News Aggregator Skill

Tools

fetch_news.py

### 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)

• 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

--keyword

python3 scripts/fetch_news.py --source all --limit 10 --keyword "DeepSeek" --deep

• --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.

--deep

content

Interactive Menu

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.

Smart Time Filtering & Reporting (CRITICAL)

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.
3. Annotation: Clearly mark these older items (e.g., "⚠️ 18h ago", "🔥 24h Hot") so the user knows they are supplementary.
4. High Value: Always prioritize "SOTA", "Major Release", or "High Heat" items even if they slightly exceed the time window.
5. 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)

• 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

  • 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").

• 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.