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news-aggregator-skill

스타13

포크3

업데이트2026년 5월 26일 10:07

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.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

chengjialu8888

chengjialu8888/AI_News_Digest

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

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)

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

이 저장소의 다른 Skills

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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)

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