| name | content-collector |
| description | Automatically collect and archive content from shared links in group chats. When a user shares a link (WeChat articles, Feishu docs, web pages, etc.) in any group chat and asks to archive/collect/save it, this skill triggers to fetch the content, create a Feishu document, and update the knowledge base table. Use when: (1) User shares a link and asks to "收录/转存/保存" content, (2) Need to archive web content to Feishu docs, (3) Building a personal knowledge base from shared links, (4) Organizing learning materials from various sources. |
Content Collector - 链接内容自动收录技能
Overview
This skill enables automatic collection and archiving of content from shared links into a structured knowledge base.
Core Workflow:
Detect Link → Fetch Content → Extract Images → Upload Images to Feishu → Create Feishu Doc → Update Table
When to Use
模式1：主动触发（显式关键词）
当用户消息包含以下触发词时，立即执行收录：
- "收录" / "转存" / "保存" / "存档" / "存一下" / "归档" / "备份" / "收藏"
- "存到知识库" / "加入知识库" / "转飞书"
示例：
- "这个链接收录一下"
- "存到知识库"
- "转存这篇教程"
模式2：静默收录（自动检测）
在群聊场景中，自动检测以下链接并静默收录：
- 飞书文档/表格/Wiki（feishu.cn）
- 微信公众号文章（mp.weixin.qq.com）
- 技术博客/教程站点
- 知识分享类链接
静默收录条件：
- 消息来自群聊（非私聊）
- 消息包含可识别的知识类链接
- 用户没有明确拒绝的意图
两种模式优先级：
检测到主动触发词 → 立即收录（显式模式）
未检测到触发词但检测到链接 → 静默收录（隐式模式）
Supported Link Types
| Type | Example | Fetch Method |
|---|
| WeChat Article | https://mp.weixin.qq.com/s/xxx | kimi_fetch |
| Feishu Doc | https://xxx.feishu.cn/docx/xxx | feishu_fetch_doc |
| Feishu Wiki | https://xxx.feishu.cn/wiki/xxx | feishu_fetch_doc |
| Web Page | General URLs | kimi_fetch / web_fetch |
Supported Image Sources
| Source | Example | Priority | Notes |
|---|
| Markdown image |  | High | 直接替换为飞书 image_key |
HTML <img src> | <img src="/assets/a.png"> | High | 相对路径需转绝对路径 |
| Lazy-load image | data-src, data-original | Medium | 常见于公众号/博客懒加载 |
srcset candidate | srcset="a 1x, b 2x" | Medium | 优先选择清晰度更高的候选图 |
| Feishu file token | boxcn... / img_v3_... | High | 需要走飞书素材下载后再上传 |
Global Availability (全局可用配置)
生效范围：所有用户、所有群聊
本技能已配置为全局可用，支持以下对象：
| 对象类型 | 支持状态 | 说明 |
|---|
| 所有用户 | ✅ 可用 | 任何用户分享的链接均可被收录 |
| 所有群聊 | ✅ 可用 | 支持技能中心群、养虾群、学习群等所有群组 |
| 私聊消息 | ✅ 可用 | 用户私信分享链接也可触发收录 |
| 多渠道 | ✅ 可用 | 飞书、其他渠道统一支持 |
权限说明：
- 任何用户均可触发收录（无需管理员权限）
- 收录的文档统一存储到指定的知识库目录
- 所有用户均可查看已收录的文档
Installation & Permission Check (安装与权限检查)
在正式使用本技能前，系统必须自动或引导用户完成以下权限校验，以确保流程不中断：
1. 飞书权限清单
| 权限项 | 验证工具 | 目的 |
|---|
| OAuth 授权 | feishu_oauth | 获取操作飞书文档和表格的用户凭证 |
| 知识库写入权限 | feishu_create_doc | 确保能在指定的 Space ID 下创建节点 |
| 多维表格编辑权限 | feishu_bitable_app_table_record | 确保能向指定的 app_token 写入记录 |
| 图片上传权限 | feishu_im_bot_upload | 允许将本地图片同步至飞书素材库 |
2. 预检流程 (Pre-flight Check)
每次“安装”或配置更新后，执行以下检查：
- 验证 Space ID 可访问性：尝试在指定目录下获取节点列表。
- 验证 Table 结构：检查
关键词、原链接、图片数量、图片处理状态 等字段是否存在（后两者可选）。
- 静默测试：如果权限不足，立即通过
feishu_oauth 弹出授权引导，而非在执行收录时报错。
Configuration
Before using, ensure these are configured in MEMORY.md:
## Content Collector Config
- **Knowledge Base Table**: `[Your Bitable App Token]` (Bitable app_token)
- **Table URL**: [Your Bitable Table URL]
- **Default Table ID**: `[Your Table ID]` (will auto-detect if available)
- **Knowledge Base Space ID**: `[Your Space ID]` (所有文档创建在此知识库下)
- **Knowledge Base URL**: [Your Knowledge Base Homepage URL]
- **Content Categories**: 技术教程, 实战案例, 产品文档, 学习笔记
- **Global Access**: 所有用户可用，所有群聊可用
- **Image Fetch Mode**: `all` / `cover_only`（默认 `all`）
- **Image Max Count**: `20`（单篇文档最多处理图片数）
- **Image Max Size MB**: `10`（单图超过阈值则跳过）
- **Image Timeout Sec**: `20`（下载超时）
- **Image Allowed Types**: `jpg,png,gif,webp`
- **Image Fallback**: `keep_original_link=true`
Note:
- This skill updates ONLY the configured knowledge base table. Do not create or update any other tables.
- All created documents must be saved under the designated Knowledge Base using wiki_node parameter.
- Global Access: 所有用户、所有群聊均可使用本技能，收录的文档对全员可见。
- 图片抓取默认开启；若用户明确要求“纯文字收录”，可跳过图片处理。
📚 知识库文档存储规则（必遵守）
所有收录的文档必须按照以下规则分类存储到知识库对应目录：
知识库目录结构
请参考各项目或团队定义的知识库标准目录结构进行存储。收录的文档通常存放在“素材”或“归档”类目录下。
文档分类映射规则
| 内容分类 | 存储目录 (wiki_node) | 命名前缀 | 示例 |
|---|
| 技术教程 | F9pFw9dxTiXmpsk5bNlco704nag (内容文档) | 📖 | 📖 [标题] |
| 实战案例 | F9pFw9dxTiXmpsk5bNlco704nag (内容文档) | 🛠️ | 🛠️ [标题] |
| 产品文档 | F9pFw9dxTiXmpsk5bNlco704nag (内容文档) | 📄 | 📄 [标题] |
| 学习笔记 | F9pFw9dxTiXmpsk5bNlco704nag (内容文档) | 💡 | 💡 [标题] |
| 热点资讯 | F9pFw9dxTiXmpsk5bNlco704nag (内容文档) | 🔥 | 🔥 [标题] |
| 设计技能 | F9pFw9dxTiXmpsk5bNlco704nag (内容文档) | 🎨 | 🎨 [标题] |
| 工具推荐 | F9pFw9dxTiXmpsk5bNlco704nag (内容文档) | 🔧 | 🔧 [标题] |
| 训练营 | F9pFw9dxTiXmpsk5bNlco704nag (内容文档) | 🎓 | 🎓 [标题] |
文档命名规范
[Emoji前缀] [原标题] | 收录日期
示例：
📖 OpenClaw保姆级教程 | 2026-03-08
🛠️ 火山方舟自动化报表案例 | 2026-03-08
🔥 GPT-5.4发布解读 | 2026-03-08
文档模板
# [Emoji] [原标题]
> 📌 **元信息**
> - 来源：[原始来源]
> - 原文链接：[原始URL]
> - 收录时间：YYYY-MM-DD
> - 内容分类：[技术教程/实战案例/产品文档/学习笔记/热点资讯/设计技能/工具推荐/训练营]
> - 关键词：[关键词1, 关键词2, 关键词3]
---
## 📋 核心要点
[3-5条核心内容摘要]
---
## 📝 正文内容
[完整的转存内容]
---
## 🔗 相关链接
- 原文链接：[原始URL]
- 知识库索引：[素材池文档索引链接]
---
📚 **收录时间**：YYYY-MM-DD
🏷️ **分类**：[分类名]
🔖 **关键词**：[关键词]
自动更新素材索引
每次收录完成后，必须：
- 更新多维表格 - 添加新记录到素材池表格
- 更新素材索引文档 - 在「📚 内容素材池文档索引」中添加条目
- 更新分类统计 - 更新各分类的文档数量和占比
Workflow
Step 1: Detect and Parse Link
Extract URL from user message using regex or direct extraction.
Step 2: Fetch Content (正文 + 原始结构)
Choose appropriate fetch method based on URL pattern:
For WeChat articles:
raw = kimi_fetch(url="https://mp.weixin.qq.com/s/xxx")
For Feishu docs:
raw = feishu_fetch_doc(doc_id="https://xxx.feishu.cn/docx/xxx")
For general web pages:
raw = kimi_fetch(url="https://example.com/article")
raw = web_fetch(url="https://example.com/article")
Standardized Output (必须统一):
fetched = {
"title": raw.get("title", ""),
"markdown": raw.get("markdown", raw.get("content", "")),
"raw_html": raw.get("html", ""),
"source_url": original_url
}
Step 3: Analyze and Categorize
智能分类判断：
根据内容特征自动判断分类：
| 判断依据 | 分类 |
|---|
| 包含"安装/配置/部署/教程"等词 | 📖 技术教程 |
| 包含"案例/实战/项目/演示"等词 | 🛠️ 实战案例 |
| 包含"安全/公告/版本/功能"等词 | 📄 产品文档 |
| 包含"学习/成长/指南/笔记"等词 | 💡 学习笔记 |
| 包含"发布/新功能/热点"等词 | 🔥 热点资讯 |
| 包含"设计/Prompt/美学"等词 | 🎨 设计技能 |
| 包含"工具/CLI/插件"等词 | 🔧 工具推荐 |
| 包含"训练营/课程/教学"等词 | 🎓 训练营 |
Step 4: Process Images (图片处理)
在创建飞书文档前，必须执行图片抓取与回填，目标是“最大化保留原文图片、最小化失败影响正文”。
Image Processing Workflow v2:
import os
import re
from urllib.parse import urljoin
IMG_MD_RE = re.compile(r'!\[(.*?)\]\(([^)]+)\)')
IMG_HTML_RE = re.compile(r'<img[^>]+(?:src|data-src|data-original)=["\']([^"\']+)["\']', re.I)
IMG_SRCSET_RE = re.compile(r'<img[^>]+srcset=["\']([^"\']+)["\']', re.I)
def normalize_img_ref(ref: str, base_url: str) -> str:
ref = (ref or "").strip()
if not ref:
return ""
if ref.startswith(("http://", "https://")):
return ref
if ref.startswith("//"):
return "https:" + ref
if ref.startswith(("img_v3_", "boxcn", "file_", "AAM")):
return ref
return urljoin(base_url, ref)
def pick_srcset_candidate(srcset_value: str) -> str:
parts = [x.strip() for x in (srcset_value or "").split(",") if x.strip()]
if not parts:
return ""
return parts[-1].split(" ")[0].strip()
def extract_image_candidates(markdown_content: str, raw_html: str, source_url: str):
candidates = []
for alt, ref in IMG_MD_RE.findall(markdown_content or ""):
candidates.append({"alt": alt or "image", "ref": normalize_img_ref(ref, source_url), "from_md": True})
for ref in IMG_HTML_RE.findall(raw_html or ""):
normalized = normalize_img_ref(ref, source_url)
if normalized:
candidates.append({"alt": "image", "ref": normalized, "from_md": False})
for srcset_value in IMG_SRCSET_RE.findall(raw_html or ""):
candidate = normalize_img_ref(pick_srcset_candidate(srcset_value), source_url)
if candidate:
candidates.append({"alt": "image", "ref": candidate, "from_md": False})
seen, ordered = set(), []
for item in candidates:
if item["ref"] and item["ref"] not in seen:
seen.add(item["ref"])
ordered.append(item)
return ordered
def fetch_and_upload_images(markdown_content, raw_html, source_url, cfg):
candidates = extract_image_candidates(markdown_content, raw_html, source_url)
max_count = int(cfg.get("image_max_count", 20))
max_bytes = int(cfg.get("image_max_size_mb", 10)) * 1024 * 1024
replace_map = {}
uploaded_extra = []
failed = []
total = 0
success = 0
for item in candidates[:max_count]:
ref = item["ref"]
total += 1
tmp_path = None
try:
if ref.startswith(("http://", "https://")):
tmp_path = download_image_to_local(
ref,
timeout=int(cfg.get("image_timeout_sec", 20)),
max_bytes=max_bytes,
allowed_types=cfg.get("image_allowed_types", "jpg,png,gif,webp")
)
else:
tmp_path = download_feishu_media_to_local(
ref,
max_bytes=max_bytes,
allowed_types=cfg.get("image_allowed_types", "jpg,png,gif,webp")
)
upload_result = feishu_im_bot_upload(action="upload_image", file_path=tmp_path)
image_key = upload_result.get("image_key")
if not image_key:
raise RuntimeError("empty image_key")
success += 1
replace_map[ref] = image_key
if not item["from_md"]:
uploaded_extra.append((item["alt"], image_key))
except Exception as e:
failed.append({"ref": ref, "reason": str(e)[:120]})
finally:
if tmp_path and os.path.exists(tmp_path):
os.remove(tmp_path)
processed = markdown_content
for src, image_key in replace_map.items():
processed = processed.replace(f"({src})", f"({image_key})")
if uploaded_extra:
lines = ["", "---", "", "## 🖼️ 原文配图（自动抓取）", ""]
for alt, image_key in uploaded_extra:
lines.append(f"")
processed += "\n".join(lines)
if failed:
processed += (
"\n\n> ⚠️ 部分图片处理失败，已保留正文收录。"
f" 成功 {success}/{total}，失败 {len(failed)}。"
)
image_stats = {
"total": total,
"success": success,
"failed": len(failed),
"failed_refs": failed
}
return processed, image_stats
Fallback Strategy:
- 单张图片失败不影响整篇文档收录
- 原文中存在但未成功托管的图片，保留原链接并记录失败原因
- 文档末尾自动追加“原文配图/失败提示”区块，确保信息不丢失
Step 5: Create Feishu Document (按知识库规则存储)
Convert processed markdown to Feishu document with proper organization:
processed_markdown_content, image_stats = fetch_and_upload_images(
markdown_content=fetched["markdown"],
raw_html=fetched["raw_html"],
source_url=fetched["source_url"],
cfg=config
)
content_category = classify_content(processed_markdown_content)
emoji_prefix = get_emoji_prefix(content_category)
wiki_node = get_wiki_node_by_category(content_category)
doc_title = f"{emoji_prefix} {original_title} | {today_date}"
doc_content = f"""# {emoji_prefix} {original_title}
> 📌 **元信息**
> - 来源：{source_name}
> - 原文链接：{original_url}
> - 收录时间：{today_date}
> - 内容分类：{content_category}
> - 关键词：{keywords}
> - 图片处理：成功 {image_stats["success"]}/{image_stats["total"]}（失败 {image_stats["failed"]}）
---
## 📋 核心要点
{extract_key_points(processed_markdown_content, 5)}
---
## 📝 正文内容
{processed_markdown_content}
---
## 🔗 相关链接
- 原文链接：{original_url}
- 知识库索引：[Your Index Document URL]
---
📅 **收录时间**：{today_date}
🏷️ **分类**：{content_category}
🔖 **关键词**：{keywords}
"""
feishu_create_doc(
title=doc_title,
markdown=doc_content,
wiki_node=wiki_node
)
存储目录映射：
| 分类 | wiki_node | 目录名 |
|---|
| 所有素材 | F9pFw9dxTiXmpsk5bNlco704nag | 04-内容素材 |
IMPORTANT:
- All documents MUST be created under the designated Knowledge Base using wiki_node parameter.
- Documents must follow the naming convention:
[Emoji] [Title] | [Date]
- Documents must use the standard template with metadata section.
Step 6: Update Knowledge Base Table
Add record to the Bitable knowledge base (ONLY update this specific table):
feishu_bitable_app_table_record(
action="create",
app_token="[Your App Token]",
table_id="[Your Table ID]",
fields={
"关键词": keywords,
"内容分类": content_category,
"文档标题": [{"text": original_title, "type": "text"}],
"来源": [{"text": source_name, "type": "text"}],
"核心要点": [{"text": key_points, "type": "text"}],
"飞书文档链接": {"link": new_doc_url, "text": "飞书文档", "type": "url"},
"原链接": {"link": original_url, "text": "原文链接", "type": "url"},
"图片数量": image_stats["total"],
"图片处理状态": f'{image_stats["success"]}/{image_stats["total"]} 成功',
"图片失败数": image_stats["failed"]
}
)
Table Fields:
| Field | Type | Description |
|---|
| 关键词 | Text | Search keywords for the content |
| 内容分类 | Single Select | Category: 📖技术教程/🛠️实战案例/📄产品文档/💡学习笔记/🔥热点资讯/🎨设计技能/🔧工具推荐/🎓训练营 |
| 文档标题 | Text | Title of the archived document |
| 来源 | Text | Original source name |
| 核心要点 | Text | Key points summary (3-5 items) |
| 飞书文档链接 | URL | Link to the created Feishu document |
| 原链接 | URL | Original source URL - 新增字段，存储采集的原始链接 |
| 图片数量 | Number | 本次检测到的图片总数 |
| 图片处理状态 | Text | 图片托管结果，例如 8/10 成功 |
| 图片失败数 | Number | 上传失败图片数，用于质量监控 |
IMPORTANT: Only update the configured knowledge base table. Never create or modify other tables.
Step 7: Update Content Index Document
After creating the document and updating the table, MUST update the index document:
index_doc = feishu_fetch_doc(doc_id="[Your Index Doc ID]")
new_index_entry = f"| {original_title} | {source_name} | [查看]({new_doc_url}) |\n"
update_category_stats(content_category)
update_total_count()
或者直接追加到索引文档的末尾：
feishu_update_doc(
doc_id="[Your Index Doc ID]",
mode="append",
markdown=f"""
| {original_title} | {source_name} | [查看]({new_doc_url}) |
"""
)
Content Categorization Guide
| Category | Emoji | Description | Examples |
|---|
| 技术教程 | 📖 | Step-by-step technical guides | Installation, configuration, API usage |
| 实战案例 | 🛠️ | Real-world implementation examples | Case studies, project demos |
| 产品文档 | 📄 | Product features, security notices | Release notes, security advisories |
| 学习笔记 | 💡 | Conceptual knowledge, methodologies | Best practices, architecture guides |
| 热点资讯 | 🔥 | Breaking news, releases | GPT-5.4, new features |
| 设计技能 | 🎨 | Design, prompts, aesthetics | AJ's prompts, design guides |
| 工具推荐 | 🔧 | Tools, CLI, plugins | gws, trae, autotools |
| 训练营 | 🎓 | Courses, bootcamps, tutorials | OpenClaw bootcamp |
分类判断优先级：
- 优先根据用户指定分类
- 其次根据标题关键词
- 最后根据内容特征自动判断
- 不确定时标记为"待分类"，请用户确认
Delete Record Process
When user replies "删除" or "删除 [keyword]":
feishu_bitable_app_table_record(
action="list",
app_token="[Your App Token]",
table_id="[Your Table ID]",
filter={
"conjunction": "and",
"conditions": [
{"field_name": "关键词", "operator": "contains", "value": [keyword]}
]
}
)
feishu_bitable_app_table_record(
action="delete",
app_token="[Your App Token]",
table_id="[Your Table ID]",
record_id="record_id_to_delete"
)
Error Handling
Common Issues
| Error | Cause | Solution |
|---|
| Fetch timeout | Network issue or heavy content | Retry with longer timeout, or use alternative fetch method |
| Unauthenticated | OAuth token expired or not authed | Trigger feishu_oauth to refresh user credentials |
| Permission denied | No write access to Space/Table | Check if user/bot has 'Editor' role in Feishu |
| Content too long | Exceeds API limits | Truncate or split into multiple documents |
| Table update failed | Wrong app_token or table_id | Verify configuration in MEMORY.md |
| Field Missing | "原链接" field not in table | Add the field to Bitable manually or via API |
| Image download failed | Source anti-hotlinking / timeout | Retry with headers, then keep original link |
| Image too large | Exceeds size limit | Compress or skip and log warning |
| Invalid image type | Unsupported format or broken file | Skip image and continue document creation |
Recovery Steps
- If fetch fails → Try alternative method (kimi_fetch → web_fetch)
- If image fetch/upload fails → Keep original image link and append warning block
- If Feishu doc creation fails → Check OAuth status
- If table update fails → Verify table structure and field names
- Always report partial success (doc created but table not updated)
Response Template
收录成功响应（流式Post格式）
{
"msg_type": "post",
"content": {
"post": {
"zh_cn": {
"title": "✅ 收录完成",
"content": [
[
{"tag": "text", "text": "📄 "},
{"tag": "text", "text": "{emoji} {原标题} | {日期}", "style": {"bold": true}}
],
[{"tag": "text", "text": ""}],
[
{"tag": "text", "text": "💡 文档亮点：", "style": {"bold": true}}
],
[
{"tag": "text", "text": "• {亮点1}"}
],
[
{"tag": "text", "text": "• {亮点2}"}
],
[
{"tag": "text", "text": "• {亮点3}"}
],
[{"tag": "text", "text": ""}],
[
{"tag": "text", "text": "🔗 "},
{"tag": "a", "text": "查看飞书文档", "href": "{文档URL}"}
]
]
}
}
}
}
简洁输出示例：
✅ 收录完成
📄 📖 OpenClaw配置指南 | 2026-03-08
💡 文档亮点：
• 完整配置示例，含9大模块详解
• 多Agent扩展配置方案
• 生产环境安全配置建议
🔗 查看飞书文档 → [点击打开](https://xxx.feishu.cn/docx/xxx)
静默收录响应（流式Post格式）
{
"msg_type": "post",
"content": {
"post": {
"zh_cn": {
"title": "✅ 已自动收录",
"content": [
[
{"tag": "text", "text": "📄 "},
{"tag": "text", "text": "{emoji} {原标题}", "style": {"bold": true}}
],
[{"tag": "text", "text": ""}],
[
{"tag": "text", "text": "💡 亮点：{亮点摘要}"}
],
[{"tag": "text", "text": ""}],
[
{"tag": "a", "text": "📎 查看文档", "href": "{文档URL}"}
]
]
}
}
}
}
批量收录响应（流式Post格式）
{
"msg_type": "post",
"content": {
"post": {
"zh_cn": {
"title": "✅ 批量收录完成（{N}份）",
"content": [
[
{"tag": "text", "text": "📄 {emoji1} {标题1}", "style": {"bold": true}}
],
[
{"tag": "text", "text": " 💡 {亮点1}"}
],
[
{"tag": "a", "text": " 🔗 查看", "href": "{链接1}"}
],
[{"tag": "text", "text": ""}],
[
{"tag": "text", "text": "📄 {emoji2} {标题2}", "style": {"bold": true}}
],
[
{"tag": "text", "text": " 💡 {亮点2}"}
],
[
{"tag": "a", "text": " 🔗 查看", "href": "{链接2}"}
]
]
}
}
}
}
输出原则：
- 必须流式Post格式 - 使用 msg_type: post
- 只包含3个核心要素：
- 文件名称（📄 Emoji + 标题 + 日期）
- 文档亮点（💡 3-5条核心要点）
- 飞书链接（🔗 点击查看）
- 不输出其他信息 - 不显示分类、不显示表格更新、不显示统计
- 保持简洁 - 每份文档3-5行内容
Best Practices
- Always verify content was fetched correctly before creating documents
- Extract key insights from the content for the summary
- Use appropriate category based on content nature
- Generate relevant keywords for better searchability
- Keep source attribution clear for copyright respect
- Handle partial failures gracefully - document what succeeded and what failed
- Update index document - Every new document must be added to the index
- Follow naming convention - Use [Emoji] [Title] | [Date] format
- Store in correct directory - Use wiki_node to place in right category
- Image-first fallback - 图片失败不阻断正文入库，优先保证知识沉淀完整性
收录完成检查清单 (Checklist)
每次收录必须完成以下所有步骤：
任何一步未完成，视为收录失败！
Integration with Memory
After each collection, update MEMORY.md:
### YYYY-MM-DD - Content Collection
- **新增收录**: [Title]
- **来源**: [Source]
- **分类**: [Category]
- **知识库状态**: 共[N]条记录
- **索引更新**: ✅ 已更新
This skill is part of the core knowledge management system. Execute with care and attention to detail.
附录：图片抓取能力 v2（执行约束）
必须满足的目标
- 不丢图：Markdown 图片 + HTML 图片都要尝试收录。
- 不阻断：图片失败不能阻断正文文档创建。
- 可观测：表格必须记录图片处理统计（总数/成功/失败）。
- 可回溯：失败图片需保留原始引用，便于后续补抓。
推荐执行策略
- 先用
extract_image_candidates 统一收集并去重。
- 每篇文档最多处理
image_max_count 张，防止超时。
- 单图限制
image_max_size_mb，超过阈值直接跳过并计入失败。
- 仅允许常见格式（jpg/png/gif/webp），其余按失败处理。
- 所有临时文件在
finally 中删除，避免磁盘残留。
最小可行验收标准（MVP）
- 输入含 10 张图的公众号文章，最终文档中至少 8 张能正常显示。
- 即使图片全部失败，也必须产出正文文档并回写表格记录。
- 收录响应中必须返回飞书文档链接，且不暴露内部异常堆栈。
图片处理方案 v2.0 - 2026-03-17
