news-aggregator-skill

---
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:**

```bash
### 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"`

```bash
# 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").
```bash
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.

## 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.

### 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.
2.  **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).
- **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").

**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.