Back to Details

news-aggregator-skill

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

新闻聚合技能 (News Aggregator Skill)

News Aggregator Skill

从多个来源获取实时热点新闻。
Fetch real-time hot news from multiple sources.

支持的数据源

Supported Data Sources

数据源标识符类型
Hacker News
hackernews
科技/创业
微博热搜
weibo
社会/娱乐
GitHub Trending
github
开源项目
36氪
36kr
科技/商业
Product Hunt
producthunt
产品发布
V2EX
v2ex
技术社区
腾讯新闻
tencent
综合新闻
华尔街见闻
wallstreetcn
财经
Data SourceIdentifierType
Hacker News
hackernews
Technology/Entrepreneurship
Weibo Hot Search
weibo
Society/Entertainment
GitHub Trending
github
Open Source Projects
36Kr
36kr
Technology/Business
Product Hunt
producthunt
Product Launch
V2EX
v2ex
Tech Community
Tencent News
tencent
General News
Wallstreetcn
wallstreetcn
Finance

工具使用

Tool Usage

基本命令

Basic Commands

bash
uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py [参数]
bash
uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py [parameters]

参数说明

Parameter Description

  • --source <源>
    : 指定数据源
    • 单个源：
      hackernews
      , 
      weibo
      , 
      github
      , 
      36kr
      , 
      producthunt
      , 
      v2ex
      , 
      tencent
      , 
      wallstreetcn
    • 多个源（逗号分隔）：
      hackernews,github,producthunt
    • 所有源：
      all
  • --limit <数量>
    : 每个源返回的最大条目数（默认：10）
  • --keyword <关键词>
    : 关键词过滤（逗号分隔）
    • 示例：
      "AI,LLM,GPT"
    • 不区分大小写，支持单词边界匹配
  • --deep
    : 启用深度抓取
    • 下载并提取文章正文内容（截取前 3000 字符）
    • 并发抓取以提高速度
    • 结果中会包含 
      content
      字段
  • --source <source>
    : Specify data source
    • Single source: 
      hackernews
      , 
      weibo
      , 
      github
      , 
      36kr
      , 
      producthunt
      , 
      v2ex
      , 
      tencent
      , 
      wallstreetcn
    • Multiple sources (comma separated): 
      hackernews,github,producthunt
    • All sources: 
      all
  • --limit <count>
    : Maximum number of entries returned per source (default: 10)
  • --keyword <keyword>
    : Keyword filtering (comma separated)
    • Example: 
      "AI,LLM,GPT"
    • Case insensitive, supports word boundary matching
  • --deep
    : Enable deep scraping
    • Download and extract article body content (truncate first 3000 characters)
    • Concurrent scraping to improve speed
    • The result will include the 
      content
      field

输出格式

Output Format

JSON 数组，每个条目包含：
  • source
    : 来源名称
  • title
    : 标题
  • url
    : 链接
  • heat
    : 热度指标（点数、回复数、星标数等）
  • time
    : 时间信息
  • content
    : 文章内容（仅在使用 
    --deep
    时）
JSON array, each entry contains:
  • source
    : Source name
  • title
    : Title
  • url
    : Link
  • heat
    : Heat indicator (points, number of replies, star count, etc.)
  • time
    : Time information
  • content
    : Article content (only available when 
    --deep
    is used)

使用策略

Usage Strategies

1. 全局扫描（广泛获取）

1. Global Scan (Wide Acquisition)

适用场景：每日新闻汇总、全面了解各领域动态
bash
undefined
Applicable scenarios: Daily news summary, comprehensive understanding of dynamics in various fields
bash
undefined

从所有源获取，每源 15 条，启用深度抓取

Fetch from all sources, 15 entries per source, enable deep scraping

uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py --source all --limit 15 --deep

**注意**：全局扫描会返回约 120 条数据，你需要根据用户兴趣进行语义过滤和分类。
uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py --source all --limit 15 --deep

**Note**: Global scan will return about 120 pieces of data, you need to perform semantic filtering and classification according to user interests.

2. 单一数据源

2. Single Data Source

适用场景：专注特定平台或领域
bash
undefined
Applicable scenarios: Focus on specific platforms or fields
bash
undefined

Hacker News 前 10 条

Top 10 entries from Hacker News

uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py --source hackernews --limit 10 --deep
uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py --source hackernews --limit 10 --deep

GitHub Trending 前 15 条

Top 15 entries from GitHub Trending

uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py --source github --limit 15 --deep
undefined
uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py --source github --limit 15 --deep
undefined

3. 关键词搜索（智能扩展）

3. Keyword Search (Intelligent Expansion)

关键规则：自动扩展用户关键词以覆盖整个领域
  • 用户说 "AI" → 使用：
    "AI,LLM,GPT,Claude,DeepSeek,Gemini,机器学习,RAG,Agent,大模型"
  • 用户说 "前端" → 使用：
    "前端,React,Vue,Next.js,TypeScript,JavaScript,CSS,Vite"
  • 用户说 "金融" → 使用：
    "金融,股票,市场,经济,加密货币,比特币,黄金,A股"
bash
undefined
Key Rules: Automatically expand user keywords to cover the entire field
  • User says "AI" → Use: 
    "AI,LLM,GPT,Claude,DeepSeek,Gemini,机器学习,RAG,Agent,大模型"
  • User says "frontend" → Use: 
    "前端,React,Vue,Next.js,TypeScript,JavaScript,CSS,Vite"
  • User says "finance" → Use: 
    "金融,股票,市场,经济,加密货币,比特币,黄金,A股"
bash
undefined

示例：用户问 "有什么 AI 相关的新闻"

Example: User asks "What AI related news are there"

uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py
--source hackernews,github,36kr
--limit 20
--keyword "AI,LLM,GPT,Claude,DeepSeek,Agent,大模型"
--deep
undefined
uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py
--source hackernews,github,36kr
--limit 20
--keyword "AI,LLM,GPT,Claude,DeepSeek,Agent,大模型"
--deep
undefined

4. 精确搜索

4. Precise Search

仅用于非常具体的专有名词
bash
undefined
Only used for very specific proper nouns
bash
undefined

搜索 "DeepSeek" 相关新闻

Search news related to "DeepSeek"

uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py --source all --limit 10 --keyword "DeepSeek" --deep
undefined
uv run --directory .agents/skills/news-aggregator-skill python scripts/fetch_news.py --source all --limit 10 --keyword "DeepSeek" --deep
undefined

输出规范

Output Specifications

报告格式要求

Report Format Requirements

语言与风格：
  • 使用简体中文
  • 采用杂志/新闻通讯风格（如《经济学人》或 Morning Brew）
  • 专业、简洁、引人入胜
报告结构：
  1. 头版头条（3-5 条）
    • 跨领域最重要的新闻
  2. 科技与 AI
    • AI、LLM、技术相关内容的专门板块
  3. 财经/社会
    • 其他重要类别（根据相关性）
单条新闻格式：
markdown
undefined
Language and Style:
  • Use Simplified Chinese
  • Adopt magazine/newsletter style (such as The Economist or Morning Brew)
  • Professional, concise and engaging
Report Structure:
  1. Front Page Headlines (3-5 entries)
    • The most important cross-domain news
  2. Technology & AI
    • Special section for AI, LLM and technology related content
  3. Finance/Society
    • Other important categories (sorted by relevance)
Single News Format:
markdown
undefined

序号. 标题文本

Number. [Title Text](Original URL)

来源：<数据源> | 时间：<时间信息> | 热度：<热度指标>
核心要点：一句话概括"所以呢？"
深度解读：
  • 要点 1：为什么重要
  • 要点 2：技术细节或背景
  • 要点 3：影响和启示

**关键规则**：
- ✅ **标题必须是 Markdown 链接**：`[OpenAI 发布 GPT-5](https://...)`
- ❌ **禁止纯文本标题**：`OpenAI 发布 GPT-5`
- 元数据行必须包含：来源、时间、热度
- 深度扫描时必须提供 2-3 条解读要点
Source: <Data Source> | Time: <Time Information> | Heat: <Heat Indicator>
Key Point: One sentence summary of "so what?"
In-depth Interpretation:
  • Point 1: Why it is important
  • Point 2: Technical details or background
  • Point 3: Impact and implications

**Key Rules**:
- ✅ **Title must be a Markdown link**: `[OpenAI releases GPT-5](https://...)`
- ❌ **Plain text titles are prohibited**: `OpenAI releases GPT-5`
- Metadata line must include: source, time, heat
- 2-3 interpretation points must be provided when deep scanning is enabled

时间过滤与智能补充

Time Filtering and Intelligent Supplement

当用户指定时间窗口（如"过去 X 小时"）且结果稀少（< 5 条）时：
  1. 优先用户窗口：先列出严格符合时间要求的条目
  2. 智能补充：如果列表过短，必须包含更大范围内的高价值/高热度条目（如过去 24 小时）
  3. 明确标注：清楚标记补充条目（如 "⚠️ 18h ago"、"🔥 24h 热门"）
  4. 价值优先：即使略微超出时间窗口，也要优先展示 SOTA、重大发布或高热度内容
GitHub Trending 特例：
  • 严格返回抓取列表中的有效条目（如 Top 10）
  • 列出所有抓取的条目
  • 不进行智能补充
  • 必须对每个项目进行深度分析：
    • 核心价值：解决什么问题？为何流行？
    • 启发思考：技术或产品洞察
    • 场景标签：3-5 个关键词（如 
      #RAG #本地优先 #Rust
      ）
When the user specifies a time window (such as "past X hours") and the results are scarce (< 5 entries):
  1. Prioritize user's window: First list entries that strictly meet the time requirements
  2. Intelligent supplement: If the list is too short, must include high-value/hot entries in a larger range (such as the past 24 hours)
  3. Clear marking: Clearly mark supplementary entries (such as "⚠️ 18h ago", "🔥 24h Hot")
  4. Value first: Even if it slightly exceeds the time window, prioritize displaying SOTA, major releases or high-heat content
GitHub Trending Special Case:
  • Strictly return valid entries in the crawl list (such as Top 10)
  • List all crawled entries
  • Do not perform intelligent supplementation
  • Must conduct in-depth analysis for each project:
    • Core Value: What problem does it solve? Why is it popular?
    • Inspirational Thinking: Technical or product insights
    • Scenario Tags: 3-5 keywords (such as 
      #RAG #Local First #Rust
      )

输出文件

Output File

  • 保存位置：工作区根目录的 
    reports/
    文件夹
  • 文件命名：带时间戳（如 
    hn_news_20260131_1430.md
    ）
  • 完整路径示例：
    /Users/dio/Documents/new_vault/reports/tech_news_20260131_1430.md
  • 用户展示：在聊天中呈现完整报告内容
  • Save Location: 
    reports/
    folder in the workspace root directory
  • File Naming: With timestamp (such as 
    hn_news_20260131_1430.md
    )
  • Full Path Example: 
    /Users/dio/Documents/new_vault/reports/tech_news_20260131_1430.md
  • User Display: Present the full report content in the chat

交互菜单

Interactive Menu

当用户说 "news-aggregator-skill 如意如意"（或类似的"菜单/帮助"触发词）时：
  1. 读取技能目录中的 
    templates.md
    文件
  2. 向用户展示文件中的可用命令列表
  3. 引导用户选择编号或复制命令执行
When the user says "news-aggregator-skill 如意如意" (or similar "menu/help" trigger words):
  1. Read the 
    templates.md
    file in the skill directory
  2. Show the user the list of available commands in the file
  3. Guide the user to select the number or copy the command to execute