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README.md
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README.md
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# News Aggregator Skill
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全网科技/金融新闻聚合助手，支持AI 智能解读。
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## ✨ 功能特性
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- **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。
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- **深度阅读**：支持 Deep Fetch 模式，自动获取正文并进行 AI 深度分析。
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- **智能周报**：自动生成杂志级排版的中文日报/周报。
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- **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。
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## 📚 聚合信源
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覆盖全球 8 大主流高价值信息渠道：
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- **全球科技**：Hacker News, Product Hunt
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- **开源社区**：GitHub Trending, V2EX
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- **中国创投**：36Kr, 腾讯新闻科技频道
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- **社会/金融**：微博热搜, 华尔街见闻
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## 📥 安装指南
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### 第一步：安装到 Code Agent
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选择以下任一方式将 Skill 添加到您的 Agent：
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#### 方法 A：使用 Openskills CLI (推荐)
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会自动处理路径依赖和配置同步。
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```bash
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# 克隆仓库
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git clone git@github.com:cclank/news-aggregator-skill.git
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# 安装 skill
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openskills install ./news-aggregator-skill
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# 同步配置到 Agent
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openskills sync
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```
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#### 方法 B：使用 NPX (推荐 2)
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直接从远程仓库添加。
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```bash
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npx skills add https://github.com/cclank/news-aggregator-skill
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```
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#### 方法 C：Claude 标准安装 (手动)
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手动将 Skill 集成到 Claude 项目的标准方式。
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```bash
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# 1. 克隆仓库
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git clone git@github.com:cclank/news-aggregator-skill.git
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# 2. 定位或创建项目的 skills 目录
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mkdir -p YourProject/.claude/skills
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# 3. 将整个文件夹复制过去
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cp -r news-aggregator-skill YourProject/.claude/skills/
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# 4. 验证：确保 SKILL.md 存在于目标目录
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ls YourProject/.claude/skills/news-aggregator-skill/SKILL.md
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```
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### 第二步：安装 Python 依赖（如果你的agent足够聪明，可以跳过）
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进入已安装的 Skill 目录，执行依赖安装：
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```bash
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# 进入 Skill 安装目录 (根据您选择的安装方式调整路径)
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cd ~/.claude/skills/news-aggregator-skill # 或 YourProject/.claude/skills/news-aggregator-skill
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# 安装依赖
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pip install -r requirements.txt
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```
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## 🚀 如何使用
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### 1. 🔮 唤醒交互菜单 (推荐)
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最简单的使用方式，来自岚叔的彩蛋--直接召唤智能菜单：
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> **"news-aggregator-skill 如意如意"**
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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。
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### 2. 🗣️ 自然语言触发
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您也可以直接说出您的需求：
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- **看热点**："帮我看看 Hacker News 和 Product Hunt 最近有什么 AI 新闻？"
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- **看国内**："36氪和腾讯新闻今天有什么科技大瓜？"
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- **看开源**："GitHub 上最近火的项目是啥？"
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- **全网扫**："全网扫描一下关于 Agent 和 LLM 的最新进展。"
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> ⚠️ **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。
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## 📊 支持源列表
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| Source Name | ID | Category |
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|-------------|----|----------|
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| **Hacker News** | `hackernews` | Global Tech |
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| **GitHub Trending** | `github` | Open Source |
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| **Product Hunt** | `producthunt` | New Products |
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| **36Kr** | `36kr` | China VC |
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| **Tencent News** | `tencent` | General Tech |
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| **Weibo** | `weibo` | Social Trends |
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| **WallStreetCN** | `wallstreetcn` | Finance |
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| **V2EX** | `v2ex` | Dev Community |
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95
SKILL.md
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SKILL.md
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---
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name: news-aggregator-skill
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description: "综合新闻聚合器，从8个主要来源（Hacker News、GitHub Trending、Product Hunt、36Kr、腾讯新闻、华尔街见闻、V2EX和微博）获取、过滤和深度分析实时内容。最适合"每日扫描"、"科技新闻简报"、"财经更新"和热点话题的"深度解读"。"
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---
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# News Aggregator Skill
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Fetch real-time hot news from multiple sources.
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## Tools
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### fetch_news.py
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**Usage:**
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```bash
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### Single Source (Limit 10)
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```bash
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### Global Scan (Option 12) - **Broad Fetch Strategy**
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> **NOTE**: This strategy is specifically for the "Global Scan" scenario where we want to catch all trends.
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```bash
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# 1. Fetch broadly (Massive pool for Semantic Filtering)
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python3 scripts/fetch_news.py --source all --limit 15 --deep
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# 2. SEMANTIC FILTERING:
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# Agent manually filters the broad list (approx 120 items) for user's topics.
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```
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### Single Source & Combinations (Smart Keyword Expansion)
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**CRITICAL**: You MUST automatically expand the user's simple keywords to cover the entire domain field.
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* User: "AI" -> Agent uses: `--keyword "AI,LLM,GPT,Claude,Generative,Machine Learning,RAG,Agent"`
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* User: "Android" -> Agent uses: `--keyword "Android,Kotlin,Google,Mobile,App"`
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* User: "Finance" -> Agent uses: `--keyword "Finance,Stock,Market,Economy,Crypto,Gold"`
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```bash
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# Example: User asked for "AI news from HN" (Note the expanded keywords)
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python3 scripts/fetch_news.py --source hackernews --limit 20 --keyword "AI,LLM,GPT,DeepSeek,Agent" --deep
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```
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### Specific Keyword Search
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Only use `--keyword` for very specific, unique terms (e.g., "DeepSeek", "OpenAI").
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```bash
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python3 scripts/fetch_news.py --source all --limit 10 --keyword "DeepSeek" --deep
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```
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**Arguments:**
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- `--source`: One of `hackernews`, `weibo`, `github`, `36kr`, `producthunt`, `v2ex`, `tencent`, `wallstreetcn`, `all`.
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- `--limit`: Max items per source (default 10).
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- `--keyword`: Comma-separated filters (e.g. "AI,GPT").
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- `--deep`: **[NEW]** Enable deep fetching. Downloads and extracts the main text content of the articles.
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**Output:**
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JSON array. If `--deep` is used, items will contain a `content` field associated with the article text.
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## Interactive Menu
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When the user says **"news-aggregator-skill 如意如意"** (or similar "menu/help" triggers):
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1. **READ** the content of `templates.md` in the skill directory.
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2. **DISPLAY** the list of available commands to the user exactly as they appear in the file.
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3. **GUIDE** the user to select a number or copy the command to execute.
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### Smart Time Filtering & Reporting (CRITICAL)
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If the user requests a specific time window (e.g., "past X hours") and the results are sparse (< 5 items):
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1. **Prioritize User Window**: First, list all items that strictly fall within the user's requested time (Time < X).
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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.
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2. **Annotation**: Clearly mark these older items (e.g., "⚠️ 18h ago", "🔥 24h Hot") so the user knows they are supplementary.
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3. **High Value**: Always prioritize "SOTA", "Major Release", or "High Heat" items even if they slightly exceed the time window.
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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".
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* **Deep Analysis (Required)**: For EACH item, you **MUST** leverage your AI capabilities to analyze:
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* **Core Value (核心价值)**: What specific problem does it solve? Why is it trending?
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* **Inspiration (启发思考)**: What technical or product insights can be drawn?
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* **Scenarios (场景标签)**: 3-5 keywords (e.g. `#RAG #LocalFirst #Rust`).
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### 6. Response Guidelines (CRITICAL)
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**Format & Style:**
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- **Language**: Simplified Chinese (简体中文).
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- **Style**: Magazine/Newsletter style (e.g., "The Economist" or "Morning Brew" vibe). Professional, concise, yet engaging.
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- **Structure**:
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- **Global Headlines**: Top 3-5 most critical stories across all domains.
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- **Tech & AI**: Specific section for AI, LLM, and Tech items.
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- **Finance / Social**: Other strong categories if relevant.
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- **Item Format**:
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- **Title**: **MUST be a Markdown Link** to the original URL.
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- ✅ Correct: `### 1. [OpenAI Releases GPT-5](https://...)`
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- ❌ Incorrect: `### 1. OpenAI Releases GPT-5`
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- **Metadata Line**: Must include Source, **Time/Date**, and Heat/Score.
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- **1-Liner Summary**: A punchy, "so what?" summary.
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- **Deep Interpretation (Bulleted)**: 2-3 bullet points explaining *why* this matters, technical details, or context. (Required for "Deep Scan").
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**Output Artifact:**
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- Always save the full report to `reports/` directory with a timestamped filename (e.g., `reports/hn_news_YYYYMMDD_HHMM.md`).
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- Present the full report content to the user in the chat.
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6
_meta.json
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_meta.json
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{
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"ownerId": "kn70rxpkwjvb2873k5x3cwyfzn7zys6f",
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"slug": "news-aggregator-skill",
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"version": "0.1.0",
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"publishedAt": 1769420683107
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}
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2
requirements.txt
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requirements.txt
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requests
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beautifulsoup4
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323
scripts/fetch_news.py
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scripts/fetch_news.py
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import argparse
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import json
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import requests
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from bs4 import BeautifulSoup
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import sys
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import time
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import re
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import concurrent.futures
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from datetime import datetime
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# Headers for scraping to avoid basic bot detection
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HEADERS = {
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"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"
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}
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def filter_items(items, keyword=None):
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if not keyword:
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return items
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keywords = [k.strip() for k in keyword.split(',') if k.strip()]
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pattern = '|'.join([r'\b' + re.escape(k) + r'\b' for k in keywords])
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regex = r'(?i)(' + pattern + r')'
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return [item for item in items if re.search(regex, item['title'])]
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def fetch_url_content(url):
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"""
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Fetches the content of a URL and extracts text from paragraphs.
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Truncates to 3000 characters.
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"""
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if not url or not url.startswith('http'):
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return ""
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try:
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response = requests.get(url, headers=HEADERS, timeout=5)
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response.raise_for_status()
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soup = BeautifulSoup(response.content, 'html.parser')
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# Remove script and style elements
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for script in soup(["script", "style", "nav", "footer", "header"]):
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script.extract()
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# Get text
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text = soup.get_text(separator=' ', strip=True)
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# Simple cleanup
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lines = (line.strip() for line in text.splitlines())
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chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
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text = ' '.join(chunk for chunk in chunks if chunk)
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return text[:3000]
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except Exception:
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return ""
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def enrich_items_with_content(items, max_workers=10):
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
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future_to_item = {executor.submit(fetch_url_content, item['url']): item for item in items}
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for future in concurrent.futures.as_completed(future_to_item):
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item = future_to_item[future]
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try:
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content = future.result()
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if content:
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item['content'] = content
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except Exception:
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item['content'] = ""
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return items
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# --- Source Fetchers ---
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def fetch_hackernews(limit=5, keyword=None):
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base_url = "https://news.ycombinator.com"
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news_items = []
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page = 1
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max_pages = 5
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while len(news_items) < limit and page <= max_pages:
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url = f"{base_url}/news?p={page}"
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try:
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response = requests.get(url, headers=HEADERS, timeout=10)
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if response.status_code != 200: break
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except: break
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soup = BeautifulSoup(response.text, 'html.parser')
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rows = soup.select('.athing')
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if not rows: break
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page_items = []
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for row in rows:
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try:
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id_ = row.get('id')
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title_line = row.select_one('.titleline a')
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if not title_line: continue
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title = title_line.get_text()
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link = title_line.get('href')
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# Metadata
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score_span = soup.select_one(f'#score_{id_}')
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score = score_span.get_text() if score_span else "0 points"
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# Age/Time
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age_span = soup.select_one(f'.age a[href="item?id={id_}"]')
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time_str = age_span.get_text() if age_span else ""
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if link and link.startswith('item?id='): link = f"{base_url}/{link}"
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page_items.append({
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"source": "Hacker News",
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"title": title,
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"url": link,
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"heat": score,
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"time": time_str
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})
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except: continue
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news_items.extend(filter_items(page_items, keyword))
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if len(news_items) >= limit: break
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page += 1
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time.sleep(0.5)
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return news_items[:limit]
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def fetch_weibo(limit=5, keyword=None):
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# Use the PC Ajax API which returns JSON directly and is less rate-limited than scraping s.weibo.com
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url = "https://weibo.com/ajax/side/hotSearch"
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headers = {
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"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",
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"Referer": "https://weibo.com/"
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}
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try:
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response = requests.get(url, headers=headers, timeout=10)
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data = response.json()
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items = data.get('data', {}).get('realtime', [])
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all_items = []
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for item in items:
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# key 'note' is usually the title, sometimes 'word'
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title = item.get('note', '') or item.get('word', '')
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if not title: continue
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# 'num' is the heat value
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heat = item.get('num', 0)
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# Construct URL (usually search query)
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# Web UI uses: https://s.weibo.com/weibo?q=%23TITLE%23&Refer=top
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full_url = f"https://s.weibo.com/weibo?q={requests.utils.quote(title)}&Refer=top"
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all_items.append({
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"source": "Weibo Hot Search",
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"title": title,
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"url": full_url,
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"heat": f"{heat}",
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"time": "Real-time"
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})
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return filter_items(all_items, keyword)[:limit]
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except Exception:
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return []
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def fetch_github(limit=5, keyword=None):
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try:
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response = requests.get("https://github.com/trending", headers=HEADERS, timeout=10)
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except: return []
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soup = BeautifulSoup(response.text, 'html.parser')
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items = []
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for article in soup.select('article.Box-row'):
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try:
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h2 = article.select_one('h2 a')
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if not h2: continue
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title = h2.get_text(strip=True).replace('\n', '').replace(' ', '')
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link = "https://github.com" + h2['href']
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desc = article.select_one('p')
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desc_text = desc.get_text(strip=True) if desc else ""
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# Stars (Heat)
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# usually the first 'Link--muted' with a SVG star
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stars_tag = article.select_one('a[href$="/stargazers"]')
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stars = stars_tag.get_text(strip=True) if stars_tag else ""
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items.append({
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"source": "GitHub Trending",
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"title": f"{title} - {desc_text}",
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"url": link,
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"heat": f"{stars} stars",
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"time": "Today"
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})
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||||
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()
|
||||
49
templates.md
Normal file
49
templates.md
Normal file
@@ -0,0 +1,49 @@
|
||||
# 🗞️ News Aggregator Skill 指令菜单
|
||||
|
||||
请回复序号（如 "1"）或直接复制指令来执行任务。
|
||||
|
||||
## 🎯 单点直击 (Single Source)
|
||||
|
||||
**1. 🦄 硅谷热点 (Hacker News)**
|
||||
> 使用news-aggregator skill 帮我深度扫描 Hacker News 看看过去 5 小时有哪些 AI/LLM 新动态？
|
||||
|
||||
**2. 🐙 开源趋势 (GitHub Trending)**
|
||||
> 使用news-aggregator skill 看看 GitHub Trending 前 10 个热门开源项目。
|
||||
|
||||
**3. 🚀 创投快讯 (36Kr)**
|
||||
> 使用news-aggregator skill 看看 36氪 前 10 条最新科技快讯。
|
||||
|
||||
**4. 🐧 腾讯科技 (Tencent News)**
|
||||
> 使用news-aggregator skill 腾讯新闻科技频道的前 10 条大新闻是什么？
|
||||
|
||||
**5. 📈 华尔街见闻 (WallstreetCN)**
|
||||
> 使用news-aggregator skill 扫一眼华尔街见闻，关注前 10 条市场动态。
|
||||
|
||||
**6. 🔴 微博吃瓜 (Weibo Hot Search)**
|
||||
> 使用news-aggregator skill 看看微博热搜榜前 10 都在讨论什么。
|
||||
|
||||
**7. 🐱 产品猎人 (Product Hunt)**
|
||||
> 使用news-aggregator skill 扫描 Product Hunt，看看今天前 10 名的新产品有哪些。
|
||||
|
||||
**8. 🤓 极客社区 (V2EX)**
|
||||
> 使用news-aggregator skill 刷一下 V2EX 的 10 个热门话题。
|
||||
|
||||
---
|
||||
|
||||
## 🥊 组合视角 (Combinations)
|
||||
|
||||
**9. ☕️ 早安·全球 AI 速递 (Tech & AI)**
|
||||
> 使用news-aggregator skill 帮我深度扫描 Hacker News 和 Product Hunt，看看过去 24 小时有哪些 **AI 和 LLM** 相关的重磅技术或新产品？
|
||||
|
||||
**10. 🇨🇳 中国科技圈早报 (China Tech)**
|
||||
> 使用news-aggregator skill 看看 36氪 和 腾讯新闻，今天国内科技圈和互联网有什么大新闻？挑 5 个最重要的给我深度总结一下。
|
||||
|
||||
**11. 👨💻 极客与开源 (Dev & Open Source)**
|
||||
> 使用news-aggregator skill GitHub 和 V2EX 上最近现在的热门项目和话题是什么？有没有什么好玩的开源工具？
|
||||
|
||||
---
|
||||
|
||||
## 🌍 上帝视角 (Global Scan)
|
||||
|
||||
**12. 🔥 全网地毯式搜索 (Global Scan)**
|
||||
> 使用news-aggregator skill 帮我全网扫描（所有源）热点新闻
|
||||
Reference in New Issue
Block a user