Query Tavily's LLM-optimized search API for web results with snippets and metadata.
npx skills add https://github.com/xdanger/dotfiles --skill search-xdanger
Please help me install this Agent Skill. Skill: search Source: https://github.com/xdanger/dotfiles/tree/main/agents/skills/search Command: npx skills add https://github.com/xdanger/dotfiles --skill search-xdanger
Web research is time-consuming and error-prone when gathering credible information. This skill accelerates discovery by querying Tavily's LLM-optimized search API to return relevant results with concise content snippets, relevance scores, and metadata, enabling faster, more reliable insights for decision-making.
Ask it to search the web for your topic and return a concise set of results with snippets and metadata.
High-intent search queries and answers about installing and using this skill.
You can retrieve LLM-optimized web search results by querying the Tavily API, which returns structured data including titles, URLs, snippets, relevance scores, and metadata for faster AI-driven insights.
Yes, web search queries support configurable parameters like include_domains, exclude_domains, time_range, and max_results to tailor results for specific research or due diligence needs.
You can retrieve raw web content by enabling the include_raw_content parameter in your search query, allowing the API to return full page content alongside standard snippets and metadata.
Yes, authentication via Tavily MCP is required, supporting either an OAuth flow or a direct API key to authorize web search requests and retrieve structured results.
Using an LLM-optimized search API is the best way to gather competitive research data, providing concise content snippets and relevance scores to accelerate discovery and decision-making.
Irrelevant results often occur when search parameters are unconfigured; filtering by time_range, include_domains, and adjusting max_results helps refine the API output for better relevance.
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