exa-search

exa-search - Semantic Search and Intelligent Content Discovery Skills

Skill Overview

exa-search is a Claude Code skill based on the Exa API. It provides semantic search, similar-content discovery, and structured research capabilities, enabling AI to understand search intent rather than simply matching keywords.

Use Cases

Advanced Searches Requiring Semantic Understanding

When traditional keyword search can’t meet your needs, exa-search can use vector embedding techniques to understand search intent and find semantically related content. For example, searching for “innovative companies in the AI space” helps identify relevant startups and technology companies, rather than just web pages containing those exact keywords.

Find Similar or Related Content

When you need to find other resources similar to a particular piece of content, the skill can recommend based on semantic meaning. For instance, given a research paper, it can find other papers on similar topics; given a company website, it can find relevant competitors or potential partners.

Structured Search by Category

If you need precise searches within specific categories, exa-search supports filtering by categories such as companies, people, and research papers. This is especially useful for market research, academic study, competitor analysis, and similar scenarios.

Core Features

Semantic/Vector Embedding Search

Using Exa’s vector embedding technology, it understands the semantic intent of a search query—not just the literal words. That means searching for “machine learning tools” can surface pages that include related AI frameworks, model libraries, and other relevant content, even if the page doesn’t contain the exact same keywords.

Intelligent Recommendations for Similar Content

By inputting any webpage URL or content, the skill can discover other content that is semantically similar. This is very helpful for content research, competitor analysis, and academic literature studies, as it can uncover related resources that are difficult to find through traditional methods.

Category Retrieval and Structured Research

Supports targeted searching by content type, including company information, personal profiles, research papers, news articles, and more. This structured search capability makes research results more precise and actionable.

Frequently Asked Questions

What is exa-search? What is it used for?

exa-search is a Claude Code skill integrated with the Exa API. It is mainly used for semantic search and similar-content discovery. It’s suitable for scenarios that require understanding search intent, such as finding research papers, competitor analysis, and company research. Unlike traditional search engines, it uses vector embeddings to find semantically relevant content—not just keyword matches.

How do I install and configure exa-search?

Installation is straightforward: run npx skills add -g BenedictKing/exa-search in your terminal. After installing, you need to configure your Exa API key—recommended to set it via environment variables. Once configured, you can naturally use the skill in Claude Code conversations to perform searches.

How do I get an Exa API key?

You need to access Exa’s official website (exa.ai), register an account, and obtain an API key. Some features may have free-tier limits; for exact pricing, refer to Exa’s official documentation. After getting your API key, it’s recommended to set it as an environment variable to avoid hard-coding it in your code.

What types of search does exa-search support?

It mainly supports three types: semantic search (understanding intent via vector embeddings), similar-content discovery (finding similar resources based on given content), and category search (filtering by categories such as companies, people, and papers). These features can be used individually or combined.

How is exa-search different from other search skills?

Unlike tavily-web (real-time web search) or firecrawl-scraper (webpage scraping), exa-search focuses on semantic understanding and similarity matching. It doesn’t scrape webpage content; instead, it uses Exa’s search index for intelligent matching, making it more suitable for scenarios where search intent needs to be understood.

Do I need to pay to use exa-search?

The skill itself is a free and open-source tool, but the Exa API it depends on may require payment. Exa provides some free credits; after that, you need to subscribe to a paid plan. For specific pricing and usage limits, check Exa’s official documentation.

What categories of content can I search with exa-search?

It supports multiple content categories, including but not limited to: company information, personal profiles, academic papers, news articles, blog posts, product pages, and more. You can specify a category during search to get more precise results.

What is the best way to use exa-search?

It’s recommended to set the API key as an environment variable and describe your search needs in natural language in the Claude Code conversation. For example: “Help me find a few AI healthcare startups” or “Find some research papers about large model alignment.” The skill will automatically convert your request into an appropriate Exa search query.