Aggregated Search Skill

Multi-source content aggregation for hot topics research. Supports 15+ data sources.

Usage

/aggregated-search <keyword> [options]

Options:

• --sources=all

  - Search all sources (default)

• --sources=github,hn,reddit

  - Specific sources

• --limit=50

  - Max results per source (default: 50)

• --days=7

  - Content age limit in days (default: 7)

• --lang=en

  - Language: en, zh, all (default: all)

• --expand

  - Enable query expansion (auto-generate related terms)

Examples:

/aggregated-search "agentic AI"
/aggregated-search "LLM agents" --sources=github,hn,arxiv
/aggregated-search "大模型" --sources=chinese --lang=zh
/aggregated-search "RAG" --limit=100 --days=30

Supported Sources (15+)

Code & Projects

Tech Communities

Academic

News & Media

Chinese Sources (中文源)

Social Media

Meta Search (Recommended)

Source Groups

Use these shortcuts for common combinations:

--sources=code

--sources=community

--sources=academic

--sources=news

--sources=chinese

--sources=social

--sources=all

Workflow

Step 0: Query Expansion (if --expand)

If

--expand

Original: "agentic commerce"
    ↓
Expanded (max 5):
  - agentic commerce (original)
  - AI shopping agent
  - conversational commerce
  - e-commerce AI assistant
  - 智能购物

Use the prompt in

sources/query-expansion.md

Step 1: Parse Input

Extract keyword, sources, limit, days, language from user input.

Step 2: Parallel Search

CRITICAL: Search all sources in parallel using multiple tool calls in a single message.

For each source:

1. Read source instruction from

   sources/{source}.md

2. Execute API call or WebFetch
3. Parse results

Step 3: Aggregate & Deduplicate

1. Merge all results
2. Deduplicate by URL and title similarity (>80% = duplicate)
3. Sort by: relevance score, date, engagement
4. Tag with source name

Step 4: Output

Generate

raw/aggregated.md

# Aggregated Search: {keyword}

**Sources:** {count} sources searched
**Results:** {total} unique items
**Generated:** {timestamp}

---

## Summary (via Tavily AI)
> AI-generated summary of the topic...

## GitHub ({count})
| # | Repository | Stars | Description |
|---|------------|-------|-------------|

## Hacker News ({count})
| # | Title | Points | Comments |
|---|-------|--------|----------|

## Academic Papers ({count})
| # | Title | Year | Citations |
|---|-------|------|-----------|

## News & Blogs ({count})
| # | Title | Source | Date |
|---|-------|--------|------|

## Chinese Sources ({count})
| # | 标题 | 来源 | 日期 |
|---|------|------|------|

---

## Statistics
- Total sources: {sources_count}
- Total results: {total_count}
- Unique results: {unique_count}
- Date range: {earliest} to {latest}

Environment Variables

# Required for full functionality
export TAVILY_API_KEY="your-key"        # Tavily search

# Optional
export YOUTUBE_API_KEY="your-key"       # YouTube API
export TWITTER_BEARER_TOKEN="your-key"  # Twitter API (paid)
export PRODUCTHUNT_TOKEN="your-key"     # Product Hunt API

Integration

Works with ai-writing hottrend pipeline:

/aggregated-search "topic"
        ↓
  raw/aggregated.md
        ↓
  hottrend-draft agent
        ↓
  output/v1_draft.md