics_service._convert_to_legacy_dict(
@@ -364,20 +379,28 @@ class AnalysisApplicationService:
                f"{platform_id}:GroupMessage:{group_id}" if platform_id else group_id
            )

-            async with self.llm_semaphore:
-                logger.debug(f"[LLM] 已进入增量分析队列 (群: {group_id})")
-                (
-                    topics,
-                    golden_quotes,
-                    token_usage,
-                ) = await self.llm_analyzer.analyze_incremental_concurrent(
-                    legacy_messages,
-                    umo=unified_msg_origin,
-                    topics_per_batch=topics_per_batch,
-                    quotes_per_batch=quotes_per_batch,
-                    topic_enabled=topic_enabled,
-                    golden_quote_enabled=golden_quote_enabled,
-                )
+            topics = []
+            golden_quotes = []
+            token_usage = TokenUsage()
+            chat_quality_review = None
+
+            if topic_enabled or golden_quote_enabled or chat_quality_enabled:
+                async with self.llm_semaphore:
+                    logger.debug(f"[LLM] 已进入增量分析队列 (群: {group_id})")
+                    (
+                        topics,
+                        golden_quotes,
+                        token_usage,
+                        chat_quality_review,
+                    ) = await self.llm_analyzer.analyze_incremental_concurrent(
+                        legacy_messages,
+                        umo=unified_msg_origin,
+                        topics_per_batch=topics_per_batch,
+                        quotes_per_batch=quotes_per_batch,
+                        topic_enabled=topic_enabled,
+                        golden_quote_enabled=golden_quote_enabled,
+                        chat_quality_enabled=chat_quality_enabled,
+                    )

            # 8. 构建 IncrementalBatch
            # 8a. 转换话题: SummaryTopic -> dict
@@ -424,7 +447,25 @@ class AnalysisApplicationService:
                "face_details": statistics.emoji_statistics.face_details,
            }

-            # 8f. 获取参与者 ID 和最后消息时间戳
+            # 8f. 转换聊天质量锐评: QualityReview -> dict
+            chat_quality_dict = None
+            if chat_quality_review:
+                chat_quality_dict = {
+                    "title": chat_quality_review.title,
+                    "subtitle": chat_quality_review.subtitle,
+                    "dimensions": [
+                        {
+                            "name": d.name,
+                            "percentage": d.percentage,
+                            "comment": d.comment,
+                            "color": d.color,
+                        }
+                        for d in chat_quality_review.dimensions
+                    ],
+                    "summary": chat_quality_review.summary,
+                }
+
+            # 8g. 获取参与者 ID 和最后消息时间戳
            participant_ids = list({msg.sender_id for msg in unified_messages})
            last_message_timestamp = max(
                (msg.timestamp for msg in unified_messages), default=0
@@ -446,6 +487,7 @@ class AnalysisApplicationService:
                topics=new_topics,
                golden_quotes=new_quotes,
                token_usage=token_usage_dict,
+                chat_quality_review=chat_quality_dict,
                last_message_timestamp=last_message_timestamp,
                participant_ids=participant_ids,
            )
@@ -39,6 +39,7 @@ class IncrementalBatch:
        topics: 本批次提取的话题列表
        golden_quotes: 本批次提取的金句列表
        token_usage: 本批次 token 消耗 {prompt_tokens, completion_tokens, total_tokens}
+        chat_quality_review: 本批次提取的聊天质量锐评
        last_message_timestamp: 本批次最后一条消息的时间戳
        participant_ids: 本批次参与者 ID 列表
    """
@@ -73,6 +74,7 @@ class IncrementalBatch:
    )

    # 增量追踪
+    chat_quality_review: dict[str, Any] | None = None
    last_message_timestamp: int = 0
    participant_ids: list[str] = field(default_factory=list)

@@ -91,6 +93,7 @@ class IncrementalBatch:
            "topics": self.topics,
            "golden_quotes": self.golden_quotes,
            "token_usage": self.token_usage,
+            "chat_quality_review": self.chat_quality_review,
            "last_message_timestamp": self.last_message_timestamp,
            "participant_ids": self.participant_ids,
        }
@@ -118,6 +121,7 @@ class IncrementalBatch:
                    "total_tokens": 0,
                },
            ),
+            chat_quality_review=data.get("chat_quality_review"),
            last_message_timestamp=data.get("last_message_timestamp", 0),
            participant_ids=data.get("participant_ids", []),
        )
@@ -170,6 +174,7 @@ class IncrementalState:
    # 合并后的 LLM 分析结果
    topics: list[dict] = field(default_factory=list)
    golden_quotes: list[dict] = field(default_factory=list)
+    chat_quality_review: dict[str, Any] | None = None

    # 合并后的统计数据（按小时）
    hourly_message_counts: dict[str, int] = field(default_factory=dict)
@@ -4,6 +4,7 @@
 """

 from dataclasses import dataclass, field
+from typing import Optional


@dataclass
@@ -39,6 +40,26 @@ class GoldenQuote:
    user_id: str = ""  # 原 qq 字段


+@dataclass
+class QualityDimension:
+    """聊天质量维度数据结构"""
+
+    name: str  # 维度名称
+    percentage: float  # 占比
+    comment: str  # 犀利点评
+    color: str = "#607d8b"  # 颜色
+
+
+@dataclass
+class QualityReview:
+    """聊天质量锐评数据结构"""
+
+    title: str
+    subtitle: str
+    dimensions: list[QualityDimension]
+    summary: str
+
+
@dataclass
 class TokenUsage:
    """Token使用统计"""
@@ -97,3 +118,4 @@ class GroupStatistics:
        default_factory=ActivityVisualization
    )
    token_usage: TokenUsage = field(default_factory=TokenUsage)
+    chat_quality_review: Optional["QualityReview"] = None
@@ -5,7 +5,13 @@

 from abc import ABC, abstractmethod

-from ..models.data_models import GoldenQuote, SummaryTopic, TokenUsage, UserTitle
+from ..models.data_models import (
+    GoldenQuote,
+    QualityReview,
+    SummaryTopic,
+    TokenUsage,
+    UserTitle,
+)


 class IAnalysisProvider(ABC):
@@ -55,7 +61,14 @@ class IAnalysisProvider(ABC):
        topic_enabled: bool = True,
        user_title_enabled: bool = True,
        golden_quote_enabled: bool = True,
-    ) -> tuple[list[SummaryTopic], list[UserTitle], list[GoldenQuote], TokenUsage]:
+        chat_quality_enabled: bool = False,
+    ) -> tuple[
+        list[SummaryTopic],
+        list[UserTitle],
+        list[GoldenQuote],
+        TokenUsage,
+        QualityReview | None,
+    ]:
        """并发分析所有内容"""
        pass

@@ -68,6 +81,7 @@ class IAnalysisProvider(ABC):
        quotes_per_batch: int = 3,
        topic_enabled: bool = True,
        golden_quote_enabled: bool = True,
-    ) -> tuple[list[SummaryTopic], list[GoldenQuote], TokenUsage]:
+        chat_quality_enabled: bool = False,
+    ) -> tuple[list[SummaryTopic], list[GoldenQuote], TokenUsage, QualityReview | None]:
        """增量模式并发分析"""
        pass
@@ -20,6 +20,8 @@ from ...domain.models.data_models import (
    EmojiStatistics,
    GoldenQuote,
    GroupStatistics,
+    QualityDimension,
+    QualityReview,
    SummaryTopic,
    TokenUsage,
 )
@@ -175,6 +177,9 @@ class IncrementalMergeService:
            # 记录最后分析消息时间戳（取最大值）
            if batch.last_message_timestamp > state.last_analyzed_message_timestamp:
                state.last_analyzed_message_timestamp = batch.last_message_timestamp
+                # 更新锐评为最新批次的 (如果有)
+                if batch.chat_quality_review:
+                    state.chat_quality_review = batch.chat_quality_review

        logger.info(
            f"合并批次完成: 群={state.group_id}, "
@@ -233,6 +238,27 @@ class IncrementalMergeService:
        # 获取最活跃时段描述
        most_active_period = state.get_most_active_period()

+        # 转换聊天质量锐评 (如果有)
+        chat_quality_review = None
+        if state.chat_quality_review:
+            review_dict = state.chat_quality_review
+            dimensions_dict = review_dict.get("dimensions", [])
+            dimensions = [
+                QualityDimension(
+                    name=d.get("name", "未知"),
+                    percentage=float(d.get("percentage", 0)),
+                    comment=d.get("comment", ""),
+                    color=d.get("color", "#607d8b"),
+                )
+                for d in dimensions_dict
+            ]
+            chat_quality_review = QualityReview(
+                title=review_dict.get("title", "聊天质量锐评"),
+                subtitle=review_dict.get("subtitle", "今天的群里发生了什么？"),
+                dimensions=dimensions,
+                summary=review_dict.get("summary", "今天也是充满活力的一天。"),
+            )
+
        statistics = GroupStatistics(
            message_count=state.total_message_count,
            total_characters=state.total_character_count,
@@ -243,6 +269,7 @@ class IncrementalMergeService:
            emoji_statistics=emoji_statistics,
            activity_visualization=activity_visualization,
            token_usage=token_usage,
+            chat_quality_review=chat_quality_review,
        )

        logger.debug(
@@ -335,6 +362,7 @@ class IncrementalMergeService:
            "topics": topics,
            "user_titles": user_titles or [],
            "user_analysis": state.user_activities,
+            "chat_quality_review": statistics.chat_quality_review,
        }

        logger.info(
@@ -0,0 +1,277 @@
+"""
+聊天质量分析模块
+专门处理群聊质量锐评分析
+"""
+
+from datetime import datetime
+
+from ....domain.models.data_models import QualityDimension, QualityReview, TokenUsage
+from ....utils.logger import logger
+from ..utils import InfoUtils
+from ..utils.json_utils import extract_quality_with_regex, parse_json_object_response
+from ..utils.llm_utils import (
+    call_provider_with_retry,
+    extract_response_text,
+    extract_token_usage,
+)
+from .base_analyzer import BaseAnalyzer
+
+
+class ChatQualityAnalyzer(BaseAnalyzer):
+    """
+    聊天质量分析器
+    专门处理群聊质量的锐评和多维度分析
+
+    注意：由于聊天质量分析返回的是 JSON 对象而非数组，
+    此分析器重写了 analyze() 方法，使用 parse_json_object_response 解析，
+    并以 extract_quality_with_regex 作为正则降级方案。
+    """
+
+    def get_provider_id_key(self) -> str:
+        """获取 Provider ID 配置键名"""
+        return "quality_provider_id"
+
+    def get_data_type(self) -> str:
+        """获取数据类型标识"""
+        return "聊天质量"
+
+    def get_max_count(self) -> int:
+        """获取最大维度数量"""
+        return 8
+
+    def get_max_tokens(self) -> int:
+        """获取最大token数"""
+        return self.config_manager.get_quality_max_tokens()
+
+    def get_temperature(self) -> float:
+        """获取温度参数"""
+        return 0.8
+
+    def build_prompt(self, data: list[dict]) -> str:
+        """
+        构建聊天质量分析提示词
+        """
+        if not data:
+            return ""
+
+        # 提取文本消息
+        text_messages = []
+        for msg in data:
+            if not isinstance(msg, dict):
+                continue
+
+            sender = msg.get("sender", {})
+            user_id = str(sender.get("user_id", ""))
+            bot_self_ids = self.config_manager.get_bot_self_ids()
+            if bot_self_ids and user_id in [str(uid) for uid in bot_self_ids]:
+                continue
+
+            nickname = InfoUtils.get_user_nickname(self.config_manager, sender)
+            msg_time = datetime.fromtimestamp(msg.get("time", 0)).strftime("%H:%M")
+            message_list = msg.get("message", [])
+
+            text_parts = []
+            for content in message_list:
+                if content.get("type") == "text":
+                    text = content.get("data", {}).get("text", "").strip()
+                    if text:
+                        text_parts.append(text)
+
+            combined_text = "".join(text_parts).strip()
+            if combined_text and not combined_text.startswith("/"):
+                text_messages.append(f"[{msg_time}] [{nickname}]: {combined_text}")
+
+        messages_text = "\n".join(text_messages[:1000])
+
+        prompt_template = self.config_manager.get_quality_analysis_prompt()
+
+        if not prompt_template:
+            prompt_template = """你是一个毒舌且幽默的群聊质量分析师。
+请分析以下群聊记录，输出一份"聊天质量锐评"。
+
+## 任务目标：
+1. 将聊天内容划分为 3-6 个不同的维度/类别（如：技术探讨、水群闲聊、就业焦虑、深夜发情等）。
+2. 为每个维度计算一个大致的百分比占位（总和为 100%）。
+3. 为每个维度写一句犀利、幽默、毒舌或温情的点评。
+4. 给出一句总结性的全群表现评价。
+5. 设定一个本次报告的主题标题和副标题。
+
+## 点评风格指南：
+- 语言要接地气，多用互联网黑话。
+- 吐槽要精准，避重就轻。
+- 如果群友在认真讨论技术，可以夸两句但也要带点调侃。
+- 如果群友在无意义水群，请狠狠吐槽。
+
+## 返回格式要求：
+必须以纯 JSON 格式返回，不得包含任何 Markdown 格式。
+
+```json
+{{
+  "title": "主题标题 (如: 互联网难民收容所)",
+  "subtitle": "副标题 (如: 只要不工作，我们就是最好的朋友)",
+  "dimensions": [
+    {{
+      "name": "维度名称",
+      "percentage": 25.5,
+      "comment": "维度的毒舌点评"
+    }}
+  ],
+  "summary": "一句总结性的金句"
+}}
+```
+
+群聊记录：
+{messages_text}
+"""
+
+        return prompt_template.format(messages_text=messages_text)
+
+    def extract_with_regex(self, result_text: str, max_count: int) -> list[dict]:
+        """
+        使用正则表达式提取质量分析数据（BaseAnalyzer 要求的接口）
+
+        注意: 此方法供 BaseAnalyzer.analyze() 的降级流程使用，
+        但由于聊天质量分析重写了 analyze()，实际由 analyze_quality() 中调用
+        extract_quality_with_regex 实现。
+        """
+        return []
+
+    def create_data_objects(self, data_list: list[dict]) -> list[QualityReview]:
+        """
+        满足 BaseAnalyzer 抽象要求。
+        聊天质量分析的数据对象创建在 analyze_quality 中完成。
+        """
+        return []
+
+    def _build_review_from_dict(self, data: dict) -> QualityReview:
+        """
+        从解析后的字典构建 QualityReview 对象
+
+        Args:
+            data: 解析后的 JSON 对象字典
+
+        Returns:
+            QualityReview 数据对象
+        """
+        dimensions = []
+        for d in data.get("dimensions", []):
+            dimensions.append(
+                QualityDimension(
+                    name=d.get("name", "未知"),
+                    percentage=float(d.get("percentage", 0)),
+                    comment=d.get("comment", ""),
+                )
+            )
+
+        # 自动分配颜色
+        colors = [
+            "#607d8b",
+            "#2196f3",
+            "#f44336",
+            "#e91e63",
+            "#ff9800",
+            "#4caf50",
+            "#009688",
+            "#9c27b0",
+        ]
+        for i, d in enumerate(dimensions):
+            d.color = colors[i % len(colors)]
+
+        return QualityReview(
+            title=data.get("title", "聊天质量锐评"),
+            subtitle=data.get("subtitle", "今天的群里发生了什么？"),
+            dimensions=dimensions,
+            summary=data.get("summary", "今天也是充满活力的一天。"),
+        )
+
+    async def analyze_quality(
+        self,
+        messages: list[dict],
+        umo: str | None = None,
+        session_id: str | None = None,
+    ) -> tuple[QualityReview | None, TokenUsage]:
+        """
+        分析聊天质量
+
+        流程遵循 BaseAnalyzer 的设计模式：
+        1. 构建 prompt
+        2. 调用 LLM
+        3. 提取 token 使用统计
+        4. JSON 解析（使用 parse_json_object_response）
+        5. 正则降级（使用 extract_quality_with_regex）
+        """
+        try:
+            # 1. 获取人格设定
+            system_prompt = await self._build_system_prompt(umo)
+
+            # 2. 构建 prompt
+            prompt = self.build_prompt(messages)
+            if not prompt:
+                return None, TokenUsage()
+
+            # 3. 调用 LLM
+            response = await call_provider_with_retry(
+                self.context,
+                self.config_manager,
+                prompt=prompt,
+                max_tokens=self.get_max_tokens(),
+                temperature=self.get_temperature(),
+                umo=umo,
+                provider_id_key=self.get_provider_id_key(),
+                system_prompt=system_prompt,
+            )
+
+            if response is None:
+                return None, TokenUsage()
+
+            # 4. 提取 token 使用统计
+            token_usage_dict = extract_token_usage(response)
+            usage = TokenUsage(
+                prompt_tokens=token_usage_dict["prompt_tokens"],
+                completion_tokens=token_usage_dict["completion_tokens"],
+                total_tokens=token_usage_dict["total_tokens"],
+            )
+
+            # 5. 提取响应文本
+            result_text = extract_response_text(response)
+            if not result_text:
+                return None, usage
+
+            # 6. JSON 解析（使用 parse_json_object_response）
+            success, parsed_data, error_msg = parse_json_object_response(
+                result_text, self.get_data_type()
+            )
+
+            if success and parsed_data:
+                review = self._build_review_from_dict(parsed_data)
+                logger.info(f"聊天质量分析成功，解析到 {len(review.dimensions)} 个维度")
+                return review, usage
+
+            # 7. 正则降级（使用 extract_quality_with_regex）
+            logger.warning(f"聊天质量JSON解析失败，尝试正则表达式提取: {error_msg}")
+            regex_data = extract_quality_with_regex(result_text)
+
+            if regex_data:
+                review = self._build_review_from_dict(regex_data)
+                logger.info(
+                    f"聊天质量正则提取成功，获得 {len(review.dimensions)} 个维度"
+                )
+                return review, usage
+
+            # 8. 全部失败
+            logger.error("聊天质量分析失败: JSON解析和正则表达式提取均未成功")
+            return None, usage
+
+        except Exception as e:
+            logger.error(f"聊天质量分析解析失败: {e}", exc_info=True)
+            return None, TokenUsage()
+
+    # Override analyze to bridge the base class interface
+    async def analyze(
+        self,
+        data: list[dict],
+        umo: str | None = None,
+        session_id: str | None = None,
+    ) -> tuple[list[QualityReview], TokenUsage]:
+        review, usage = await self.analyze_quality(data, umo, session_id)
+        return [review] if review else [], usage
@@ -7,12 +7,14 @@ import asyncio

 from ...domain.models.data_models import (
    GoldenQuote,
+    QualityReview,
    SummaryTopic,
    TokenUsage,
    UserTitle,
 )
 from ...domain.repositories.analysis_repository import IAnalysisProvider
 from ...utils.logger import logger
+from .analyzers.chat_quality_analyzer import ChatQualityAnalyzer
 from .analyzers.golden_quote_analyzer import GoldenQuoteAnalyzer
 from .analyzers.topic_analyzer import TopicAnalyzer
 from .analyzers.user_title_analyzer import UserTitleAnalyzer
@@ -46,6 +48,7 @@ class LLMAnalyzer(IAnalysisProvider):
        self.topic_analyzer = TopicAnalyzer(context, config_manager)
        self.user_title_analyzer = UserTitleAnalyzer(context, config_manager)
        self.golden_quote_analyzer = GoldenQuoteAnalyzer(context, config_manager)
+        self.chat_quality_analyzer = ChatQualityAnalyzer(context, config_manager)

    async def analyze_topics(
        self,
@@ -170,7 +173,14 @@ class LLMAnalyzer(IAnalysisProvider):
        topic_enabled: bool = True,
        user_title_enabled: bool = True,
        golden_quote_enabled: bool = True,
-    ) -> tuple[list[SummaryTopic], list[UserTitle], list[GoldenQuote], TokenUsage]:
+        chat_quality_enabled: bool = False,
+    ) -> tuple[
+        list[SummaryTopic],
+        list[UserTitle],
+        list[GoldenQuote],
+        TokenUsage,
+        QualityReview | None,
+    ]:
        """
        并发执行所有分析任务（话题、用户称号、金句），支持按需启用。

@@ -230,8 +240,16 @@ class LLMAnalyzer(IAnalysisProvider):
                )
                task_names.append("golden_quote")

+            if chat_quality_enabled:
+                tasks.append(
+                    self.chat_quality_analyzer.analyze_quality(
+                        messages, umo, session_id
+                    )
+                )
+                task_names.append("chat_quality")
+
            if not tasks:
-                return [], [], [], TokenUsage()
+                return [], [], [], TokenUsage(), None

            results = await asyncio.gather(*tasks, return_exceptions=True)

@@ -239,6 +257,8 @@ class LLMAnalyzer(IAnalysisProvider):
            topics, topic_usage = [], TokenUsage()
            user_titles, title_usage = [], TokenUsage()
            golden_quotes, quote_usage = [], TokenUsage()
+            chat_quality_review = None
+            quality_usage = TokenUsage()  # Initialize here

            for i, result in enumerate(results):
                name = task_names[i]
@@ -252,38 +272,52 @@ class LLMAnalyzer(IAnalysisProvider):
                    user_titles, title_usage = result
                elif name == "golden_quote" and isinstance(result, tuple):
                    golden_quotes, quote_usage = result
+                elif name == "chat_quality" and isinstance(result, tuple):
+                    chat_quality_review, quality_usage = result
+                    if not isinstance(quality_usage, TokenUsage):
+                        quality_usage = TokenUsage()

            # 合并Token使用统计
            total_usage = TokenUsage(
                prompt_tokens=topic_usage.prompt_tokens
                + title_usage.prompt_tokens
-                + quote_usage.prompt_tokens,
+                + quote_usage.prompt_tokens
+                + quality_usage.prompt_tokens,
                completion_tokens=topic_usage.completion_tokens
                + title_usage.completion_tokens
-                + quote_usage.completion_tokens,
+                + quote_usage.completion_tokens
+                + quality_usage.completion_tokens,
                total_tokens=topic_usage.total_tokens
                + title_usage.total_tokens
-                + quote_usage.total_tokens,
+                + quote_usage.total_tokens
+                + quality_usage.total_tokens,
            )

            logger.info(
-                f"并发分析完成 - 话题: {len(topics)}, 称号: {len(user_titles)}, 金句: {len(golden_quotes)}"
+                f"并发分析完成 - 话题: {len(topics)}, 称号: {len(user_titles)}, 金句: {len(golden_quotes)}, 质量锐评: {1 if chat_quality_review else 0}"
+            )
+            return (
+                topics,
+                user_titles,
+                golden_quotes,
+                total_usage,
+                chat_quality_review,
            )
-            return topics, user_titles, golden_quotes, total_usage

        except Exception as e:
            logger.error(f"并发分析失败: {e}")
-            return [], [], [], TokenUsage()
+            return [], [], [], TokenUsage(), None

    async def analyze_incremental_concurrent(
        self,
        messages: list[dict],
        umo: str | None = None,
-        topics_per_batch: int = 3,
-        quotes_per_batch: int = 3,
+        topics_per_batch: int = 2,
+        quotes_per_batch: int = 1,
        topic_enabled: bool = True,
        golden_quote_enabled: bool = True,
-    ) -> tuple[list[SummaryTopic], list[GoldenQuote], TokenUsage]:
+        chat_quality_enabled: bool = False,
+    ) -> tuple[list[SummaryTopic], list[GoldenQuote], TokenUsage, QualityReview | None]:
        """
        增量分析模式的并发执行方法。
        仅执行话题分析和金句分析（用户称号分析在最终报告时执行），
@@ -311,7 +345,7 @@ class LLMAnalyzer(IAnalysisProvider):
                session_id = f"incr_{timestamp}"

            logger.info(
-                f"开始增量并发分析 (话题:{topic_enabled}/{topics_per_batch}, 金句:{golden_quote_enabled}/{quotes_per_batch})，"
+                f"开始增量并发分析 (话题:{topic_enabled}/{topics_per_batch}, 金句:{golden_quote_enabled}/{quotes_per_batch}, 质量锐评:{chat_quality_enabled})，"
                f"消息数量: {len(messages)}，会话ID: {session_id}"
            )

@@ -342,14 +376,24 @@ class LLMAnalyzer(IAnalysisProvider):
                    )
                    task_names.append("golden_quote")

+                if chat_quality_enabled:
+                    tasks.append(
+                        self.chat_quality_analyzer.analyze_quality(
+                            messages, umo, session_id
+                        )
+                    )
+                    task_names.append("chat_quality")
+
                if not tasks:
-                    return [], [], TokenUsage()
+                    return [], [], TokenUsage(), None

                results = await asyncio.gather(*tasks, return_exceptions=True)

                # 处理结果
                topics, topic_usage = [], TokenUsage()
                golden_quotes, quote_usage = [], TokenUsage()
+                chat_quality_review = None
+                quality_usage = TokenUsage()

                for i, result in enumerate(results):
                    name = task_names[i]
@@ -361,20 +405,29 @@ class LLMAnalyzer(IAnalysisProvider):
                        topics, topic_usage = result
                    elif name == "golden_quote" and isinstance(result, tuple):
                        golden_quotes, quote_usage = result
+                    elif name == "chat_quality" and isinstance(result, tuple):
+                        chat_quality_review, quality_usage = result
+                        if not isinstance(quality_usage, TokenUsage):
+                            quality_usage = TokenUsage()

                # 合并Token使用统计
                total_usage = TokenUsage(
-                    prompt_tokens=topic_usage.prompt_tokens + quote_usage.prompt_tokens,
+                    prompt_tokens=topic_usage.prompt_tokens
+                    + quote_usage.prompt_tokens
+                    + quality_usage.prompt_tokens,
                    completion_tokens=topic_usage.completion_tokens
-                    + quote_usage.completion_tokens,
-                    total_tokens=topic_usage.total_tokens + quote_usage.total_tokens,
+                    + quote_usage.completion_tokens
+                    + quality_usage.completion_tokens,
+                    total_tokens=topic_usage.total_tokens
+                    + quote_usage.total_tokens
+                    + quality_usage.total_tokens,
                )

                logger.info(
-                    f"增量并发分析完成 - 话题: {len(topics)}, 金句: {len(golden_quotes)}, "
+                    f"增量并发分析完成 - 话题: {len(topics)}, 金句: {len(golden_quotes)}, 质量锐评: {1 if chat_quality_review else 0}, "
                    f"Token消耗: {total_usage.total_tokens}"
                )
-                return topics, golden_quotes, total_usage
+                return topics, golden_quotes, total_usage, chat_quality_review

            finally:
                # 无论成功或失败，都要恢复原始的最大数量设置
@@ -383,7 +436,7 @@ class LLMAnalyzer(IAnalysisProvider):

        except Exception as e:
            logger.error(f"增量并发分析失败: {e}", exc_info=True)
-            return [], [], TokenUsage()
+            return [], [], TokenUsage(), None

    def _save_debug_messages(self, messages: list[dict], session_id: str):
        """
@@ -6,9 +6,11 @@
 from .info_utils import InfoUtils
 from .json_utils import (
    extract_golden_quotes_with_regex,
+    extract_quality_with_regex,
    extract_topics_with_regex,
    extract_user_titles_with_regex,
    fix_json,
+    parse_json_object_response,
    parse_json_response,
 )
 from .llm_utils import (
@@ -18,16 +20,18 @@ from .llm_utils import (
 )

 __all__ = [
-    # JSON处理工具
+    # JSON processing utilities
    "fix_json",
    "parse_json_response",
+    "parse_json_object_response",
    "extract_topics_with_regex",
    "extract_user_titles_with_regex",
    "extract_golden_quotes_with_regex",
-    # LLM工具
+    "extract_quality_with_regex",
+    # LLM utilities
    "call_provider_with_retry",
    "extract_token_usage",
    "extract_response_text",
-    # 信息工具
+    # Info utilities
    "InfoUtils",
 ]
@@ -85,7 +85,7 @@ def parse_json_response(
    result_text: str, data_type: str
 ) -> tuple[bool, list[dict] | None, str | None]:
    """
-    统一的JSON解析方法
+    统一的JSON解析方法（用于JSON数组响应）

    Args:
        result_text: LLM返回的原始文本
@@ -134,6 +134,78 @@ def parse_json_response(
        return False, None, error_msg


+def parse_json_object_response(
+    result_text: str, data_type: str
+) -> tuple[bool, dict | None, str | None]:
+    """
+    统一的JSON解析方法（用于JSON对象响应，如聊天质量分析）
+
+    与 parse_json_response 不同，此函数用于解析返回单个 JSON 对象 {...}
+    而非数组 [{...}, {...}] 的场景。
+
+    解析策略：
+    1. 先去除 markdown 代码块标记
+    2. 直接解析原始 JSON（避免 fix_json 破坏中文引号等合法内容）
+    3. 若直接解析失败，再使用 fix_json 修复后重试
+
+    Args:
+        result_text: LLM返回的原始文本
+        data_type: 数据类型标识（用于日志）
+
+    Returns:
+        (成功标志, 解析后的字典, 错误消息)
+    """
+    try:
+        # 1. 去除 markdown 代码块标记
+        raw_text = result_text.strip()
+        raw_text = re.sub(r"```(?:json)?\s*", "", raw_text)
+        raw_text = re.sub(r"```\s*$", "", raw_text)
+        raw_text = raw_text.strip()
+
+        # 2. 提取 JSON 对象
+        json_match = re.search(r"\{.*\}", raw_text, re.DOTALL)
+        if not json_match:
+            error_msg = f"{data_type}响应中未找到JSON对象"
+            logger.warning(error_msg)
+            return False, None, error_msg
+
+        json_text = json_match.group()
+        logger.debug(f"{data_type}分析JSON原文: {json_text[:500]}...")
+
+        # 3. 尝试直接解析（保留原始文本，避免中文引号被破坏）
+        try:
+            data = json.loads(json_text)
+            logger.info(f"{data_type}直接解析成功")
+            return True, data, None
+        except json.JSONDecodeError:
+            logger.debug(f"{data_type}直接解析失败，尝试修复JSON...")
+
+        # 4. 使用 fix_json 修复后重试
+        fixed_json = fix_json(json_text)
+        fixed_match = re.search(r"\{.*\}", fixed_json, re.DOTALL)
+        if fixed_match:
+            try:
+                data = json.loads(fixed_match.group())
+                logger.info(f"{data_type}修复后解析成功")
+                return True, data, None
+            except json.JSONDecodeError as e:
+                error_msg = f"{data_type}JSON修复后解析仍失败: {e}"
+                logger.warning(error_msg)
+                return False, None, error_msg
+
+        error_msg = f"{data_type}修复后未找到JSON对象"
+        return False, None, error_msg
+
+    except json.JSONDecodeError as e:
+        error_msg = f"{data_type}JSON解析失败: {e}"
+        logger.warning(error_msg)
+        return False, None, error_msg
+    except Exception as e:
+        error_msg = f"{data_type}解析异常: {e}"
+        logger.error(error_msg)
+        return False, None, error_msg
+
+
 def extract_topics_with_regex(result_text: str, max_topics: int) -> list[dict]:
    """
    使用正则表达式提取话题信息
@@ -280,3 +352,58 @@ def extract_golden_quotes_with_regex(result_text: str, max_count: int) -> list[d
    except Exception as e:
        logger.error(f"金句正则表达式提取失败: {e}")
        return []
+
+
+def extract_quality_with_regex(result_text: str) -> dict | None:
+    """
+    使用正则表达式提取聊天质量分析数据
+
+    当 JSON 解析失败时作为降级方案使用。
+
+    Args:
+        result_text: LLM 返回的原始文本
+
+    Returns:
+        解析后的质量分析字典，失败返回 None
+    """
+    try:
+        title_m = re.search(r'"title"\s*:\s*"([^"]*(?:\\.[^"]*)*)"', result_text)
+        subtitle_m = re.search(r'"subtitle"\s*:\s*"([^"]*(?:\\.[^"]*)*)"', result_text)
+        summary_m = re.search(r'"summary"\s*:\s*"([^"]*(?:\\.[^"]*)*)"', result_text)
+
+        # Extract dimensions array
+        dims_match = re.search(r'"dimensions"\s*:\s*\[(.*?)\]', result_text, re.DOTALL)
+        dims = []
+        if dims_match:
+            dim_objects = re.findall(
+                r'\{[^}]*"name"\s*:\s*"([^"]*)"[^}]*'
+                r'"percentage"\s*:\s*([\d.]+)[^}]*'
+                r'"comment"\s*:\s*"([^"]*(?:\\.[^"]*)*)"[^}]*\}',
+                dims_match.group(1),
+            )
+            for dm in dim_objects:
+                dims.append(
+                    {
+                        "name": dm[0],
+                        "percentage": float(dm[1]),
+                        "comment": dm[2],
+                    }
+                )
+
+        if not dims:
+            logger.warning("聊天质量正则提取未找到有效维度数据")
+            return None
+
+        data = {
+            "title": title_m.group(1) if title_m else "聊天质量锐评",
+            "subtitle": subtitle_m.group(1) if subtitle_m else "今天的群里发生了什么？",
+            "dimensions": dims,
+            "summary": summary_m.group(1) if summary_m else "今天也是充满活力的一天。",
+        }
+
+        logger.info(f"聊天质量正则表达式提取成功，提取到 {len(dims)} 个维度")
+        return data
+
+    except Exception as e:
+        logger.error(f"聊天质量正则表达式提取失败: {e}")
+        return None
@@ -164,6 +164,12 @@ class ConfigManager:
            "golden_quote_analysis_enabled", True
        )

+    def get_chat_quality_analysis_enabled(self) -> bool:
+        """获取是否启用聊天质量分析"""
+        return self._get_group("analysis_features").get(
+            "chat_quality_analysis_enabled", False
+        )
+
    def get_max_topics(self) -> int:
        """获取最大话题数量"""
        return self._get_group("analysis_features").get("max_topics", 5)
@@ -196,6 +202,10 @@ class ConfigManager:
        """获取用户称号分析最大token数"""
        return self._get_group("llm").get("user_title_max_tokens", 4096)

+    def get_quality_max_tokens(self) -> int:
+        """获取聊天质量分析最大token数"""
+        return self._get_group("llm").get("quality_max_tokens", 4096)
+
    def get_debug_mode(self) -> bool:
        """获取是否启用调试模式"""
        return self._get_group("basic").get("debug_mode", False)
@@ -281,6 +291,14 @@ class ConfigManager:
            return prompt
        return ""

+    def get_quality_analysis_prompt(self, style: str = "quality_prompt") -> str:
+        """获取聊天质量分析提示词模板"""
+        prompts_config = self._get_group("prompts").get("quality_analysis_prompts", {})
+        prompt = prompts_config.get(style, "")
+        if prompt:
+            return prompt
+        return ""
+
    def set_topic_analysis_prompt(self, prompt: str):
        """设置话题分析提示词模板"""
        prompts = self._ensure_group("prompts")
@@ -379,6 +397,13 @@ class ConfigManager:
        )
        self.config.save_config()

+    def set_chat_quality_analysis_enabled(self, enabled: bool):
+        """设置是否启用聊天质量分析"""
+        self._ensure_group("analysis_features")["chat_quality_analysis_enabled"] = (
+            enabled
+        )
+        self.config.save_config()
+
    def set_max_topics(self, count: int):
        """设置最大话题数量"""
        self._ensure_group("analysis_features")["max_topics"] = count
@@ -42,6 +42,12 @@ class ReportGenerator(IReportGenerator):
        )
        self._avatar_session = None

+    async def close(self):
+        """关闭资源"""
+        if self._avatar_session:
+            await self._avatar_session.close()
+            self._avatar_session = None
+
    async def generate_image_report(
        self,
        analysis_result: dict,
@@ -73,9 +79,10 @@ class ReportGenerator(IReportGenerator):
                nickname_getter=nickname_getter,
            )

-            # 先渲染HTML模板（使用异步方法）
-            image_template = await self.html_templates.get_image_template_async()
-            html_content = self._render_html_template(image_template, render_payload)
+            # 先渲染HTML模板（使用 Jinja2 渲染器以支持逻辑标签）
+            html_content = self.html_templates.render_template(
+                "image_template.html", **render_payload
+            )

            # 检查HTML内容是否有效
            if not html_content:
@@ -229,9 +236,10 @@ class ReportGenerator(IReportGenerator):
            )
            logger.info(f"PDF 渲染数据准备完成，包含 {len(render_data)} 个字段")

-            # 生成 HTML 内容（使用异步方法）
-            pdf_template = await self.html_templates.get_pdf_template_async()
-            html_content = self._render_html_template(pdf_template, render_data)
+            # 生成 HTML 内容（使用 Jinja2 渲染器以支持逻辑标签）
+            html_content = self.html_templates.render_template(
+                "pdf_template.html", **render_data
+            )

            # 检查HTML内容是否有效
            if not html_content:
@@ -388,6 +396,37 @@ class ReportGenerator(IReportGenerator):
        )
        logger.info(f"活跃度图表HTML生成完成，长度: {len(hourly_chart_html)}")

+        # 生成聊天质量锐评HTML
+        chat_quality_html = ""
+        chat_quality_review = analysis_result.get("chat_quality_review")
+        if not chat_quality_review and hasattr(stats, "chat_quality_review"):
+            chat_quality_review = stats.chat_quality_review
+
+        if chat_quality_review:
+            # 如果是对象，转为字典（为了统一渲染）
+            if hasattr(chat_quality_review, "dimensions"):
+                review_data = {
+                    "title": chat_quality_review.title,
+                    "subtitle": chat_quality_review.subtitle,
+                    "dimensions": [
+                        {
+                            "name": d.name,
+                            "percentage": d.percentage,
+                            "comment": d.comment,
+                            "color": d.color,
+                        }
+                        for d in chat_quality_review.dimensions
+                    ],
+                    "summary": chat_quality_review.summary,
+                }
+            else:
+                review_data = chat_quality_review
+
+            chat_quality_html = self.html_templates.render_template(
+                "chat_quality_item.html", **review_data
+            )
+            logger.info(f"聊天质量锐评HTML生成完成，长度: {len(chat_quality_html)}")
+
        # 准备最终渲染数据
        render_data = {
            "current_date": datetime.now().strftime("%Y年%m月%d日"),
@@ -401,6 +440,7 @@ class ReportGenerator(IReportGenerator):
            "titles_html": titles_html,
            "quotes_html": quotes_html,
            "hourly_chart_html": hourly_chart_html,
+            "chat_quality_html": chat_quality_html,
            "total_tokens": stats.token_usage.total_tokens
            if stats.token_usage.total_tokens
            else 0,
@@ -506,30 +546,6 @@ class ReportGenerator(IReportGenerator):
            return True
        return normalized == str(user_id).strip()

-    def _render_html_template(self, template: str, data: dict) -> str:
-        """HTML模板渲染，使用 {{key}} 占位符格式
-
-        Args:
-            template: HTML模板字符串
-            data: 渲染数据字典
-        """
-        result = template
-
-        for key, value in data.items():
-            # 统一使用双大括号格式 {{key}}
-            placeholder = "{{" + key + "}}"
-            result = result.replace(placeholder, str(value))
-
-        # 检查是否还有未替换的占位符
-        import re
-
-        if remaining_placeholders := re.findall(r"\{\{[^}]+\}\}", result):
-            logger.warning(
-                f"未替换的占位符 ({len(remaining_placeholders)}个): {remaining_placeholders[:10]}"
-            )
-
-        return result
-
    @staticmethod
    def _safe_url_for_log(url: str | None) -> str:
        """对日志中的 URL 进行脱敏，避免泄露 token。"""
@@ -0,0 +1,25 @@
+<div style="background: white; border: 1px solid #ddd; padding: 20px; margin-bottom: 24px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); border-left: 5px solid #007bff;">
+    <div style="margin-bottom: 15px;">
+        <h3 style="margin: 0; font-size: 1.5em; color: #222;">{{ title }}</h3>
+        {% if subtitle %}<span style="color: #666; font-size: 0.9em;">{{ subtitle }}</span>{% endif %}
+    </div>
+    
+    <div style="margin-bottom: 15px;">
+        {% for dimension in dimensions %}
+        <div style="margin-bottom: 15px;">
+            <div style="display: flex; justify-content: space-between; margin-bottom: 5px;">
+                <span style="font-weight: 600;">{{ dimension.name }}</span>
+                <span style="color: #007bff; font-weight: 700;">{{ dimension.percentage }}%</span>
+            </div>
+            <div style="height: 12px; background: #f0f0f0; border-radius: 6px; overflow: hidden;">
+                <div style="height: 100%; width: {{ dimension.percentage }}%; background-color: {{ dimension.color }}; border-radius: 6px;"></div>
+            </div>
+            <p style="margin: 8px 0 0; color: #555; font-size: 0.9em; line-height: 1.5;">{{ dimension.comment }}</p>
+        </div>
+        {% endfor %}
+    </div>
+    
+    <div style="background: #f8f9fa; border-top: 1px solid #eee; padding: 12px; font-size: 0.95em; color: #333; font-style: italic;">
+        {{ summary }}
+    </div>
+</div>
@@ -435,8 +435,9 @@
            color: var(--text-muted);
            text-align: center;
            padding: 30px;
-            font-size: 0.8em;
+            font-size: 0.85em;
            border-top: 1px solid var(--border);
+            line-height: 1.8;
        }

        /* Responsive */
@@ -518,14 +519,21 @@
                {{hourly_chart_html}}
            </div>

+            {% if chat_quality_html %}
+            <div class="section full-width-section">
+                <h2 class="section-title">群聊质量分析</h2>
+                {{ chat_quality_html }}
+            </div>
+            {% endif %}
+
            {{topics_html}}
            {{titles_html}}
            {{quotes_html}}
        </div>
        <div class="footer">
-            由 AstrBot QQ群日常分析插件 生成 | {{current_datetime}} | SXP-Simon/astrbot_plugin_qq_group_daily_analysis<br>
-            <small style="opacity: 0.8; font-size: 0.9em; margin-top: 8px; display: block;">AI分析消耗：{{total_tokens}}
-                tokens (输入: {{prompt_tokens}}, 输出: {{completion_tokens}})</small>
+            由 AstrBot QQ群日常分析插件 生成 | {{current_datetime}}<br>
+            SXP-Simon/astrbot_plugin_qq_group_daily_analysis<br>
+            <span style="opacity: 0.7;">AI分析消耗：{{total_tokens}} tokens (输入: {{prompt_tokens}}, 输出: {{completion_tokens}})</span>
        </div>
    </div>
 </body>
@@ -0,0 +1,25 @@
+<div style="background: #000; border: 1px solid #0f0; padding: 15px; margin-bottom: 20px; box-shadow: 0 0 10px #0f0; color: #0f0; font-family: 'Consolas', 'Monaco', 'Courier New', monospace;">
+    <div style="text-align: center; margin-bottom: 15px;">
+        <h3 style="margin: 0; color: #0f0;">[ STATUS: {{ title }} ]</h3>
+        {% if subtitle %}<div style="font-size: 0.8em; color: #0c0;"># {{ subtitle }}</div>{% endif %}
+    </div>
+    
+    <div>
+        {% for dimension in dimensions %}
+        <div style="margin-bottom: 12px;">
+            <div style="display: flex; justify-content: space-between; font-size: 0.9em; margin-bottom: 4px;">
+                <span>> {{ dimension.name }}</span>
+                <span>{{ dimension.percentage }}%</span>
+            </div>
+            <div style="height: 10px; background: #030; border: 1px solid #0f0; overflow: hidden;">
+                <div style="height: 100%; width: {{ dimension.percentage }}%; background-color: #0f0;"></div>
+            </div>
+            <p style="margin: 4px 0 0 10px; font-size: 0.8em; color: #0a0;">// {{ dimension.comment }}</p>
+        </div>
+        {% endfor %}
+    </div>
+    
+    <div style="border-top: 1px solid #0f0; margin-top: 10px; padding-top: 10px; line-height: 1.4; color: #0f0;">
+        <span style="font-weight: bold;">[LOG]:</span> {{ summary }}
+    </div>
+</div>
@@ -499,7 +499,7 @@
            display: flex;
            justify-content: space-between;
            font-family: var(--font-mono);
-            font-size: 0.8rem;
+            font-size: 0.9rem;
            color: var(--text-secondary);
        }

@@ -543,7 +543,17 @@
                    </div>
                </div>

+                {% if chat_quality_html %}
+                <div>
+                    <div class="section-header">
+                        <span class="prompt">$</span> analyze --quality
+                    </div>
+                    {{ chat_quality_html }}
+                </div>
+                {% endif %}
+
                <!-- Topics moved into left column -->
+                {% if topics_html %}
                <div>
                    <div class="section-header">
                        <span class="prompt">$</span> ls ./topics
@@ -552,6 +562,7 @@
                        {{topics_html}}
                    </div>
                </div>
+                {% endif %}
            </div>

            <!-- Right Side: Visuals & Members -->
@@ -571,6 +582,7 @@
                </div>

                <!-- User Titles grouped here -->
+                {% if titles_html %}
                <div style="margin-top: 20px;">
                    <div class="section-header">
                        <span class="prompt">$</span> cat ./user_titles
@@ -579,8 +591,10 @@
                        {{titles_html}}
                    </div>
                </div>
+                {% endif %}

                <!-- Golden Quotes grouped here -->
+                {% if quotes_html %}
                <div style="margin-top: 20px;">
                    <div class="section-header">
                        <span class="prompt">$</span> grep -r "golden_quotes"
@@ -589,6 +603,7 @@
                        {{quotes_html}}
                    </div>
                </div>
+                {% endif %}
            </div>
        </div>

@@ -0,0 +1,187 @@
+<style>
+    .retro-quality-item {
+        background: #FFFFFF;
+        border: 3px solid var(--c-text);
+        padding: 24px;
+        margin-bottom: 25px;
+        font-family: var(--font-mono);
+        color: var(--c-text);
+        box-shadow: 8px 8px 0 rgba(0, 0, 0, 0.1);
+        position: relative;
+    }
+
+    .retro-quality-item::before {
+        content: '';
+        position: absolute;
+        top: 0; left: 0; right: 0; height: 6px;
+        background: repeating-linear-gradient(90deg, var(--c-accent), var(--c-accent) 15px, var(--c-dec-3) 15px, var(--c-dec-3) 30px);
+    }
+
+    .retro-quality-header {
+        display: flex;
+        justify-content: space-between;
+        align-items: flex-end;
+        margin-bottom: 20px;
+        border-bottom: 2px solid var(--c-text);
+        padding-bottom: 10px;
+        margin-top: 15px;
+    }
+
+    .retro-theme-title {
+        font-family: var(--font-display);
+        font-size: 2em;
+        font-weight: bold;
+        text-transform: uppercase;
+        letter-spacing: 1px;
+        color: var(--c-dec-1);
+    }
+
+    .retro-time-range {
+        font-size: 1.1em;
+        font-weight: bold;
+        background: var(--c-dec-3);
+        color: #fff;
+        padding: 4px 10px;
+        border: 2px solid var(--c-text);
+        box-shadow: 2px 2px 0 var(--c-text);
+    }
+
+    .retro-dimension-bar-container {
+        margin: 25px 0;
+        background: #EEEEEE;
+        border: 2px solid var(--c-text);
+        height: 40px;
+        display: flex;
+        width: 100%;
+        position: relative;
+        box-shadow: inset 0 2px 5px rgba(0, 0, 0, 0.1);
+    }
+
+    .retro-dimension-segment {
+        height: 100%;
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        overflow: hidden;
+        border-right: 2px solid var(--c-text);
+        white-space: nowrap;
+        font-size: 0.9em;
+        font-weight: bold;
+        transition: filter 0.2s;
+    }
+    
+    .retro-dimension-segment:hover {
+        filter: brightness(1.1);
+    }
+
+    .retro-dimension-segment:last-child {
+        border-right: none;
+    }
+
+    .retro-comments-grid {
+        display: grid;
+        grid-template-columns: repeat(2, 1fr);
+        gap: 20px;
+        margin-bottom: 25px;
+    }
+
+    .retro-comment-box {
+        background: #FAFAFA;
+        border: 2px solid var(--c-text);
+        border-top: 4px solid var(--c-dec-2);
+        padding: 15px;
+        font-size: 0.95em;
+        line-height: 1.5;
+        box-shadow: 3px 3px 0 rgba(0, 0, 0, 0.05);
+    }
+
+    .retro-comment-title {
+        display: inline-block;
+        font-weight: bold;
+        margin-bottom: 10px;
+        color: var(--c-dec-1);
+        text-transform: uppercase;
+        border-bottom: 2px solid var(--c-grid);
+        padding-bottom: 4px;
+        width: 100%;
+    }
+
+    .retro-summary-section {
+        margin-top: 25px;
+        padding: 18px;
+        background: var(--c-dec-1);
+        color: #fff;
+        border: 3px solid var(--c-text);
+        font-size: 1.1em;
+        line-height: 1.6;
+        position: relative;
+        box-shadow: 4px 4px 0 var(--c-text);
+    }
+
+    .retro-summary-label {
+        background: var(--c-accent);
+        color: #fff;
+        padding: 4px 12px;
+        font-weight: bold;
+        margin-right: 15px;
+        display: inline-block;
+        font-family: var(--font-display);
+        letter-spacing: 1px;
+        border: 2px solid #fff;
+    }
+</style>
+
+<div class="retro-quality-item">
+    <div class="retro-quality-header">
+        <div class="retro-theme-title">{{ title }}</div>
+        {% if subtitle %}<div class="retro-time-range">{{ subtitle }}</div>{% endif %}
+    </div>
+
+    <!-- Stacked dimension bar chart -->
+    <div class="retro-dimension-bar-container">
+        {% set total = 0 %}
+        {% for dim in dimensions %}
+            {% set total = total + dim.percentage %}
+        {% endfor %}
+
+        {% set scale = 1 %}
+        {% if total > 100 %}
+            {% set scale = 100 / total %}
+        {% elif total > 0 %}
+            {% set scale = 100 / total %}
+        {% endif %}
+
+        {% set bg_colors = ['var(--c-accent)', 'var(--c-dec-1)', 'var(--c-dec-3)', 'var(--c-dec-2)', '#FFBA08', '#273C75', '#E07A5F', '#8D99AE'] %}
+        {% set txt_colors = ['#ffffff', '#ffffff', '#000000', '#ffffff', '#000000', '#ffffff', '#000000', '#ffffff'] %}
+        
+        {% for dim in dimensions %}
+            {% if dim.percentage > 0 %}
+            <div class="retro-dimension-segment"
+                style="width: {{ dim.percentage * scale }}%; background-color: {{ bg_colors[loop.index0 % bg_colors|length] }}; color: {{ txt_colors[loop.index0 % txt_colors|length] }};"
+                title="{{ dim.comment }}">
+                <span style="display: inline-block; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; max-width: 100%; padding: 0 5px;">
+                    {{ dim.name }} {{ dim.percentage }}%
+                </span>
+            </div>
+            {% endif %}
+        {% endfor %}
+    </div>
+
+    <!-- Dimension comments adapted to a retro dual-column layout -->
+    <div class="retro-comments-grid">
+        {% for dim in dimensions %}
+            {% if dim.percentage > 0 %}
+            <div class="retro-comment-box">
+                <div class="retro-comment-title">{{ dim.name }}</div>
+                <div style="color: var(--c-text);">{{ dim.comment }}</div>
+            </div>
+            {% endif %}
+        {% endfor %}
+    </div>
+
+    <!-- System Summary Section -->
+    <div class="retro-summary-section">
+        <span class="retro-summary-label">OVERVIEW</span>
+        <span>{{ summary }}</span>
+    </div>
+</div>
@@ -640,7 +640,7 @@
            padding: 60px 0;
            border-top: 8px solid var(--c-text);
            font-family: var(--font-mono);
-            font-size: 0.8rem;
+            font-size: 0.9rem;
            display: flex;
            flex-direction: column;
            gap: 20px;
@@ -772,48 +772,70 @@
        <div class="grid-container">
            <div class="content-card">
                <div class="activity-wrapper">
-                    {{hourly_chart_html}}
+                    {{hourly_chart_html|safe}}
                </div>
            </div>
        </div>

+        <!-- Chat Quality Review -->
+        {% if chat_quality_html %}
+        <div class="section-label">
+            Quality Analysis <span>STABILITY_METRICS</span>
+        </div>
+        <div class="grid-container">
+            <div style="grid-column: span 12;">
+                {{ chat_quality_html|safe }}
+            </div>
+        </div>
+        {% endif %}
+
        <!-- Topics -->
+        {% if topics_html %}
        <div class="section-label">
            Thread Matrix <span>TOPICS_MODULE</span>
        </div>
        <div class="topic-list">
-            {{topics_html}}
+            {{topics_html|safe}}
        </div>
+        {% endif %}

        <!-- Titles -->
+        {% if titles_html %}
        <div class="section-label">
            Operator Registry <span>TITLES_MODULE</span>
        </div>
        <div class="grid-container">
            <div class="content-card">
                <div class="title-grid">
-                    {{titles_html}}
+                    {{titles_html|safe}}
                </div>
            </div>
        </div>
+        {% endif %}

        <!-- Quotes -->
+        {% if quotes_html %}
        <div class="section-label">
            Golden Lines <span>QUOTES_MODULE</span>
        </div>
        <div class="quote-list">
-            {{quotes_html}}
+            {{quotes_html|safe}}
        </div>
+        {% endif %}

        <footer>
-            <div>CRT_EXPORT_CHANNEL // QQ群日常分析插件</div>
-            <div class="footer-tokens">
-                <span>Total Tokens // {{total_tokens}}</span>
-                <span>Prompt // {{prompt_tokens}}</span>
-                <span>Completion // {{completion_tokens}}</span>
+            <div class="footer-row">
+                <div>CRT_EXPORT_CHANNEL // QQ群日常分析插件</div>
+                <div class="footer-author">SXP-Simon / astrbot_plugin_qq_group_daily_analysis</div>
            </div>
-            <div class="footer-author">SXP-Simon / astrbot_plugin_qq_group_daily_analysis</div>
-            <div>END_OF_LINE_</div>
+            <div class="footer-row">
+                <div class="footer-tokens">
+                    <span>Total Tokens // {{total_tokens}}</span>
+                    <span>Prompt // {{prompt_tokens}}</span>
+                    <span>Completion // {{completion_tokens}}</span>
+                </div>
+            </div>
+            <div>{{current_datetime}} // END_OF_LINE_</div>
        </footer>
    </div>
 </body>
@@ -835,7 +835,7 @@
        <div class="grid-container">
            <div class="content-box">
                <div class="activity-wrapper">
-                    {{hourly_chart_html}}
+                    {{hourly_chart_html|safe}}
                </div>
            </div>
        </div>
@@ -845,7 +845,7 @@
            Thread Matrix <span>TOPICS_MODULE</span>
        </div>
        <div class="topic-list">
-            {{topics_html}}
+            {{topics_html|safe}}
        </div>

        <!-- Titles -->
@@ -855,7 +855,7 @@
        <div class="grid-container">
            <div class="content-box">
                <div class="title-grid">
-                    {{titles_html}}
+                    {{titles_html|safe}}
                </div>
            </div>
        </div>
@@ -865,7 +865,7 @@
            Golden Lines <span>QUOTES_MODULE</span>
        </div>
        <div class="quote-list">
-            {{quotes_html}}
+            {{quotes_html|safe}}
        </div>

        <footer>
@@ -0,0 +1,21 @@
+<div style="background: #f9f9f9; border-radius: 8px; padding: 12px; margin-bottom: 20px; border: 1px solid #eee;">
+    <h3 style="margin: 0 0 10px; font-size: 1.2em; color: #333; text-align: center;">{{ title }}</h3>
+    {% if subtitle %}<p style="margin: -5px 0 10px; font-size: 0.85em; color: #666; text-align: center;">{{ subtitle }}</p>{% endif %}
+    
+    {% for dimension in dimensions %}
+    <div style="margin-bottom: 10px;">
+        <div style="display: flex; justify-content: space-between; font-size: 0.9em; margin-bottom: 3px;">
+            <span>{{ dimension.name }}</span>
+            <span>{{ dimension.percentage }}%</span>
+        </div>
+        <div style="height: 6px; background: #e0e0e0; border-radius: 3px; overflow: hidden;">
+            <div style="height: 100%; width: {{ dimension.percentage }}%; background-color: {{ dimension.color }};"></div>
+        </div>
+        <p style="margin: 3px 0 0; font-size: 0.8em; color: #555;">{{ dimension.comment }}</p>
+    </div>
+    {% endfor %}
+    
+    <div style="border-top: 1px solid #eee; margin-top: 10px; padding-top: 8px; font-size: 0.9em; line-height: 1.4; color: #444;">
+        {{ summary }}
+    </div>
+</div>
@@ -40,16 +40,23 @@
        {{hourly_chart_html}}
    </div>

+    {% if chat_quality_html %}
+    <div class="section">
+        <h2>群聊质量分析</h2>
+        {{ chat_quality_html }}
+    </div>
+    {% endif %}
+
    {{topics_html}}

    {{titles_html}}

    {{quotes_html}}

-    <div class="footer">
-        Generated by AstrBot | {{current_datetime}}
-        <br>
-        <small>Token Usage: {{total_tokens}} (Prompt: {{prompt_tokens}}, Completion: {{completion_tokens}})</small>
+    <div class="footer" style="margin-top: 30px; padding: 15px; border-top: 1px solid #ccc; color: #666; font-size: 0.85em; text-align: center; line-height: 1.8;">
+        Generated by AstrBot | {{current_datetime}}<br>
+        SXP-Simon/astrbot_plugin_qq_group_daily_analysis<br>
+        <span style="opacity: 0.7;">Token Usage: {{total_tokens}} (Prompt: {{prompt_tokens}}, Completion: {{completion_tokens}})</span>
    </div>
 </body>

@@ -0,0 +1,27 @@
+<div style="background: #fff5f5; border: 2px solid #ff4d4f; border-radius: 12px; padding: 20px; margin-bottom: 25px; box-shadow: 0 4px 12px rgba(255, 77, 79, 0.1); position: relative; overflow: hidden;">
+    <div style="position: absolute; top: -10px; right: -10px; opacity: 0.1; font-size: 80px; transform: rotate(15deg);">福</div>
+    
+    <div style="text-align: center; margin-bottom: 20px;">
+        <h3 style="margin: 0; color: #cf1322; font-size: 1.6em; border-bottom: 2px solid #ff4d4f; display: inline-block; padding-bottom: 5px;">{{ title }}</h3>
+        {% if subtitle %}<p style="margin: 8px 0 0; color: #fa541c; font-weight: bold;">{{ subtitle }}</p>{% endif %}
+    </div>
+    
+    <div>
+        {% for dimension in dimensions %}
+        <div style="margin-bottom: 15px;">
+            <div style="display: flex; justify-content: space-between; font-size: 1em; margin-bottom: 6px; color: #333; font-weight: bold;">
+                <span>{{ dimension.name }}</span>
+                <span style="color: #cf1322;">{{ dimension.percentage }}%</span>
+            </div>
+            <div style="height: 12px; background: #ffe8e6; border-radius: 6px; overflow: hidden; border: 1px solid #ffccc7;">
+                <div style="height: 100%; width: {{ dimension.percentage }}%; background: linear-gradient(90deg, #ff4d4f, #cf1322);"></div>
+            </div>
+            <p style="margin: 6px 0 0; font-size: 0.9em; color: #8c8c8c; font-style: italic;">{{ dimension.comment }}</p>
+        </div>
+        {% endfor %}
+    </div>
+    
+    <div style="margin-top: 15px; padding: 12px; background: #fff1f0; border-radius: 8px; border-left: 4px solid #f5222d; font-size: 0.95em; color: #434343;">
+        {{ summary }}
+    </div>
+</div>
@@ -633,13 +633,23 @@
        </div>

        <div class="sf-main-flow">
-            <!-- 1. Topics: Full Width -->
+            <!-- 0. Chat Quality Analysis -->
+            {% if chat_quality_html %}
            <div class="sf-section-box">
-                <div class="sf-section-title"><span>🏮</span> 核心话题回顾</div>
+                <div class="sf-section-title"><span>🏺</span> 聊天质量锐评</div>
+                {{ chat_quality_html }}
+            </div>
+            {% endif %}
+
+            <!-- 1. Topics: Full Width -->
+            {% if topics_html %}
+            <div class="sf-section-box">
+                <div class="sf-section-title"><span>&#127982;</span> 核心话题回顾</div>
                <div class="sf-topic-list">
                    {{topics_html}}
                </div>
            </div>
+            {% endif %}

            <!-- 2. Chart: Full Width with SF window -->
            <div class="sf-section-box">
@@ -651,22 +661,27 @@
            </div>

            <!-- 3. Characters: Masonry 2-Column Grid -->
+            {% if titles_html %}
            <div class="sf-section-box">
                <div class="sf-section-title"><span>🎖️</span> 群友风云榜</div>
                {{titles_html}}
            </div>
+            {% endif %}

            <!-- 4. Quotes: Staggered Timeline -->
+            {% if quotes_html %}
            <div class="sf-section-box">
                <div class="sf-section-title"><span>✨</span> 每日金句回响</div>
                <div class="sf-quote-timeline">
                    {{quotes_html}}
                </div>
            </div>
+            {% endif %}
        </div>

        <footer>
            <div>宜 · 总结分析 // {{current_datetime}}</div>
+            <div style="font-size: 0.85rem; margin-top: 8px; opacity: 0.7;">Token 消耗：{{total_tokens}} (Prompt: {{prompt_tokens}}, Completion: {{completion_tokens}})</div>
            <div class="sf-footer-tag">SXP-Simon / astrbot_plugin_qq_group_daily_analysis</div>
        </footer>
    </div>