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News Aggregator Skill

该 Skill 通过 Python 脚本抓取 Hacker News、GitHub、Hugging Face Papers、媒体、AI 通讯、播客与博客等 28 个来源的实时内容，支持来源组合、关键词过滤及 general/finance/tech/social/ai_daily 等晨报配置，按统一模板生成简体中文摘要、热度与深度解读报告，保存到 reports 目录后展示。

abigale-cyber
abigale-cyber
热度 36.22
最近更新 2026-05-11 17:41:08
分类：数据依赖：代码执行 / 联网浏览搜索 / 读写文件 / 第三方 API / 内部调用大模型应用场景：研究 / 摘要总结 / 报告撰写 / 市场分析 ...

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add content-system-news-aggregator-skill
Metadata
name
news-aggregator-skill
description
Comprehensive news aggregator that fetches, filters, and deeply analyzes real-time content from 28 sources including Hacker News, GitHub, Hugging Face Papers, AI Newsletters, WallStreetCN, Weibo, and Podcasts. Use when user requests 'daily scans', 'tech news', 'finance updates', 'AI briefings', 'deep analysis', or says '如意如意' to open the interactive menu.

News Aggregator Skill

Fetch real-time hot news from 28 sources, generate deep analysis reports in Chinese.

🔄 Universal Workflow (3 Steps)

Every news request follows the same workflow, regardless of source or combination:

Step 1: Fetch Data

# Single source
python3 scripts/fetch_news.py --source <source_key> --no-save

# Multiple sources (comma-separated)
python3 scripts/fetch_news.py --source hackernews,github,wallstreetcn --no-save

# All sources (broad scan)
python3 scripts/fetch_news.py --source all --limit 15 --deep --no-save

# With keyword filter (auto-expand: "AI" → "AI,LLM,GPT,Claude,Agent,RAG")
python3 scripts/fetch_news.py --source hackernews --keyword "AI,LLM,GPT" --deep --no-save

Step 2: Generate Report

Read the output JSON and format every item using the Unified Report Template below. Translate all content to Simplified Chinese.

Step 3: Save & Present

Save the report to reports/YYYY-MM-DD/<source>_report.md, then display the full content to the user.

📰 Unified Report Template

All sources use this single template. Show/hide optional fields based on data availability.

#### N. [标题 (中文翻译)](https://original-url.com)
- **Source**: 源名 | **Time**: 时间 | **Heat**: 🔥 热度值
- **Links**: [Discussion](hn_url) | [GitHub](gh_url)     ← 仅在数据存在时显示
- **Summary**: 一句话中文摘要。
- **Deep Dive**: 💡 **Insight**: 深度分析（背景、影响、技术价值）。

Source-Specific Adaptations

Only the differences from the universal template:

Source Adaptation
Hacker News MUST include [Discussion](hn_url) link
GitHub Use 🌟 Stars for Heat, add Lang field, add #Tags in Deep Dive
Hugging Face Use 🔥 +N upvotes for Heat, include [GitHub](url) if present, write 深度解读 (not just translate abstract)
Weibo Preserve exact heat text (e.g. "108万")

🛠️ Tools

fetch_news.py

Arg Description Default
--source Source key(s), comma-separated. See table below. all
--limit Max items per source 15
--keyword Comma-separated keyword filter None
--deep Download article text for richer analysis Off
--save Force save to reports dir Auto for single source
--outdir Custom output directory reports/YYYY-MM-DD/

Available Sources (28)

Category Key Name
Global News hackernews Hacker News
36kr 36氪
wallstreetcn 华尔街见闻
tencent 腾讯新闻
weibo 微博热搜
v2ex V2EX
producthunt Product Hunt
github GitHub Trending
AI/Tech huggingface HF Daily Papers
ai_newsletters All AI Newsletters (aggregate)
bensbites Ben's Bites
interconnects Interconnects (Nathan Lambert)
oneusefulthing One Useful Thing (Ethan Mollick)
chinai ChinAI (Jeffrey Ding)
memia Memia
aitoroi AI to ROI
kdnuggets KDnuggets
Podcasts podcasts All Podcasts (aggregate)
lexfridman Lex Fridman
80000hours 80,000 Hours
latentspace Latent Space
Essays essays All Essays (aggregate)
paulgraham Paul Graham
waitbutwhy Wait But Why
jamesclear James Clear
farnamstreet Farnam Street
scottyoung Scott Young
dankoe Dan Koe

daily_briefing.py (Morning Routines)

Pre-configured multi-source profiles:

python3 scripts/daily_briefing.py --profile <profile>
Profile Sources Instruction File
general HN, 36Kr, GitHub, Weibo, PH, WallStreetCN instructions/briefing_general.md
finance WallStreetCN, 36Kr, Tencent instructions/briefing_finance.md
tech GitHub, HN, Product Hunt instructions/briefing_tech.md
social Weibo, V2EX, Tencent instructions/briefing_social.md
ai_daily HF Papers, AI Newsletters instructions/briefing_ai_daily.md
reading_list Essays, Podcasts (Use universal template)

Workflow: Execute script → Read corresponding instruction file → Generate report following both the instruction file AND the universal template.

⚠️ Rules (Strict)

  1. Language: ALL output in Simplified Chinese (简体中文). Keep well-known English proper nouns (ChatGPT, Python, etc.).
  2. Time: MANDATORY field. Never skip. If missing in JSON, mark as "Unknown Time". Preserve "Real-time" / "Today" / "Hot" as-is.
  3. Anti-Hallucination: Only use data from the JSON. Never invent news items. Use simple SVO sentences. Do not fabricate causal relationships.
  4. Smart Keyword Expansion: When user says "AI" → auto-expand to "AI,LLM,GPT,Claude,Agent,RAG,DeepSeek". Similar expansions for other domains.
  5. Smart Fill: If results < 5 items in a time window, supplement with high-value items from wider range. Mark supplementary items with ⚠️.
  6. Save: Always save report to reports/YYYY-MM-DD/ before displaying.

📋 Interactive Menu

When the user says "如意如意" or asks for "menu/help":

  1. Read templates.md
  2. Display the menu
  3. Execute the user's selection using the Universal Workflow above

Requirements

  • Python 3.8+, pip install -r requirements.txt
  • Playwright (for HF Papers & Ben's Bites): playwright install chromium