News Aggregator Skill

Fetch real-time hot news from multiple sources.

⚠️ Global Rules (Strict Enforcement)

1. Mandatory Time Display: EVERY report item, regardless of the source or command used (Single Source, Morning Routine, or Combinations), MUST include the precise publication time or relative time (e.g., "10:30", "2 hours ago", "2024-01-20").

   • NEVER skip the time field.
   • NEVER hallucinate the time. If it's missing in the JSON, mark it as "Unknown Time".
   • For "Real-time" or "Trending" lists (e.g., Weibo, GitHub), preserve the "Real-time" or "Today" tag.

2. Logical Integrity (Anti-Hallucination):

   • NO INVENTED CAUSALITY: Do not use "Because", "Although", "Due to", or "However" unless the source text EXPLICITLY supports this relationship.
   • SVO Preference: Use simple Subject-Verb-Object sentences. Avoid complex compound sentences that force you to invent logical bridges.
   • Fact Check: If you fix grammar, you arguably make a claim. If you change "A, B" to "A caused B", you MUST be 100% sure. When in doubt, leave it as two separate sentences.

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 of hackernews, 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.

daily_briefing.py (Unified Morning Routine)

Run this single script to fetch all necessary data for the morning briefing.

python3 scripts/daily_briefing.py --profile [general|finance|tech|social] > briefing_data.json

Workflow:

1. Execute scripts/daily_briefing.py with the desired profile.
2. READ the corresponding instruction file in instructions/:
   • general -> instructions/briefing_general.md
   • finance -> instructions/briefing_finance.md
   • tech -> instructions/briefing_tech.md
   • social -> instructions/briefing_social.md
3. Generate the report strictly satisfying the volume constraints in the instruction file.

Interactive Menu

When the user says "news-aggregator-skill 如意如意" (or similar "menu/help" triggers):

1. READ the content of templates.md in the skill directory.
2. DISPLAY the list of available commands to the user exactly as they appear in the file.
3. GUIDE the user to select a number or copy the command to execute.
4. Morning Routine (Recommended): For the best quality, guide the user to run the "Three-Course Morning Routine" (Options 12, 13, 14) sequentially, rather than combining them into one request. This ensures each report gets full AI Context attention.

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):

1. Prioritize User Window: First, list all items that strictly fall within the user's requested time (Time < X).
2. 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.
3. High Value: Always prioritize "SOTA", "Major Release", or "High Heat" items even if they slightly exceed the time window.
4. 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).

6. Response Guidelines (CRITICAL)

Format & Style:

• Language: Simplified Chinese (简体中文). (IMPORTANT: Translate Title, Summary, and Analysis into Chinese)
• Style: Magazine/Newsletter style (e.g., "The Economist" or "Morning Brew" vibe). Professional, concise, yet engaging.
• Structure:
  • Global Headlines: Top 15-20 critical stories across all domains. (For Global Scan, aim for comprehensive coverage, not just a few highlights).
  • Tech & AI: Specific section for AI, LLM, and Tech items.
  • Finance / Social: Other strong categories if relevant.
• Item Format Template (STRICT):
  Switching to List Format for better rendering. Do NOT use Blockquotes (>).

  #### 1. [Title (Translated)](https://original-url.com)
  - **Source**: SourceName | **Time**: X hours ago | **Heat**: 🔥 999
  - **Summary**: [Hacker News Discussion](hn_url) (if valid) + One sentence summary in Chinese.
  - **Deep Dive**: 💡 **Insight**: Deep analysis, market impact, or technical context.

  • Zero Hallucination & Diligence (CRITICAL):
    • Truth: You must ONLY use data present in the provided JSON. NEVER invent news items.
    • Diligence: Do NOT use "No significant updates" as an excuse to skip analysis. You MUST exhaustively review the JSON.
    • Fallback: Only state "No significant updates" if the fetching script truly returned 0 relevant items. If the source is empty, state so clearly (e.g., "Source returned 0 items"). DO NOT fabricate news to fill the space.
• Key Rules:
  • Hacker News (HN): For HN items, you MUST provide the link to the HN discussion page (comments) in addition to the original article link.
  • Translation: Translate titles, summaries, and deep dive analysis into Simplified Chinese.
  • Title: MUST be a clickable link. Do NOT use plain text titles.
  • Metadata: Source, Time, and Heat MUST be visible immediately below the title.
  • Time: MANDATORY FIELD. You MUST include the time provided in the JSON (e.g., "2 hours ago", "2024-01-20", "Real-time", "Today").
    • If the JSON says "Real-time", "Today", or "Hot", display it exactly as is.
    • DO NOT SKIP THIS FIELD.
  • Deep Interpretation (Bulleted): 2-3 bullet points explaining why this matters, technical details, or context. (Required for "Deep Scan").

Output Artifact:

• Always save the full report to a date-based subdirectory in reports/ (e.g., reports/YYYY-MM-DD/filename_HHMM.md). If the directory does not exist, you MUST create it first.
• IMPORTANT: The Agent (You) are responsible for formatting the JSON output into Markdown. Do not rely on external scripts for summarization.
• Present the full report content to the user in the chat.