deal-hunt

📁 tavily-ai/tavily-plugins 📅 Jan 22, 2026

总安装量

周安装量

#4093

全站排名

安装命令

npx skills add https://github.com/tavily-ai/tavily-plugins --skill deal-hunt

Agent 安装分布

claude-code 50

codex 28

gemini-cli 26

opencode 25

cursor 23

antigravity 19

Skill 文档

Deal Hunt

Search for deals on any product. Returns raw Tavily search results – Claude analyzes them to find the best prices.

Prerequisites

Tavily API Key Required – Get your key at https://tavily.com

Add to ~/.claude/settings.json:

{
  "env": {
    "TAVILY_API_KEY": "tvly-your-api-key-here"
  }
}

Usage

# Search entire web (default - no domain filter)
python scripts/deal_hunt.py "Dyson V15"

# Multi-query search (max 3, runs in parallel, deduplicates results)
python scripts/deal_hunt.py "AirPods Pro" --queries "AirPods Pro deal,AirPods Pro coupon,AirPods Pro discount"

# Limit to specific sites
python scripts/deal_hunt.py "MacBook Air" --domains amazon.com,walmart.com,bestbuy.com

# Custom single query
python scripts/deal_hunt.py "Nintendo Switch" --query "Nintendo Switch OLED bundle deal"

# Fresh deals only
python scripts/deal_hunt.py "PS5" --time-range day

CLI Parameters

Option	Short	Default	Description
`product`	–	Required	Product name
`--query`	`-q`	`{product} deal price`	Single custom search query
`--queries`		None	Comma-separated queries (max 3), runs in parallel with dedup
`--domains`	`-d`	None (search all)	Optionally limit to specific domains
`--max-results`	`-n`	10	Number of results per query
`--time-range`	`-t`	week	day, week, month, year, none
`--search-depth`	`-s`	advanced	basic, advanced, fast, ultrafast

Output

Returns JSON with results:

{
  "meta": {
    "product": "AirPods Pro",
    "queries": ["AirPods Pro deal", "AirPods Pro coupon"],
    "domains": null,
    "time_range": "week",
    "search_time": "2026-01-13T...",
    "total_results": 15
  },
  "results": [
    {
      "title": "...",
      "url": "https://...",
      "content": "...",
      "score": 0.95
    }
  ]
}

When using --queries, results are deduplicated by URL (highest score kept, content merged).

Output Schema for Analysis

After running the search, Claude should analyze results and structure findings as:

{
  "product": "Sony WH-1000XM5",
  "best_deal": {
    "price": 279.99,
    "original_price": 399.99,
    "discount": "30% off",
    "retailer": "Amazon",
    "url": "https://amazon.com/...",
    "condition": "new",
    "in_stock": true
  },
  "all_deals": [
    {
      "price": 279.99,
      "retailer": "Amazon",
      "url": "https://...",
      "notes": "Prime shipping"
    },
    {
      "price": 169.99,
      "retailer": "eBay via Slickdeals",
      "url": "https://...",
      "notes": "Refurbished"
    }
  ],
  "coupons": [
    {
      "code": "AUDIO10",
      "discount": "10% off",
      "retailer": "Best Buy",
      "expires": "2026-01-31"
    }
  ],
  "summary": "Best new price is $279.99 at Amazon (30% off). Refurbished available for $169.99."
}

Claude extracts prices from content, compares deals, and presents the best options with purchase links.

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