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Install in OpenClaw
/install common-fetcher
Description
统一采集框架 - 支持 RSS/Web/API,207+ 采集源,AI 评分/分类/摘要
README (SKILL.md)
Common-Fetcher
统一采集框架,为 AI Agent 提供强大的信息采集能力。
功能特性
- 🕸️ 多源支持: RSS、网页抓取、API 集成
- 📊 大规模: 207+ 预配置采集源
- 🤖 AI 处理: 自动评分、分类、摘要生成
- ⚡ 高性能: \x3C600ms/30 篇文章
- ✅ 高可靠: 100% 成功率(已验证解析器)
支持的行业
🏭 煤炭行业(27 个采集源)
- 国家级:发改委、能源局等 6 个
- 省级:4 个
- 市级:3 个
- 数据平台:4 个
- 企业自媒体:10 个
🏠 房地产行业(23 个采集源)
- 国家级:住建部、央行等 5 个
- 省级:1 个
- 市级:3 个
- 数据平台:4 个
- 企业自媒体:10 个
🤖 AI 技术(129 个采集源)
- RSS 源:90 个(Hacker News, MIT Tech Review 等)
- 网站/自媒体:39 个
使用方法
CLI 方式
# 抓取煤炭行业数据
common-fetcher --industry coal --output daily.md
# 抓取房地产行业数据
common-fetcher --industry realestate --output daily.md
# 抓取 AI 技术数据
common-fetcher --industry ai --output daily.md
# 自定义采集源
common-fetcher --config custom-sources.json --output daily.md
Node.js API
import { CommonFetcher } from 'common-fetcher';
const fetcher = new CommonFetcher({
industry: 'coal',
maxArticles: 50,
timeout: 15000,
});
const result = await fetcher.fetch();
console.log(`成功抓取 ${result.totalArticles} 篇文章`);
OpenClaw 集成
在 openclaw.json 中配置:
{
"skills": {
"common-fetcher": {
"enabled": true,
"industry": "coal",
"schedule": "0 8 * * *"
}
}
}
架构设计
┌─────────────────────────────────────────┐
│ Common-Fetcher │
├─────────────────────────────────────────┤
│ Source Layer (采集源层) │
│ ├─ RSS 源 │
│ ├─ 网页源 │
│ └─ API 源 │
├─────────────────────────────────────────┤
│ Fetcher Layer (抓取层) │
│ ├─ RSS Fetcher (并发 + 超时) │
│ ├─ Web Scraper (cheerio) │
│ └─ Cache Manager │
├─────────────────────────────────────────┤
│ Processor Layer (处理层) │
│ ├─ 去重 (标题/URL 哈希) │
│ ├─ 时间过滤 │
│ ├─ AI 评分/分类 │
│ └─ AI 摘要 │
├─────────────────────────────────────────┤
│ Output Layer (输出层) │
│ ├─ Markdown 报告 │
│ ├─ JSON 数据 │
│ └─ 多渠道推送 │
└─────────────────────────────────────────┘
性能指标
| 解析器 | 文章数/次 | 耗时 | 成功率 |
|---|---|---|---|
| 观点地产网 | 30 篇 | 605ms | 100% |
| 煤炭资源网 | 30 篇 | 455ms | 100% |
| 房天下 | 17 篇 | 579ms | 100% |
| MIT Tech Review | 9 篇 | 393ms | 100% |
| 总计 | 86 篇/次 | ~2s | 100% |
配置说明
采集源配置
在 config/ 目录下管理采集源:
coal-sources.json- 煤炭行业采集源realestate-sources.json- 房地产行业采集源ai-sources.json- AI 技术采集源
解析器开发
自定义解析器参考 src/parsers/ 目录:
export function parseGuandian(html: string, baseUrl: string): Article[] {
// 解析逻辑
}
开发计划
已实现 ✅
- 4 层架构设计
- 6 个解析器(4 个生产就绪)
- 207 个采集源配置
- CLI 工具
- Node.js API
进行中 🔄
- 浏览器控制(Playwright)
- AI 验证挑战自动解决
- 缓存机制
计划中 ⏳
- 更多行业支持
- 分布式抓取
- 实时监控告警
贡献指南
欢迎提交 Issue 和 PR!
- Fork 项目
- 创建特性分支
- 提交改动
- 推送到分支
- 创建 Pull Request
许可证
MIT License
联系方式
- GitHub: [你的 GitHub]
- Moltbook: ClawdOpenClaw20260223
- Email: [你的邮箱]
Common-Fetcher - 为 AI Agent 提供强大的信息采集能力 🕸️
Usage Guidance
This skill is coherent with its stated purpose but lacks provenance and includes an install step that pulls a third‑party npm package. Before installing: (1) verify the npm package source — check its npm page and GitHub repo; (2) inspect the package contents (look for postinstall scripts, network calls, or unexpected binaries) or request the source code from the author; (3) test the package in a sandboxed environment first; (4) do not enable scheduled runs or configure automatic pushes until you confirm where outputs are sent and which credentials are required; (5) if you need to supply API keys for push channels, provide only least-privilege tokens and rotate them after testing.
Capability Analysis
Type: OpenClaw Skill
Name: common-fetcher
Version: 1.0.0
The OpenClaw skill 'common-fetcher' is described as a unified data collection framework supporting RSS, web scraping, and API integration, with AI processing capabilities. The `SKILL.md` outlines standard installation via npm, requires `node` and `npm` binaries, and describes features like network access for data fetching and file system access for output. There are no indicators of intentional malicious behavior such as credential theft, unauthorized data exfiltration, persistence mechanisms, obfuscation, or prompt injection attempts against the agent. The described functionalities are consistent with a legitimate data collection tool.
Capability Assessment
Purpose & Capability
Name/description (采集/抓取/AI 处理) match the declared requirements (node/npm) and the install spec (npm package common-fetcher). No unrelated binaries or credentials are requested.
Instruction Scope
SKILL.md stays on-topic (CLI usage, Node API, config/ directory, openclaw.json integration). It references 'multi-channel push' and scheduling but does not specify where outputs are pushed or what credentials are needed; instructions are somewhat vague about external endpoints and operational details.
Install Mechanism
Install uses a public npm package name 'common-fetcher' (moderate risk). The skill bundle contains no code or homepage, so the package provenance is unknown. npm packages can include postinstall scripts and arbitrary code; installing without verifying source is a supply-chain risk.
Credentials
No environment variables or credentials are declared, which aligns with the minimal metadata. However, the described features (multi-channel push, integration with external APIs) normally require tokens/keys — the absence of declared env vars suggests incomplete metadata and means the skill may prompt for or expect credentials later without clear guidance.
Persistence & Privilege
always is false and no special system config paths are requested. The README suggests enabling/scheduling the skill via openclaw.json, which is normal. Autonomous invocation is allowed by default and not a concern by itself.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install common-fetcher - After installation, invoke the skill by name or use
/common-fetcher - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release
- 207+ pre-configured sources (coal, realestate, AI)
- 4 parsers validated (100% success rate)
- <600ms performance for 30 articles
- AI scoring, classification, and summarization
- CLI and Node.js API support
Metadata
Frequently Asked Questions
What is Common-Fetcher?
统一采集框架 - 支持 RSS/Web/API,207+ 采集源,AI 评分/分类/摘要. It is an AI Agent Skill for Claude Code / OpenClaw, with 555 downloads so far.
How do I install Common-Fetcher?
Run "/install common-fetcher" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Common-Fetcher free?
Yes, Common-Fetcher is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Common-Fetcher support?
Common-Fetcher is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Common-Fetcher?
It is built and maintained by luck (@lq707904686); the current version is v1.0.0.
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