💡 Summary
Exa Search is a semantic search skill for Claude Code that leverages the Exa API for intelligent content discovery.
🎯 Target Audience
🤖 AI Roast: “Powerful, but the setup might scare off the impatient.”
Risk: Medium. Review: shell/CLI command execution; outbound network access (SSRF, data egress); API keys/tokens handling and storage; filesystem read/write scope and path traversal. Run with least privilege and audit before enabling in production.
Exa Search Skill
Semantic search skill for Claude Code using Exa API. Provides embeddings-based intelligent search, similar content discovery, and structured research capabilities.
Features
- 🔍 Semantic Search: Neural, fast, deep, and auto search modes
- 📄 Content Extraction: Get clean, parsed HTML content from search results
- 🔗 Find Similar: Discover semantically similar pages based on a URL
- 💬 Direct Answers: Get immediate answers to questions
- 📊 Structured Research: Automated research with custom output schemas
- 🎯 Category Filtering: Search by company, people, research papers, news, PDFs, GitHub, tweets, etc.
- 💰 Cost Tracking: Detailed cost breakdown for each request
Installation
Option 1: Install via skills CLI (Recommended)
# Install globally to all detected agents (Claude Code, Cursor, Codex, etc.) npx skills add -g BenedictKing/exa-search # Or install to current project only npx skills add BenedictKing/exa-search
The skill will be automatically installed and loaded by Claude Code.
Option 2: Manual Installation via Git Clone
- Clone this repository to your Claude skills directory:
cd ~/.claude/skills git clone https://github.com/BenedictKing/exa-search.git
- Configure your API key:
cd exa-search/.claude/skills/exa-search cp .env.example .env # Edit .env and add your Exa API key
Configuration
Get your API key from Exa Dashboard.
Two ways to configure:
- Environment Variable (recommended):
export EXA_API_KEY=your_api_key_here
.envfile:
# In .claude/skills/exa-search/.env EXA_API_KEY=your_api_key_here
Usage
Trigger the Skill
Use /exa-search or let Claude automatically invoke it when you need:
- Semantic web search
- Finding similar content
- Direct answers to questions
- Structured research
Example Queries
Semantic Search:
Find latest research papers on transformer architectures
Find Similar:
Find pages similar to https://arxiv.org/abs/1706.03762
Direct Answer:
What are the key differences between GPT-4 and Claude?
Structured Research:
Research the top AI companies and their key metrics
API Endpoints
1. Search
Embeddings-based semantic search with multiple modes:
neural: Semantic search using embeddingsfast: Quick keyword-based searchauto: Automatically choose best method (default)deep: Comprehensive deep search
2. Contents
Extract full content from search result IDs.
3. Find Similar
Discover semantically similar pages based on a given URL.
4. Answer
Get direct answers to questions with source citations.
5. Research
Automated deep research with custom output schemas.
Advanced Features
Category Filtering
Search within specific content types:
company,people,research paper,news,pdf,github,tweet, etc.
Date Filtering
Filter by publication date or crawl date:
{ "startPublishedDate": "2025-01-01", "endPublishedDate": "2025-12-31" }
Domain Control
Include or exclude specific domains:
{ "includeDomains": ["arxiv.org", "github.com"], "excludeDomains": ["example.com"] }
Content Options
Control what content to retrieve:
{ "contents": { "text": true, "highlights": true, "summary": true } }
Pricing
Exa API pricing (as of 2026):
- Neural Search (1-25 results): $0.005
- Neural Search (26-100 results): $0.025
- Deep Search (1-25 results): $0.015
- Deep Search (26-100 results): $0.075
- Content text/highlights/summary: $0.001 per page
Each API response includes a costDollars field with detailed cost breakdown.
Architecture
This skill uses a two-phase architecture:
- Main skill: Understands user intent, chooses endpoint, assembles payload
- Sub-skill (exa-fetcher): Executes HTTP calls in isolated context
This design minimizes token usage and keeps conversation history clean.
Comparison with Tavily
| Feature | Exa | Tavily | |---------|-----|--------| | Search Type | Embeddings-based semantic | Keyword + AI-enhanced | | Find Similar | ✅ | ❌ | | Category Search | ✅ (company, people, papers, etc.) | ❌ | | Search Modes | neural/fast/deep/auto | basic/advanced | | Cost Tracking | ✅ Detailed per request | ❌ | | Crawl/Map | ❌ | ✅ |
Use Exa when:
- You need semantic/embeddings-based search
- Finding similar content is important
- You want category-specific searches
- Cost tracking is required
Use Tavily when:
- You need site crawling/mapping
- You want structured research with citations
- You prefer keyword-based search
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
MIT License - see LICENSE file for details.
Author
BenedictKing
- GitHub: @BenedictKing
Links
Pros
- Offers multiple search modes for flexibility
- Provides detailed cost tracking for API usage
- Supports semantic search and content extraction
- Allows for advanced filtering options
Cons
- Requires API key configuration
- May have costs associated with API usage
- Dependency on external Exa API for functionality
- Learning curve for advanced features
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Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.
Copyright belongs to the original author BenedictKing.
