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