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news-aggregator-skill

@mahsumaktas · 收录于 1 周前 · 上游提交 4 个月前

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.

场景每日早报热点追踪行业动态

适合你，如果想一站式获取并理解多平台热门资讯

/ 下载安装
https://github.com/mahsumaktas/agent-evolution-kit/tree/main/skills/news-aggregator-skill ↗
/ 通过 npx 安装 校验哈希
npx oh-my-skill add mahsumaktas/agent-evolution-kit/news-aggregator-skill
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- mahsumaktas/agent-evolution-kit/news-aggregator-skill
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify mahsumaktas/agent-evolution-kit/news-aggregator-skill
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
1GitHub stars
~1.2K上下文体积 · 单文件
索引托管

怎么用

商店整理自技能原文 · 版本 c599c87 · 表述以原文为准
它做什么

安装后，Claude 可以实时抓取并分析来自 8 个来源的热门新闻，包括 Hacker News、GitHub Trending、Product Hunt、36氪、腾讯新闻、华尔街见闻、V2EX 和微博。它会自动扩展关键词，进行深度抓取，并以杂志风格生成带链接的摘要报告，同时保存到本地。

什么时候触发

当你要求查看最新新闻、技术动态、金融资讯或热门话题时触发。也可以直接说“news-aggregator-skill 如意如意”调出交互菜单。

装好后可以这样说
Claude 会抓取 HN 上 AI 相关文章并生成摘要。
Claude 会从多个来源抓取并整理成简报。
技能原文 SKILL.md作者撰写 · MIT · c599c87

News Aggregator Skill

Fetch real-time hot news from multiple sources.

Tools
fetch_news.py

Usage:

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

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.
  3. Annotation: Clearly mark these older items (e.g., "⚠️ 18h ago", "🔥 24h Hot") so the user knows they are supplementary.
  4. High Value: Always prioritize "SOTA", "Major Release", or "High Heat" items even if they slightly exceed the time window.
  5. 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".
  6. Deep Analysis (Required): For EACH item, you MUST leverage your AI capabilities to analyze:
  7. Core Value (核心价值): What specific problem does it solve? Why is it trending?
  8. Inspiration (启发思考): What technical or product insights can be drawn?
  9. 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.
按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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