news-aggregator-skill

Original source / Raw SKILL.md

---
name: news-aggregator-skill
description: "Comprehensive news aggregator that fetches, filters, and deeply analyzes real-time content from 8 major sources: Hacker News, GitHub Trending, Product Hunt, 36Kr, Tencent News, WallStreetCN, V2EX, and Weibo. Best for 'daily scans', 'tech news briefings', 'finance updates', and 'deep interpretations' of hot topics."
---

# News Aggregator Skill

Fetch real-time hot news from multiple sources.

## Tools

### fetch_news.py

**Usage:**

```bash
### Single Source (Limit 10)
```bash
### Global Scan (Option 12) - **Broad Fetch Strategy**
> **NOTE**: This strategy is specifically for the "Global Scan" scenario where we want to catch all trends.

```bash
#  1. Fetch broadly (Massive pool for Semantic Filtering)
python3 scripts/fetch_news.py --source all --limit 15 --deep

# 2. SEMANTIC FILTERING:
# Agent manually filters the broad list (approx 120 items) for user's topics.
```

### Single Source & Combinations (Smart Keyword Expansion)
**CRITICAL**: You MUST automatically expand the user's simple keywords to cover the entire domain field.
*   User: "AI" -> Agent uses: `--keyword "AI,LLM,GPT,Claude,Generative,Machine Learning,RAG,Agent"`
*   User: "Android" -> Agent uses: `--keyword "Android,Kotlin,Google,Mobile,App"`
*   User: "Finance" -> Agent uses: `--keyword "Finance,Stock,Market,Economy,Crypto,Gold"`

```bash
# Example: User asked for "AI news from HN" (Note the expanded keywords)
python3 scripts/fetch_news.py --source hackernews --limit 20 --keyword "AI,LLM,GPT,DeepSeek,Agent" --deep
```

### Specific Keyword Search
Only use `--keyword` for very specific, unique terms (e.g., "DeepSeek", "OpenAI").
```bash
python3 scripts/fetch_news.py --source all --limit 10 --keyword "DeepSeek" --deep
```

**Arguments:**

- `--source`: One of `hackernews`, `weibo`, `github`, `36kr`, `producthunt`, `v2ex`, `tencent`, `wallstreetcn`, `all`.
- `--limit`: Max items per source (default 10).
- `--keyword`: Comma-separated filters (e.g. "AI,GPT").
- `--deep`: **[NEW]** Enable deep fetching. Downloads and extracts the main text content of the articles.

**Output:**
JSON array. If `--deep` is used, items will contain a `content` field associated with the article text.

## Interactive Menu

When the user says **"news-aggregator-skill 如意如意"** (or similar "menu/help" triggers):
1.  **READ** the content of `templates.md` in the skill directory.
2.  **DISPLAY** the list of available commands to the user exactly as they appear in the file.
3.  **GUIDE** the user to select a number or copy the command to execute.

### Smart Time Filtering & Reporting (CRITICAL)
If the user requests a specific time window (e.g., "past X hours") and the results are sparse (< 5 items):
1.  **Prioritize User Window**: First, list all items that strictly fall within the user's requested time (Time < X).
2.  **Smart Fill**: If the list is short, you MUST include high-value/high-heat items from a wider range (e.g. past 24h) to ensure the report provides at least 5 meaningful insights.
2.  **Annotation**: Clearly mark these older items (e.g., "⚠️ 18h ago", "🔥 24h Hot") so the user knows they are supplementary.
3.  **High Value**: Always prioritize "SOTA", "Major Release", or "High Heat" items even if they slightly exceed the time window.
4.  **GitHub Trending Exception**: For purely list-based sources like **GitHub Trending**, strictly return the valid items from the fetched list (e.g. Top 10). **List ALL fetched items**. Do **NOT** perform "Smart Fill".
    *   **Deep Analysis (Required)**: For EACH item, you **MUST** leverage your AI capabilities to analyze:
        *   **Core Value (核心价值)**: What specific problem does it solve? Why is it trending?
        *   **Inspiration (启发思考)**: What technical or product insights can be drawn?
        *   **Scenarios (场景标签)**: 3-5 keywords (e.g. `#RAG #LocalFirst #Rust`).

### 6. Response Guidelines (CRITICAL)

**Format & Style:**
- **Language**: Simplified Chinese (简体中文).
- **Style**: Magazine/Newsletter style (e.g., "The Economist" or "Morning Brew" vibe). Professional, concise, yet engaging.
- **Structure**:
    - **Global Headlines**: Top 3-5 most critical stories across all domains.
    - **Tech & AI**: Specific section for AI, LLM, and Tech items.
    - **Finance / Social**: Other strong categories if relevant.
- **Item Format**:
    - **Title**: **MUST be a Markdown Link** to the original URL.
        - ✅ Correct: `### 1. [OpenAI Releases GPT-5](https://...)`
        - ❌ Incorrect: `### 1. OpenAI Releases GPT-5`
    - **Metadata Line**: Must include Source, **Time/Date**, and Heat/Score.
    - **1-Liner Summary**: A punchy, "so what?" summary.
    - **Deep Interpretation (Bulleted)**: 2-3 bullet points explaining *why* this matters, technical details, or context. (Required for "Deep Scan").

**Output Artifact:**
- Always save the full report to `reports/` directory with a timestamped filename (e.g., `reports/hn_news_YYYYMMDD_HHMM.md`).
- Present the full report content to the user in the chat.


---

## Referenced Files

> The following files are referenced in this skill and included for context.

### scripts/fetch_news.py

```python
import argparse
import json
import requests
from bs4 import BeautifulSoup
import sys
import time
import re
import concurrent.futures
from datetime import datetime

# Headers for scraping to avoid basic bot detection
HEADERS = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
}

def filter_items(items, keyword=None):
    if not keyword:
        return items
    keywords = [k.strip() for k in keyword.split(',') if k.strip()]
    pattern = '|'.join([r'\b' + re.escape(k) + r'\b' for k in keywords])
    regex = r'(?i)(' + pattern + r')'
    return [item for item in items if re.search(regex, item['title'])]

def fetch_url_content(url):
    """
    Fetches the content of a URL and extracts text from paragraphs.
    Truncates to 3000 characters.
    """
    if not url or not url.startswith('http'):
        return ""
    try:
        response = requests.get(url, headers=HEADERS, timeout=5)
        response.raise_for_status()
        soup = BeautifulSoup(response.content, 'html.parser')
         # Remove script and style elements
        for script in soup(["script", "style", "nav", "footer", "header"]):
            script.extract()
        # Get text
        text = soup.get_text(separator=' ', strip=True)
        # Simple cleanup
        lines = (line.strip() for line in text.splitlines())
        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
        text = ' '.join(chunk for chunk in chunks if chunk)
        return text[:3000]
    except Exception:
        return ""

def enrich_items_with_content(items, max_workers=10):
    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        future_to_item = {executor.submit(fetch_url_content, item['url']): item for item in items}
        for future in concurrent.futures.as_completed(future_to_item):
            item = future_to_item[future]
            try:
                content = future.result()
                if content:
                    item['content'] = content
            except Exception:
                item['content'] = ""
    return items

# --- Source Fetchers ---

def fetch_hackernews(limit=5, keyword=None):
    base_url = "https://news.ycombinator.com"
    news_items = []
    page = 1
    max_pages = 5
    
    while len(news_items) < limit and page <= max_pages:
        url = f"{base_url}/news?p={page}"
        try:
            response = requests.get(url, headers=HEADERS, timeout=10)
            if response.status_code != 200: break
        except: break

        soup = BeautifulSoup(response.text, 'html.parser')
        rows = soup.select('.athing')
        if not rows: break
        
        page_items = []
        for row in rows:
            try:
                id_ = row.get('id')
                title_line = row.select_one('.titleline a')
                if not title_line: continue
                title = title_line.get_text()
                link = title_line.get('href')
                
                # Metadata
                score_span = soup.select_one(f'#score_{id_}')
                score = score_span.get_text() if score_span else "0 points"
                
                # Age/Time
                age_span = soup.select_one(f'.age a[href="item?id={id_}"]')
                time_str = age_span.get_text() if age_span else ""
                
                if link and link.startswith('item?id='): link = f"{base_url}/{link}"
                
                page_items.append({
                    "source": "Hacker News", 
                    "title": title, 
                    "url": link, 
                    "heat": score,
                    "time": time_str
                })
            except: continue
        
        news_items.extend(filter_items(page_items, keyword))
        if len(news_items) >= limit: break
        page += 1
        time.sleep(0.5)

    return news_items[:limit]

def fetch_weibo(limit=5, keyword=None):
    # Use the PC Ajax API which returns JSON directly and is less rate-limited than scraping s.weibo.com
    url = "https://weibo.com/ajax/side/hotSearch"
    headers = {
        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
        "Referer": "https://weibo.com/"
    }
    
    try:
        response = requests.get(url, headers=headers, timeout=10)
        data = response.json()
        items = data.get('data', {}).get('realtime', [])
        
        all_items = []
        for item in items:
            # key 'note' is usually the title, sometimes 'word'
            title = item.get('note', '') or item.get('word', '')
            if not title: continue
            
            # 'num' is the heat value
            heat = item.get('num', 0)
            
            # Construct URL (usually search query)
            # Web UI uses: https://s.weibo.com/weibo?q=%23TITLE%23&Refer=top
            full_url = f"https://s.weibo.com/weibo?q={requests.utils.quote(title)}&Refer=top"
            
            all_items.append({
                "source": "Weibo Hot Search", 
                "title": title, 
                "url": full_url, 
                "heat": f"{heat}",
                "time": "Real-time"
            })
            
        return filter_items(all_items, keyword)[:limit]
    except Exception: 
        return []

def fetch_github(limit=5, keyword=None):
    try:
        response = requests.get("https://github.com/trending", headers=HEADERS, timeout=10)
    except: return []
    
    soup = BeautifulSoup(response.text, 'html.parser')
    items = []
    for article in soup.select('article.Box-row'):
        try:
            h2 = article.select_one('h2 a')
            if not h2: continue
            title = h2.get_text(strip=True).replace('\n', '').replace(' ', '')
            link = "https://github.com" + h2['href']
            
            desc = article.select_one('p')
            desc_text = desc.get_text(strip=True) if desc else ""
            
            # Stars (Heat)
            # usually the first 'Link--muted' with a SVG star
            stars_tag = article.select_one('a[href$="/stargazers"]')
            stars = stars_tag.get_text(strip=True) if stars_tag else ""
            
            items.append({
                "source": "GitHub Trending", 
                "title": f"{title} - {desc_text}", 
                "url": link,
                "heat": f"{stars} stars",
                "time": "Today"
            })
        except: continue
    return filter_items(items, keyword)[:limit]

def fetch_36kr(limit=5, keyword=None):
    try:
        response = requests.get("https://36kr.com/newsflashes", headers=HEADERS, timeout=10)
        soup = BeautifulSoup(response.text, 'html.parser')
        items = []
        for item in soup.select('.newsflash-item'):
            title = item.select_one('.item-title').get_text(strip=True)
            href = item.select_one('.item-title')['href']
            time_tag = item.select_one('.time')
            time_str = time_tag.get_text(strip=True) if time_tag else ""
            
            items.append({
                "source": "36Kr", 
                "title": title, 
                "url": f"https://36kr.com{href}" if not href.startswith('http') else href,
                "time": time_str,
                "heat": ""
            })
        return filter_items(items, keyword)[:limit]
    except: return []

def fetch_v2ex(limit=5, keyword=None):
    try:
        # Hot topics json
        data = requests.get("https://www.v2ex.com/api/topics/hot.json", headers=HEADERS, timeout=10).json()
        items = []
        for t in data:
            # V2EX API fields: created, replies (heat)
            replies = t.get('replies', 0)
            created = t.get('created', 0)
            # convert epoch to readable if possible, simpler to just leave as is or basic format
            # Let's keep it simple
            items.append({
                "source": "V2EX", 
                "title": t['title'], 
                "url": t['url'],
                "heat": f"{replies} replies",
                "time": "Hot"
            })
        return filter_items(items, keyword)[:limit]
    except: return []

def fetch_tencent(limit=5, keyword=None):
    try:
        url = "https://i.news.qq.com/web_backend/v2/getTagInfo?tagId=aEWqxLtdgmQ%3D"
        data = requests.get(url, headers={"Referer": "https://news.qq.com/"}, timeout=10).json()
        items = []
        for news in data['data']['tabs'][0]['articleList']:
            items.append({
                "source": "Tencent News", 
                "title": news['title'], 
                "url": news.get('url') or news.get('link_info', {}).get('url'),
                "time": news.get('pub_time', '') or news.get('publish_time', '')
            })
        return filter_items(items, keyword)[:limit]
    except: return []

def fetch_wallstreetcn(limit=5, keyword=None):
    try:
        url = "https://api-one.wallstcn.com/apiv1/content/information-flow?channel=global-channel&accept=article&limit=30"
        data = requests.get(url, timeout=10).json()
        items = []
        for item in data['data']['items']:
            res = item.get('resource')
            if res and (res.get('title') or res.get('content_short')):
                 ts = res.get('display_time', 0)
                 time_str = datetime.fromtimestamp(ts).strftime('%H:%M') if ts else ""
                 items.append({
                     "source": "Wall Street CN", 
                     "title": res.get('title') or res.get('content_short'), 
                     "url": res.get('uri'),
                     "time": time_str
                 })
        return filter_items(items, keyword)[:limit]
    except: return []

def fetch_producthunt(limit=5, keyword=None):
    try:
        # Using RSS for speed and reliability without API key
        response = requests.get("https://www.producthunt.com/feed", headers=HEADERS, timeout=10)
        soup = BeautifulSoup(response.text, 'xml')
        if not soup.find('item'): soup = BeautifulSoup(response.text, 'html.parser')
        
        items = []
        for entry in soup.find_all(['item', 'entry']):
            title = entry.find('title').get_text(strip=True)
            link_tag = entry.find('link')
            url = link_tag.get('href') or link_tag.get_text(strip=True) if link_tag else ""
            
            pubBox = entry.find('pubDate') or entry.find('published')
            pub = pubBox.get_text(strip=True) if pubBox else ""
            
            items.append({
                "source": "Product Hunt", 
                "title": title, 
                "url": url,
                "time": pub,
                "heat": "Top Product" # RSS implies top rank
            })
        return filter_items(items, keyword)[:limit]
    except: return []

def main():
    parser = argparse.ArgumentParser()
    sources_map = {
        'hackernews': fetch_hackernews, 'weibo': fetch_weibo, 'github': fetch_github,
        '36kr': fetch_36kr, 'v2ex': fetch_v2ex, 'tencent': fetch_tencent,
        'wallstreetcn': fetch_wallstreetcn, 'producthunt': fetch_producthunt
    }
    
    parser.add_argument('--source', default='all', help='Source(s) to fetch from (comma-separated)')
    parser.add_argument('--limit', type=int, default=10, help='Limit per source. Default 10')
    parser.add_argument('--keyword', help='Comma-sep keyword filter')
    parser.add_argument('--deep', action='store_true', help='Download article content for detailed summarization')
    
    args = parser.parse_args()
    
    to_run = []
    if args.source == 'all':
        to_run = list(sources_map.values())
    else:
        requested_sources = [s.strip() for s in args.source.split(',')]
        for s in requested_sources:
            if s in sources_map: to_run.append(sources_map[s])
            
    results = []
    for func in to_run:
        try:
            results.extend(func(args.limit, args.keyword))
        except: pass
        
    if args.deep and results:
        sys.stderr.write(f"Deep fetching content for {len(results)} items...\n")
        results = enrich_items_with_content(results)
        
    print(json.dumps(results, indent=2, ensure_ascii=False))

if __name__ == "__main__":
    main()

```



---

## Skill Companion Files

> Additional files collected from the skill directory layout.

### README.md

```markdown
# News Aggregator Skill

全网科技/金融新闻聚合助手，支持AI 智能解读。

## ✨ 功能特性

- **多源聚合**：一站式覆盖硅谷科技、中国创投、开源社区及金融市场。
- **深度阅读**：支持 Deep Fetch 模式，自动获取正文并进行 AI 深度分析。
- **智能周报**：自动生成杂志级排版的中文日报/周报。
- **交互菜单**：可通过"news-aggregator-skill 如意如意"唤醒交互式菜单，指哪打哪。

## 📚 聚合信源

覆盖全球 8 大主流高价值信息渠道：

- **全球科技**：Hacker News, Product Hunt
- **开源社区**：GitHub Trending, V2EX
- **中国创投**：36Kr, 腾讯新闻科技频道
- **社会/金融**：微博热搜, 华尔街见闻

## 📥 安装指南

### 第一步：安装到 Code Agent

选择以下任一方式将 Skill 添加到您的 Agent：

#### 方法 A：使用 Openskills CLI (推荐)

会自动处理路径依赖和配置同步。

```bash
# 克隆仓库
git clone [email protected]:cclank/news-aggregator-skill.git

# 安装 skill
openskills install ./news-aggregator-skill

# 同步配置到 Agent
openskills sync
```

#### 方法 B：使用 NPX (推荐 2)

直接从远程仓库添加。

```bash
npx skills add https://github.com/cclank/news-aggregator-skill
```

#### 方法 C：Claude 标准安装 (手动)

手动将 Skill 集成到 Claude 项目的标准方式。

```bash
# 1. 克隆仓库
git clone [email protected]:cclank/news-aggregator-skill.git

# 2. 定位或创建项目的 skills 目录
mkdir -p YourProject/.claude/skills

# 3. 将整个文件夹复制过去
cp -r news-aggregator-skill YourProject/.claude/skills/

# 4. 验证：确保 SKILL.md 存在于目标目录
ls YourProject/.claude/skills/news-aggregator-skill/SKILL.md
```

### 第二步：安装 Python 依赖（如果你的agent足够聪明，可以跳过）

进入已安装的 Skill 目录，执行依赖安装：

```bash
# 进入 Skill 安装目录 (根据您选择的安装方式调整路径)
cd ~/.claude/skills/news-aggregator-skill  # 或 YourProject/.claude/skills/news-aggregator-skill

# 安装依赖
pip install -r requirements.txt
```

## 🚀 如何使用

### 1. 🔮 唤醒交互菜单 (推荐)

最简单的使用方式，来自岚叔的彩蛋--直接召唤智能菜单：

> **"news-aggregator-skill 如意如意"**

系统将为您展示功能列表（如：早安日报、硅谷热点、全网扫描等），回复数字即可执行。

### 2. 🗣️ 自然语言触发

您也可以直接说出您的需求：

- **看热点**："帮我看看 Hacker News 和 Product Hunt 最近有什么 AI 新闻？"
- **看国内**："36氪和腾讯新闻今天有什么科技大瓜？"
- **看开源**："GitHub 上最近火的项目是啥？"
- **全网扫**："全网扫描一下关于 Agent 和 LLM 的最新进展。"

> ⚠️ **全网扫描注意**: Global Scan 是基于各平台"热榜"进行关键词过滤，而非全文检索。如果关键词（如 Agent）在当天的全网热榜中未出现，可能返回较少结果。

## 📊 支持源列表

| Source Name | ID | Category |
|-------------|----|----------|
| **Hacker News** | `hackernews` | Global Tech |
| **GitHub Trending** | `github` | Open Source |
| **Product Hunt** | `producthunt` | New Products |
| **36Kr** | `36kr` | China VC |
| **Tencent News** | `tencent` | General Tech |
| **Weibo** | `weibo` | Social Trends |
| **WallStreetCN** | `wallstreetcn` | Finance |
| **V2EX** | `v2ex` | Dev Community |


```

### _meta.json

```json
{
  "owner": "administratorfung",
  "slug": "news-aggregator-skill-3",
  "displayName": "News Aggregator Skill",
  "latest": {
    "version": "0.1.0",
    "publishedAt": 1769680924048,
    "commit": "https://github.com/clawdbot/skills/commit/3fc0e1f189327f3a62a2a3873ba8275931cf2a14"
  },
  "history": []
}

```