diff --git a/plugins/message_summary/main.py b/plugins/message_summary/main.py
index a73bb52..4f26519 100644
--- a/plugins/message_summary/main.py
+++ b/plugins/message_summary/main.py
@@ -516,11 +516,393 @@ class MessageSummaryPlugin(MessagePluginInterface):
rendered = "".join(html_parts)
return cls._sanitize_rendered_html(rendered)
- def _render_summary_template_html(self, group_name: str, summary_text: str) -> str:
+ @staticmethod
+ def _strip_markdown_inline(text: str) -> str:
+ """清理行内 Markdown 标记,输出可直接展示的纯文本。
+
+ 设计说明:
+ 1. 仅处理常见行内语法(粗体/斜体/代码/链接),避免把模板渲染复杂度继续堆高;
+ 2. 不在这里做 HTML 生成,保证“结构化渲染层”始终输出纯文本;
+ 3. 处理后再交给模板做自动转义,避免注入风险。
+ """
+ cleaned = str(text or "").strip()
+ if not cleaned:
+ return ""
+ # 图片语法: -> alt
+ cleaned = re.sub(r"!\[([^\]]*)\]\([^)]+\)", r"\1", cleaned)
+ # 链接语法:[text](url) -> text
+ cleaned = re.sub(r"\[([^\]]+)\]\([^)]+\)", r"\1", cleaned)
+ # 行内代码:`code` -> code
+ cleaned = re.sub(r"`([^`]+)`", r"\1", cleaned)
+ # 粗体/斜体标记清理
+ cleaned = re.sub(r"(\*\*|__)(.*?)\1", r"\2", cleaned)
+ cleaned = re.sub(r"(\*|_)(.*?)\1", r"\2", cleaned)
+ # 删除零宽字符,避免昵称或内容出现布局异常。
+ cleaned = re.sub(r"[\u200b-\u200f\u2060\ufeff]", "", cleaned)
+ return cleaned.strip()
+
+ @classmethod
+ def _extract_llm_payload_text(cls, summary_text: str) -> str:
+ """从 LLM 返回文本中提取真正的总结正文。
+
+ 兼容场景:
+ 1. 直接返回 Markdown 正文;
+ 2. 返回 JSON 字符串(如 {"text":"..."});
+ 3. 返回被双引号包裹且含转义换行的字符串。
+ """
+ text = str(summary_text or "").strip()
+ if not text:
+ return ""
+
+ # 第一层:尝试按 JSON 解析外层包装。
+ try:
+ if (text.startswith("{") and text.endswith("}")) or (text.startswith("[") and text.endswith("]")):
+ payload = json.loads(text)
+ if isinstance(payload, dict):
+ for key in ("text", "summary", "answer", "content", "result"):
+ value = payload.get(key)
+ if isinstance(value, str) and value.strip():
+ return value.strip()
+ if isinstance(payload, str) and payload.strip():
+ return payload.strip()
+ except Exception:
+ pass
+
+ # 第二层:处理被引号包裹的 JSON 字符串(例如 "\"# 标题\\n内容\"")。
+ try:
+ if text.startswith('"') and text.endswith('"'):
+ decoded = json.loads(text)
+ if isinstance(decoded, str) and decoded.strip():
+ return decoded.strip()
+ except Exception:
+ pass
+
+ return text
+
+ @classmethod
+ def _build_summary_layout_data(cls, summary_text: str) -> Dict[str, Any]:
+ """把 LLM 总结文本重排为模板可直接消费的结构化数据。
+
+ 输出结构:
+ - lead: 导语文本(通常来自一级标题后的首段)
+ - sections: 分节内容,每节包含 title + items
+ - fallback_text: 兜底纯文本(解析失败时仍可展示)
+ """
+ raw_text = cls._extract_llm_payload_text(summary_text)
+ lines = [line.rstrip() for line in str(raw_text or "").splitlines()]
+
+ # 标准化:去除分隔线与空白噪音,减少模板渲染无效块。
+ normalized_lines: List[str] = []
+ for raw_line in lines:
+ line = str(raw_line or "").strip()
+ if not line:
+ normalized_lines.append("")
+ continue
+ if re.fullmatch(r"[-*_]{3,}", line):
+ # Markdown 横线仅用于语义分隔,结构化渲染里直接跳过。
+ continue
+ normalized_lines.append(line)
+
+ document_title = ""
+ lead_lines: List[str] = []
+ sections: List[Dict[str, Any]] = []
+ current_section: Optional[Dict[str, Any]] = None
+ paragraph_buffer: List[str] = []
+ in_code_block = False
+ code_lines: List[str] = []
+
+ def flush_paragraph() -> None:
+ """把段落缓存写入当前分节。"""
+ nonlocal paragraph_buffer, current_section
+ if not paragraph_buffer:
+ return
+ paragraph_text = cls._strip_markdown_inline(" ".join(paragraph_buffer).strip())
+ paragraph_buffer = []
+ if not paragraph_text:
+ return
+ if current_section is None:
+ lead_lines.append(paragraph_text)
+ return
+ current_section["items"].append({"kind": "paragraph", "text": paragraph_text})
+
+ def ensure_section(title: str) -> None:
+ """确保当前分节存在,并切换到新分节。"""
+ nonlocal current_section
+ clean_title = cls._strip_markdown_inline(title) or "未命名章节"
+ current_section = {"title": clean_title, "items": []}
+ sections.append(current_section)
+
+ for line in normalized_lines:
+ # 代码块开始/结束处理。
+ if line.startswith("```"):
+ flush_paragraph()
+ if in_code_block:
+ if current_section is None:
+ ensure_section("代码片段")
+ code_text = "\n".join(code_lines).strip()
+ if code_text:
+ current_section["items"].append({"kind": "code", "text": code_text})
+ code_lines = []
+ in_code_block = False
+ else:
+ in_code_block = True
+ code_lines = []
+ continue
+
+ if in_code_block:
+ code_lines.append(line)
+ continue
+
+ if not line:
+ flush_paragraph()
+ continue
+
+ # 一级标题:用于页面主标题文案,不作为分节。
+ h1_match = re.match(r"^#\s+(.+)$", line)
+ if h1_match:
+ flush_paragraph()
+ title_text = cls._strip_markdown_inline(h1_match.group(1))
+ if title_text:
+ document_title = title_text
+ continue
+
+ # 二级及以下标题统一作为分节标题。
+ section_match = re.match(r"^#{2,6}\s+(.+)$", line)
+ if section_match:
+ flush_paragraph()
+ ensure_section(section_match.group(1))
+ continue
+
+ # 有些模型会输出“1. 标题”这种无井号标题,识别后转成分节。
+ numeric_heading_match = re.match(r"^\d+[.)、]\s+(.+)$", line)
+ if numeric_heading_match and len(cls._strip_markdown_inline(numeric_heading_match.group(1))) <= 40:
+ flush_paragraph()
+ ensure_section(numeric_heading_match.group(1))
+ continue
+
+ # 引用块。
+ quote_match = re.match(r"^>\s*(.+)$", line)
+ if quote_match:
+ flush_paragraph()
+ if current_section is None:
+ ensure_section("精选引用")
+ quote_text = cls._strip_markdown_inline(quote_match.group(1))
+ if quote_text:
+ current_section["items"].append({"kind": "quote", "text": quote_text})
+ continue
+
+ # 无序列表。
+ bullet_match = re.match(r"^[-*+]\s+(.+)$", line)
+ if bullet_match:
+ flush_paragraph()
+ if current_section is None:
+ ensure_section("重点清单")
+ bullet_text = cls._strip_markdown_inline(bullet_match.group(1))
+ if bullet_text:
+ current_section["items"].append({"kind": "bullet", "text": bullet_text})
+ continue
+
+ # 有序列表作为 bullet 统一渲染,避免复杂列表嵌套导致样式凌乱。
+ ordered_match = re.match(r"^\d+[.)]\s+(.+)$", line)
+ if ordered_match:
+ flush_paragraph()
+ if current_section is None:
+ ensure_section("重点清单")
+ ordered_text = cls._strip_markdown_inline(ordered_match.group(1))
+ if ordered_text:
+ current_section["items"].append({"kind": "bullet", "text": ordered_text})
+ continue
+
+ # 其余内容按段落累计,遇到空行或结构标记时再落盘。
+ paragraph_buffer.append(line)
+
+ flush_paragraph()
+ if in_code_block and code_lines:
+ if current_section is None:
+ ensure_section("代码片段")
+ code_text = "\n".join(code_lines).strip()
+ if code_text:
+ current_section["items"].append({"kind": "code", "text": code_text})
+
+ # 清理空章节,并过滤“统计注入段”等不需要重复展示的标题。
+ skip_titles = {"群概览", "tokens", "统计信息", "消息统计"}
+ cleaned_sections: List[Dict[str, Any]] = []
+ for section in sections:
+ if not section.get("items"):
+ continue
+ title_text = str(section.get("title") or "").strip().lower()
+ if title_text in skip_titles:
+ continue
+ cleaned_sections.append(section)
+
+ # 导语优先使用 lead_lines,否则取首个章节首条,保证顶部一定有可读摘要。
+ lead = " ".join(lead_lines).strip()
+ if not lead and cleaned_sections:
+ first_items = cleaned_sections[0].get("items") or []
+ if first_items:
+ lead = str(first_items[0].get("text", "")).strip()
+ if not lead:
+ lead = "暂无总结内容。"
+
+ # 模板兜底纯文本:用于极端解析失败场景。
+ fallback_text = cls._strip_markdown_inline(
+ "\n".join([line for line in normalized_lines if line]).strip()
+ ) or "暂无总结内容。"
+
+ return {
+ "document_title": document_title,
+ "lead": lead,
+ "sections": cleaned_sections,
+ "fallback_text": fallback_text,
+ }
+
+ @staticmethod
+ def _to_int(value: Any) -> int:
+ """安全转换为整数,避免模板渲染阶段因脏数据抛错。"""
+ try:
+ return int(value or 0)
+ except Exception:
+ return 0
+
+ @classmethod
+ def _build_summary_template_metrics(
+ cls,
+ message_stats: Optional[Dict[str, Any]],
+ layout_data: Dict[str, Any],
+ metadata: Optional[Dict[str, Any]] = None,
+ ) -> Dict[str, Any]:
+ """构建 Gemini 模板高信息密度展示所需的统计视图数据。"""
+ # 说明:
+ # 1. 该函数专注“模板展示层”数据准备,不改动业务存储;
+ # 2. 尽量从 message_stats 与结构化 sections 中提取可读指标;
+ # 3. 数据不全时采用稳妥兜底,避免模板渲染失败。
+ stats = message_stats or {}
+ usage = (metadata or {}).get("usage", {}) or {}
+ sections = layout_data.get("sections") or []
+
+ total_count = cls._to_int(stats.get("total_count"))
+ participant_count = cls._to_int(stats.get("participant_count"))
+ text_count = cls._to_int(stats.get("text_count"))
+ image_count = cls._to_int(stats.get("image_count"))
+ video_count = cls._to_int(stats.get("video_count"))
+ link_count = cls._to_int(stats.get("link_count"))
+ emoji_count = cls._to_int(stats.get("emoji_count"))
+
+ # 媒体量:把图片+视频合并,符合卡片化概览阅读习惯。
+ media_count = image_count + video_count
+ section_count = len(sections)
+ bullet_count = 0
+ quote_count = 0
+ code_count = 0
+ for section in sections:
+ for item in section.get("items", []):
+ kind = str(item.get("kind") or "")
+ if kind == "bullet":
+ bullet_count += 1
+ elif kind == "quote":
+ quote_count += 1
+ elif kind == "code":
+ code_count += 1
+
+ # 活跃度等级:仅用于 UI 文案提示,不参与核心业务计算。
+ if total_count >= 1600:
+ activity_badge = "爆炸活跃"
+ elif total_count >= 1000:
+ activity_badge = "高活跃"
+ elif total_count >= 400:
+ activity_badge = "中活跃"
+ else:
+ activity_badge = "常规活跃"
+
+ # 话题雷达:优先取前几节标题,避免依赖额外 LLM 输出字段。
+ topic_tags: List[str] = []
+ for section in sections:
+ title_text = str(section.get("title") or "").strip()
+ if not title_text:
+ continue
+ normalized = re.sub(r"^[\d\W_]+", "", title_text)
+ normalized = re.sub(r"\s+", " ", normalized).strip()
+ if not normalized:
+ continue
+ topic_tags.append(normalized[:24])
+ if len(topic_tags) >= 6:
+ break
+
+ # 核心看点:优先抓 bullet,没有则回退 paragraph。
+ highlights: List[str] = []
+ for section in sections:
+ for item in section.get("items", []):
+ item_text = str(item.get("text") or "").strip()
+ if not item_text:
+ continue
+ if item.get("kind") == "bullet":
+ highlights.append(item_text[:120])
+ if len(highlights) >= 4:
+ break
+ if len(highlights) >= 4:
+ break
+ if not highlights:
+ for section in sections:
+ for item in section.get("items", []):
+ item_text = str(item.get("text") or "").strip()
+ if not item_text:
+ continue
+ highlights.append(item_text[:120])
+ if len(highlights) >= 4:
+ break
+ if len(highlights) >= 4:
+ break
+
+ # 使用统计:读取模型调用元数据,辅助观测总结成本。
+ total_tokens = cls._to_int(usage.get("total_tokens"))
+ prompt_tokens = cls._to_int(usage.get("prompt_tokens"))
+ completion_tokens = cls._to_int(usage.get("completion_tokens"))
+ latency = usage.get("latency")
+ latency_text = f"{latency}s" if latency not in (None, "") else "-"
+
+ return {
+ "kpi_cards": [
+ {"label": "Msgs", "value": total_count, "tone": "slate"},
+ {"label": "Active", "value": participant_count, "tone": "blue"},
+ {"label": "Text", "value": text_count, "tone": "violet"},
+ {"label": "Media", "value": media_count, "tone": "emerald"},
+ ],
+ "mini_stats": [
+ {"label": "Links", "value": link_count},
+ {"label": "Emoji", "value": emoji_count},
+ {"label": "Video", "value": video_count},
+ {"label": "Sections", "value": section_count},
+ {"label": "Bullets", "value": bullet_count},
+ {"label": "Quotes", "value": quote_count},
+ {"label": "Code", "value": code_count},
+ ],
+ "topic_tags": topic_tags,
+ "highlights": highlights,
+ "activity_badge": activity_badge,
+ "token_total": total_tokens,
+ "token_prompt": prompt_tokens,
+ "token_completion": completion_tokens,
+ "latency_text": latency_text,
+ }
+
+ def _render_summary_template_html(
+ self,
+ group_name: str,
+ summary_text: str,
+ message_stats: Optional[Dict[str, Any]] = None,
+ metadata: Optional[Dict[str, Any]] = None,
+ ) -> str:
"""根据模板路径渲染总结图片 HTML。"""
- # 约束:模板只负责展示,正文仍然由模型生成并在此做安全转义后注入。
+ # 约束:
+ # 1. 不再把 LLM 原文直接转 HTML 内嵌到模板;
+ # 2. 先结构化解析文本,再由模板按组件渲染,稳定控制最终排版。
renderer = HtmlTemplateRenderer()
- summary_html = self._summary_markdown_to_html(summary_text)
+ layout_data = self._build_summary_layout_data(summary_text)
+ metrics_data = self._build_summary_template_metrics(
+ message_stats=message_stats,
+ layout_data=layout_data,
+ metadata=metadata,
+ )
# 说明:
# 1. 这里注入“本地字体 CSS”到模板,避免依赖 Google Fonts 等外网资源;
# 2. 字体文件统一从仓库根目录 fonts/ 下读取,便于部署时统一管理;
@@ -531,7 +913,13 @@ class MessageSummaryPlugin(MessagePluginInterface):
{
"title": f"{group_name} 群聊总结",
"generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
- "summary_html": Markup(summary_html),
+ # 兼容字段:保留给旧模板使用,新版 Gemini 模板已不再依赖该字段。
+ "summary_html": Markup(self._summary_markdown_to_html(summary_text)),
+ "summary_doc_title": layout_data.get("document_title", ""),
+ "summary_lead": layout_data.get("lead", ""),
+ "summary_sections": layout_data.get("sections", []),
+ "summary_fallback_text": layout_data.get("fallback_text", ""),
+ "summary_metrics": metrics_data,
"local_font_css": Markup(local_font_css),
},
)
@@ -631,6 +1019,8 @@ class MessageSummaryPlugin(MessagePluginInterface):
self,
answer: str,
group_name: str,
+ message_stats: Optional[Dict[str, Any]],
+ metadata: Optional[Dict[str, Any]],
output_filename: str,
total_timeout: int,
) -> str:
@@ -642,7 +1032,12 @@ class MessageSummaryPlugin(MessagePluginInterface):
# 优先模板渲染,便于后续仅改 HTML 文件完成样式迭代。
if self._summary_image_mode != "markdown":
try:
- html_content = self._render_summary_template_html(group_name=group_name, summary_text=answer)
+ html_content = self._render_summary_template_html(
+ group_name=group_name,
+ summary_text=answer,
+ message_stats=message_stats,
+ metadata=metadata,
+ )
await asyncio.wait_for(html_to_image(html_content, str(output_path)), timeout=total_timeout)
if not output_path.exists() or output_path.stat().st_size < 1024:
raise RuntimeError("模板截图输出文件异常")
@@ -800,8 +1195,12 @@ class MessageSummaryPlugin(MessagePluginInterface):
answer = self._clean_summary_output(response.get("text", ""))
metadata = {"usage": response.get("usage", {}) or {}}
spath = ""
- answer = self._prepend_stats_section(answer, message_stats or {})
- answer = self._append_usage_info(answer, metadata)
+ # 说明:
+ # 1. 模板模式下统计信息由卡片呈现,不再额外拼接“群概览”文本块;
+ # 2. markdown 模式仍保留旧逻辑,保证兼容历史输出。
+ if self._summary_image_mode == "markdown":
+ answer = self._prepend_stats_section(answer, message_stats or {})
+ answer = self._append_usage_info(answer, metadata)
if answer and len(answer.strip()) > 0:
try:
@@ -814,6 +1213,8 @@ class MessageSummaryPlugin(MessagePluginInterface):
spath = await self._render_summary_image(
answer=answer,
group_name=group_name,
+ message_stats=message_stats or {},
+ metadata=metadata,
output_filename=output_path,
total_timeout=total_timeout,
)
diff --git a/plugins/message_summary/templates/gemini_summary_card.html b/plugins/message_summary/templates/gemini_summary_card.html
index 617401d..4dd2aae 100644
--- a/plugins/message_summary/templates/gemini_summary_card.html
+++ b/plugins/message_summary/templates/gemini_summary_card.html
@@ -115,118 +115,106 @@
.meta-value.brand {
color: var(--brand);
}
+ .topic-radar {
+ margin-top: 12px;
+ }
+ .topic-tags {
+ margin-top: 6px;
+ display: flex;
+ flex-wrap: wrap;
+ gap: 6px;
+ }
+ .topic-tag {
+ padding: 3px 8px;
+ border-radius: 999px;
+ font-size: 10px;
+ font-weight: 700;
+ color: #334155;
+ background: #f8fafc;
+ border: 1px solid #e2e8f0;
+ }
.summary-body {
padding: 18px 20px 20px;
}
- .markdown-body {
+ .lead-callout {
+ margin-top: 10px;
+ padding: 10px 12px;
+ background: var(--ok-soft);
+ border-left: 2px solid var(--ok-line);
+ border-radius: 0 6px 6px 0;
font-size: 13px;
- line-height: 1.86;
- color: var(--text);
- word-wrap: break-word;
+ line-height: 1.78;
+ color: #166534;
}
- .markdown-body > *:first-child { margin-top: 0; }
- .markdown-body > *:last-child { margin-bottom: 0; }
- .markdown-body h1,
- .markdown-body h2,
- .markdown-body h3,
- .markdown-body h4,
- .markdown-body h5,
- .markdown-body h6 {
- color: var(--title);
- margin: 16px 0 8px;
- line-height: 1.42;
+ .sections {
+ margin-top: 12px;
+ display: flex;
+ flex-direction: column;
+ gap: 10px;
+ }
+ .section-card {
+ border: 1px solid #e5eaf1;
+ background: #ffffff;
+ border-radius: 8px;
+ padding: 10px 11px;
+ }
+ .section-title {
+ margin: 0 0 8px;
+ font-size: 14px;
font-weight: 800;
+ color: var(--title);
letter-spacing: -.01em;
}
- .markdown-body h1 { font-size: 20px; border-bottom: 1px solid var(--line); padding-bottom: 6px; }
- .markdown-body h2 { font-size: 18px; }
- .markdown-body h3 { font-size: 16px; }
- .markdown-body h4,
- .markdown-body h5,
- .markdown-body h6 { font-size: 14px; }
- .markdown-body p { margin: 10px 0; }
- .markdown-body ul,
- .markdown-body ol {
- margin: 8px 0 12px;
- padding-left: 18px;
+ .section-items {
+ display: flex;
+ flex-direction: column;
+ gap: 6px;
}
- .markdown-body li { margin: 4px 0; }
- .markdown-body blockquote {
- margin: 10px 0;
+ .item-paragraph {
+ margin: 0;
+ font-size: 13px;
+ line-height: 1.82;
+ color: var(--text);
+ }
+ .item-bullet {
+ margin: 0;
+ padding-left: 14px;
+ position: relative;
+ font-size: 13px;
+ line-height: 1.78;
+ color: var(--text);
+ }
+ .item-bullet::before {
+ content: "•";
+ position: absolute;
+ left: 0;
+ top: 0;
+ color: var(--brand);
+ font-weight: 900;
+ }
+ .item-quote {
+ margin: 0;
padding: 8px 10px;
border-left: 3px solid var(--quote-line);
background: var(--quote-bg);
color: #475569;
border-radius: 0 4px 4px 0;
- }
- .markdown-body hr {
- border: none;
- border-top: 1px dashed #dbe3ee;
- margin: 14px 0;
- }
- .markdown-body a {
- color: var(--brand);
- text-decoration: none;
- border-bottom: 1px dotted rgba(37, 99, 235, .5);
- }
- .markdown-body strong {
- color: #1e293b;
- font-weight: 800;
- }
- .markdown-body em {
- color: #475569;
- font-style: italic;
- }
- .markdown-body code {
- /* 说明:代码字体优先使用本地/系统等宽字体栈,保证服务端离线场景可读。 */
- font-family: var(--abot-font-code, "Cascadia Mono", "Consolas", "SFMono-Regular", Menlo, monospace);
font-size: 12px;
- background: #eff6ff;
- color: #1e40af;
- padding: 1px 4px;
- border-radius: 4px;
+ line-height: 1.75;
}
- .markdown-body pre {
- margin: 10px 0;
+ .item-code {
+ margin: 0;
padding: 10px 11px;
border-radius: 6px;
background: var(--code-bg);
color: var(--code-text);
- overflow-x: auto;
border: 1px solid #1e293b;
- }
- .markdown-body pre code {
- background: transparent;
- color: inherit;
- padding: 0;
- border-radius: 0;
+ /* 说明:代码字体优先使用本地/系统等宽字体栈,保证服务端离线场景可读。 */
+ font-family: var(--abot-font-code, "Cascadia Mono", "Consolas", "SFMono-Regular", Menlo, monospace);
font-size: 12px;
line-height: 1.7;
- }
- .markdown-body table {
- width: 100%;
- border-collapse: collapse;
- margin: 10px 0;
- font-size: 11px;
- border: 1px solid #e2e8f0;
- }
- .markdown-body th,
- .markdown-body td {
- border: 1px solid #e2e8f0;
- padding: 6px 7px;
- text-align: left;
- vertical-align: top;
- }
- .markdown-body th {
- background: #f8fafc;
- color: #334155;
- font-weight: 700;
- }
- .markdown-body img {
- max-width: 100%;
- border-radius: 6px;
- border: 1px solid #e2e8f0;
- margin: 6px 0;
+ white-space: pre-wrap;
+ word-break: break-word;
}
.summary-footer {
margin-top: 14px;
@@ -255,6 +243,82 @@
font-size: 12px;
color: #166534;
}
+ .fallback-text {
+ margin-top: 10px;
+ font-size: 12px;
+ line-height: 1.76;
+ color: #475569;
+ white-space: pre-wrap;
+ }
+ .summary-grid {
+ display: grid;
+ grid-template-columns: 1.45fr 1fr;
+ gap: 10px;
+ margin-top: 12px;
+ }
+ .mini-card {
+ border: 1px solid #e5eaf1;
+ border-radius: 8px;
+ padding: 10px 11px;
+ background: #fff;
+ }
+ .mini-title {
+ margin: 0 0 8px;
+ font-size: 10px;
+ text-transform: uppercase;
+ letter-spacing: .08em;
+ color: #94a3b8;
+ font-weight: 800;
+ }
+ .mini-list {
+ display: flex;
+ flex-direction: column;
+ gap: 5px;
+ }
+ .mini-row {
+ display: flex;
+ justify-content: space-between;
+ align-items: center;
+ gap: 8px;
+ font-size: 12px;
+ color: #475569;
+ }
+ .mini-row .value {
+ font-weight: 800;
+ color: #1e293b;
+ font-family: var(--abot-font-code, "Cascadia Mono", "Consolas", monospace);
+ }
+ .highlight-list {
+ display: flex;
+ flex-direction: column;
+ gap: 6px;
+ }
+ .highlight-item {
+ margin: 0;
+ font-size: 12px;
+ line-height: 1.72;
+ color: #334155;
+ padding-left: 12px;
+ position: relative;
+ }
+ .highlight-item::before {
+ content: "";
+ position: absolute;
+ left: 0;
+ top: 7px;
+ width: 5px;
+ height: 5px;
+ border-radius: 50%;
+ background: #2563eb;
+ }
+ @media (max-width: 640px) {
+ .summary-grid {
+ grid-template-columns: 1fr;
+ }
+ .meta-grid {
+ grid-template-columns: repeat(2, minmax(0, 1fr));
+ }
+ }
@@ -265,39 +329,85 @@
-
+