News Aggregator Skill

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

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🔄 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.

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📰 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")

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🛠️ 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.

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⚠️ 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

GitHub Owner

Owner: cclank

SKILL.md

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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