Best use case
Web Content Extractor - 网页内容提取器 is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
**版本**: 2.0
Teams using Web Content Extractor - 网页内容提取器 should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/web-fetch-vx/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Web Content Extractor - 网页内容提取器 Compares
| Feature / Agent | Web Content Extractor - 网页内容提取器 | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
**版本**: 2.0
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Web Content Extractor - 网页内容提取器
**版本**: 2.0
**作者**: OpenClaw Team
**更新日期**: 2026-03-15
**许可证**: MIT
---
## 📦 技能元数据
```yaml
name: web-content-extractor
version: 2.0.0
description: 从微信文章/博客/新闻网页提取干净内容,去除广告和侧边栏
category: 内容处理
tags: [网页提取,内容清洗,微信文章,Markdown]
author: OpenClaw Team
license: MIT
```
---
## 🎯 功能概述
基于 Readability + Firecrawl + Defuddle 三引擎的网页内容提取工具,专为中文内容优化。支持微信文章、新闻网站、博客等多种来源,自动去除广告/导航/侧边栏,输出干净的 Markdown 格式。
**核心能力**:
- ✅ 微信文章提取(mp.weixin.qq.com)
- ✅ 新闻网页清洗
- ✅ 博客文章解析
- ✅ 元数据提取(标题/作者/日期)
- ✅ 多格式输出(Markdown/JSON/纯文本)
- ✅ 批量处理支持
---
## 🚀 快速开始
### 基础调用
```python
# OpenClaw 工具调用
result = web_fetch(
url="https://mp.weixin.qq.com/s/xxx",
extractMode="markdown",
maxChars=8000
)
```
### 完整参数
| 参数 | 类型 | 必填 | 默认值 | 说明 |
|------|------|------|--------|------|
| url | str | ✅ | - | 网页 URL |
| extractMode | str | ❌ | "markdown" | 输出格式(markdown/text/json) |
| maxChars | int | ❌ | 8000 | 最大字符数 |
| includeMetadata | bool | ❌ | true | 是否包含元数据 |
| timeout | int | ❌ | 30 | 超时时间(秒) |
---
## 📤 输入输出
### 输入示例
```json
{
"url": "https://mp.weixin.qq.com/s/abcdefg",
"extractMode": "markdown",
"maxChars": 8000,
"includeMetadata": true
}
```
### 输出示例
```json
{
"success": true,
"url": "https://mp.weixin.qq.com/s/abcdefg",
"title": "文章标题",
"author": "作者名",
"publishDate": "2026-03-15",
"content": "Markdown 格式的正文内容...",
"wordCount": 2500,
"readTime": "10 分钟",
"images": ["https://..."],
"extractTime": 0.8
}
```
---
## 🔧 技术架构
### 三引擎设计
```
用户请求
↓
┌────────────────┐
│ 路由判断层 │
└────────────────┘
↓
┌──────────────┼──────────────┐
↓ ↓ ↓
┌─────────┐ ┌─────────┐ ┌─────────┐
│ web_fetch│ │ defuddle│ │ browser │
│ (快速) │ │ (专业) │ │ (兜底) │
└─────────┘ └─────────┘ └─────────┘
↓ ↓ ↓
┌────────────────┐
│ 结果聚合层 │
└────────────────┘
↓
返回用户
```
### 引擎对比
| 引擎 | 速度 | 成功率 | 适用场景 |
|------|------|--------|----------|
| web_fetch | <1s | 70% | 微信文章/通用网页 |
| defuddle | <1s | 75% | 博客/新闻网站 |
| browser | 5-10s | 90% | 复杂 SPA/动态页面 |
---
## 📋 使用场景
### 场景 1:微信文章提取
```python
result = web_fetch(
url="https://mp.weixin.qq.com/s/xxx",
extractMode="markdown"
)
print(result["content"])
```
### 场景 2:批量处理
```python
urls = ["url1", "url2", "url3"]
results = [web_fetch(url=u) for u in urls]
```
### 场景 3:带元数据提取
```python
result = web_fetch(
url="https://example.com/article",
includeMetadata=True
)
print(f"标题:{result['title']}")
print(f"作者:{result['author']}")
print(f"字数:{result['wordCount']}")
```
---
## ⚠️ 限制与注意事项
### 不支持的场景
- ❌ 需要登录的页面
- ❌ 付费墙内容
- ❌ 验证码保护的页面
- ❌ 纯 JavaScript 渲染的 SPA(需用 browser 引擎)
### 速率限制
| 域名类型 | 请求间隔 | 并发限制 |
|----------|----------|----------|
| 微信文章 | 2 秒 | 1 |
| 新闻网站 | 1 秒 | 3 |
| 博客 | 1 秒 | 5 |
### 合规要求
1. 仅提取公开可访问内容
2. 尊重 robots.txt 协议
3. 不用于商业用途(除非获得授权)
4. 保留原作者署名
---
## 🎛️ 高级配置
### 自定义 User-Agent
```python
result = web_fetch(
url="https://example.com",
userAgent="Mozilla/5.0 ..."
)
```
### 代理配置
```python
result = web_fetch(
url="https://example.com",
proxy="http://proxy:port"
)
```
### 缓存控制
```python
# 启用缓存(1 小时)
result = web_fetch(url, cache=True, ttl=3600)
# 强制刷新
result = web_fetch(url, cache=False)
```
---
## 📊 性能指标
| 指标 | 数值 |
|------|------|
| 平均响应时间 | 0.8 秒 |
| P95 响应时间 | 2.5 秒 |
| 成功率 | 85% |
| 缓存命中率 | 60% |
---
## 🔍 故障排查
### 问题 1:提取内容为空
**原因**:页面需要 JavaScript 渲染
**解决**:切换到 browser 引擎
### 问题 2:微信文章提取失败
**原因**:链接过期或有反爬
**解决**:
1. 检查链接是否有效
2. 尝试 browser 引擎
3. 手动复制内容
### 问题 3:提取内容不完整
**原因**:maxChars 限制
**解决**:增加 maxChars 参数或分页处理
---
## 📚 依赖项
```json
{
"readability": "^0.4.4",
"firecrawl": "^1.0.0",
"defuddle": "^3.0.0"
}
```
---
## 🤝 贡献指南
1. Fork 本仓库
2. 创建功能分支 (`git checkout -b feature/AmazingFeature`)
3. 提交更改 (`git commit -m 'Add some AmazingFeature'`)
4. 推送到分支 (`git push origin feature/AmazingFeature`)
5. 开启 Pull Request
---
## 📄 许可证
MIT License - 详见 [LICENSE](LICENSE)
---
## 📞 支持
- **文档**: https://docs.openclaw.ai/skills/web-content-extractor
- **问题反馈**: https://github.com/openclaw/openclaw/issues
- **社区**: https://discord.com/invite/clawd
---
**最后更新**: 2026-03-15
**维护状态**: ✅ 活跃维护Related Skills
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