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1.1情报虾

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.
情报虾
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概述

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/_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:

SourceAdaptation
------
Hacker NewsMUST include Discussion link
GitHubUse 🌟 Stars for Heat, add Lang field, add #Tags in Deep Dive
Hugging FaceUse 🔥 +N upvotes for Heat, include GitHub if present, write 深度解读 (not just translate abstract)
WeiboPreserve exact heat text (e.g. "108万")

🛠️ Tools

fetch_news.py

ArgDescriptionDefault
---------
--sourceSource key(s), comma-separated. See table below.all
--limitMax items per source15
--keywordComma-separated keyword filterNone
--deepDownload article text for richer analysisOff
--saveForce save to reports dirAuto for single source
--outdirCustom output directoryreports/YYYY-MM-DD/

Available Sources (28)

CategoryKeyName
---------
Global NewshackernewsHacker News
36kr36氪
wallstreetcn华尔街见闻
tencent腾讯新闻
weibo微博热搜
v2exV2EX
producthuntProduct Hunt
githubGitHub Trending
AI/TechhuggingfaceHF Daily Papers
ai_newslettersAll AI Newsletters (aggregate)
bensbitesBen's Bites
interconnectsInterconnects (Nathan Lambert)
oneusefulthingOne Useful Thing (Ethan Mollick)
chinaiChinAI (Jeffrey Ding)
memiaMemia
aitoroiAI to ROI
kdnuggetsKDnuggets
PodcastspodcastsAll Podcasts (aggregate)
lexfridmanLex Fridman
80000hours80,000 Hours
latentspaceLatent Space
EssaysessaysAll Essays (aggregate)
paulgrahamPaul Graham
waitbutwhyWait But Why
jamesclearJames Clear
farnamstreetFarnam Street
scottyoungScott Young
dankoeDan Koe

daily_briefing.py (Morning Routines)

Pre-configured multi-source profiles:

python3 scripts/daily_briefing.py --profile <profile>
ProfileSourcesInstruction File
---------
generalHN, 36Kr, GitHub, Weibo, PH, WallStreetCNinstructions/briefing_general.md
financeWallStreetCN, 36Kr, Tencentinstructions/briefing_finance.md
techGitHub, HN, Product Huntinstructions/briefing_tech.md
socialWeibo, V2EX, Tencentinstructions/briefing_social.md
ai_dailyHF Papers, AI Newslettersinstructions/briefing_ai_daily.md
reading_listEssays, 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

版本历史

共 1 个版本

  • v1.0.0 Initial release 当前
    2026-06-09 21:13 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

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