exa-search

Perform semantic search and content discovery via the Exa API.

Updated Jan 26, 2026
One-click install
npx skills add https://github.com/TwuanMinn/fadelab --skill exa-search-twuanminn
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: exa-search
Source: https://github.com/TwuanMinn/fadelab/tree/main/.agent/skills/skills/exa-search
Command: npx skills add https://github.com/TwuanMinn/fadelab --skill exa-search-twuanminn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides fast semantic search and content discovery for research materials by leveraging the Exa API to surface relevant content and establish relationships between items.

Core Features & Use Cases

  • Semantic search with embeddings to retrieve related documents and notes.
  • Content discovery across datasets, researchers, and papers to accelerate literature reviews.
  • Use Case: A research team quickly surfaces related studies for a literature review and creates linked insight notes.

Quick Start

Install the skill and configure your Exa API key, then ask it to perform a semantic search for related content.

Frequently Asked Questions about exa-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use semantic search for a literature review?

Semantic search for literature reviews uses embeddings to retrieve related documents and notes across datasets and papers. It accelerates cross-domain discovery by establishing relationships between research materials to surface relevant content quickly.

Can I filter content discovery results by category and rank by relevance?

Yes, content discovery supports category-based filtering and ranking by relevance. You can apply these filters to semantic search results to refine the retrieval of related documents, researchers, and papers across specific domains.

Do I need an Exa API key to perform semantic search on research materials?

Yes, you need an Exa API key to perform semantic search on research materials. Configuring your Exa API access is required to leverage embeddings for content discovery and retrieve related documents and notes.

What is the best way to find related studies and create linked insight notes?

The best way to find related studies and create linked insight notes is using semantic search with embeddings to surface relevant content. This establishes relationships between retrieved papers and datasets to accelerate cross-domain discovery.

How does embeddings-based content discovery work for cross-domain research?

Embeddings-based content discovery works by mapping semantic relationships across companies, people, and papers. It retrieves related documents and notes to accelerate literature reviews and dataset exploration across different domains.