💡 摘要
该技能从多个主要来源聚合和分析实时新闻,提供科技和金融更新。
🎯 适合人群
🤖 AI 吐槽: “看起来很能打,但别让配置把人劝退。”
风险:Medium。建议检查:是否执行 shell/命令行指令;是否发起外网请求(SSRF/数据外发);文件读写范围与路径穿越风险。以最小权限运行,并在生产环境启用前审计代码与依赖。
name: news-aggregator-skill description: "Comprehensive news aggregator that fetches, filters, and deeply analyzes real-time content from 8 major sources: Hacker News, GitHub Trending, Product Hunt, 36Kr, Tencent News, WallStreetCN, V2EX, and Weibo. Best for 'daily scans', 'tech news briefings', 'finance updates', and 'deep interpretations' of hot topics."
News Aggregator Skill
Fetch real-time hot news from multiple sources.
Tools
fetch_news.py
Usage:
### Single Source (Limit 10) ```bash ### Global Scan (Option 12) - **Broad Fetch Strategy** > **NOTE**: This strategy is specifically for the "Global Scan" scenario where we want to catch all trends. ```bash # 1. Fetch broadly (Massive pool for Semantic Filtering) python3 scripts/fetch_news.py --source all --limit 15 --deep # 2. SEMANTIC FILTERING: # Agent manually filters the broad list (approx 120 items) for user's topics.
Single Source & Combinations (Smart Keyword Expansion)
CRITICAL: You MUST automatically expand the user's simple keywords to cover the entire domain field.
- User: "AI" -> Agent uses:
--keyword "AI,LLM,GPT,Claude,Generative,Machine Learning,RAG,Agent" - User: "Android" -> Agent uses:
--keyword "Android,Kotlin,Google,Mobile,App" - User: "Finance" -> Agent uses:
--keyword "Finance,Stock,Market,Economy,Crypto,Gold"
# Example: User asked for "AI news from HN" (Note the expanded keywords) python3 scripts/fetch_news.py --source hackernews --limit 20 --keyword "AI,LLM,GPT,DeepSeek,Agent" --deep
Specific Keyword Search
Only use --keyword for very specific, unique terms (e.g., "DeepSeek", "OpenAI").
python3 scripts/fetch_news.py --source all --limit 10 --keyword "DeepSeek" --deep
Arguments:
--source: One ofhackernews,weibo,github,36kr,producthunt,v2ex,tencent,wallstreetcn,all.--limit: Max items per source (default 10).--keyword: Comma-separated filters (e.g. "AI,GPT").--deep: [NEW] Enable deep fetching. Downloads and extracts the main text content of the articles.
Output:
JSON array. If --deep is used, items will contain a content field associated with the article text.
Interactive Menu
When the user says "news-aggregator-skill 如意如意" (or similar "menu/help" triggers):
- READ the content of
templates.mdin the skill directory. - DISPLAY the list of available commands to the user exactly as they appear in the file.
- GUIDE the user to select a number or copy the command to execute.
Smart Time Filtering & Reporting (CRITICAL)
If the user requests a specific time window (e.g., "past X hours") and the results are sparse (< 5 items):
- Prioritize User Window: First, list all items that strictly fall within the user's requested time (Time < X).
- Smart Fill: If the list is short, you MUST include high-value/high-heat items from a wider range (e.g. past 24h) to ensure the report provides at least 5 meaningful insights.
- Annotation: Clearly mark these older items (e.g., "⚠️ 18h ago", "🔥 24h Hot") so the user knows they are supplementary.
- High Value: Always prioritize "SOTA", "Major Release", or "High Heat" items even if they slightly exceed the time window.
- GitHub Trending Exception: For purely list-based sources like GitHub Trending, strictly return the valid items from the fetched list (e.g. Top 10). List ALL fetched items. Do NOT perform "Smart Fill".
- Deep Analysis (Required): For EACH item, you MUST leverage your AI capabilities to analyze:
- Core Value (核心价值): What specific problem does it solve? Why is it trending?
- Inspiration (启发思考): What technical or product insights can be drawn?
- Scenarios (场景标签): 3-5 keywords (e.g.
#RAG #LocalFirst #Rust).
- Deep Analysis (Required): For EACH item, you MUST leverage your AI capabilities to analyze:
6. Response Guidelines (CRITICAL)
Format & Style:
- Language: Simplified Chinese (简体中文).
- Style: Magazine/Newsletter style (e.g., "The Economist" or "Morning Brew" vibe). Professional, concise, yet engaging.
- Structure:
- Global Headlines: Top 3-5 most critical stories across all domains.
- Tech & AI: Specific section for AI, LLM, and Tech items.
- Finance / Social: Other strong categories if relevant.
- Item Format:
- Title: MUST be a Markdown Link to the original URL.
- ✅ Correct:
### 1. [OpenAI Releases GPT-5](https://...) - ❌ Incorrect:
### 1. OpenAI Releases GPT-5
- ✅ Correct:
- Metadata Line: Must include Source, Time/Date, and Heat/Score.
- 1-Liner Summary: A punchy, "so what?" summary.
- Deep Interpretation (Bulleted): 2-3 bullet points explaining why this matters, technical details, or context. (Required for "Deep Scan").
- Title: MUST be a Markdown Link to the original URL.
Output Artifact:
- Always save the full report to
reports/directory with a timestamped filename (e.g.,reports/hn_news_YYYYMMDD_HHMM.md). - Present the full report content to the user in the chat.
优点
- 从多个来源聚合新闻。
- 提供对热门话题的深入分析。
- 可定制的关键词过滤。
缺点
- 可能会让用户感到信息过载。
- 需要手动过滤特定主题。
- 依赖外部来源的准确性。
相关技能
免责声明:本内容来源于 GitHub 开源项目,仅供展示和评分分析使用。
版权归原作者所有 cclank.
