Verified:GitHub

exa-search

Search semantically across concepts to surface related content when keywords fail.

2|Updated Jan 13, 2026

Search Tags:#multi-source #semantic-search #information-retrieval #concept-discovery #perspective-research #research-tools

Ready to run

npx skills add https://github.com/mhagrelius/dotfiles --skill exa-search-mhagrelius

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: exa-search
Source: https://github.com/mhagrelius/dotfiles/tree/main/.claude/skills/exa-search
Command: npx skills add https://github.com/mhagrelius/dotfiles --skill exa-search-mhagrelius

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Helps users locate conceptually related content when exact keywords fail, enabling cross-perspective discovery and faster research.

Core Features & Use Cases

Semantic discovery prioritizes meaning over exact terms to surface related concepts and ideas.
Multi-perspective research across academic, industry, and critique sources.
Use cases include finding papers, articles, and sources that share underlying concepts with a given query.

Quick Start

Find papers on emergent AI behavior.

Frequently Asked Questions about exa-search

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

FAQPage Schema

How does semantic search find related papers when exact keyword search fails?▼

Semantic search finds related papers by prioritizing meaning over exact terms. It matches underlying concepts rather than specific keywords, allowing you to discover academic papers and industry reports that share ideas with your query even when exact terms fail.

What is the best way to research a concept across multiple perspectives and sources?▼

The best way to research across multiple perspectives is using multi-source aggregation. This approach collects academic papers, industry reports, and critiques, surfacing related content across diverse viewpoints to help you understand a topic comprehensively.

Can I use semantic search to find similar articles when I only have a general idea description?▼

Yes, you can use semantic search with only a general idea description. The engine matches conceptual meaning rather than exact terms, so inputting a general description will surface related articles and papers sharing underlying concepts with your query.

Do I need a specialized retrieval engine for concept discovery across academic sources?▼

Yes, concept discovery requires a semantic retrieval engine that emphasizes meaning over keywords and supports multi-source aggregation. The engine should also fallback to keyword-based search if semantic results are weak to ensure reliable academic content retrieval.

What happens when semantic search returns weak results for my research query?▼

When semantic search returns weak results, the system falls back to keyword-based search. This ensures you still retrieve relevant academic papers, industry reports, and critiques even when semantic matching does not surface sufficient related content.