exa-search▌
affaan-m/everything-claude-code · updated Apr 8, 2026
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Neural search for web content, code, companies, and people via the Exa MCP server.
Exa Search
Neural search for web content, code, companies, and people via the Exa MCP server.
When to Activate
- User needs current web information or news
- Searching for code examples, API docs, or technical references
- Researching companies, competitors, or market players
- Finding professional profiles or people in a domain
- Running background research for any development task
- User says "search for", "look up", "find", or "what's the latest on"
MCP Requirement
Exa MCP server must be configured. Add to ~/.claude.json:
"exa-web-search": {
"command": "npx",
"args": ["-y", "exa-mcp-server"],
"env": { "EXA_API_KEY": "YOUR_EXA_API_KEY_HERE" }
}
Get an API key at exa.ai.
This repo's current Exa setup documents the tool surface exposed here: web_search_exa and get_code_context_exa.
If your Exa server exposes additional tools, verify their exact names before depending on them in docs or prompts.
Core Tools
web_search_exa
General web search for current information, news, or facts.
web_search_exa(query: "latest AI developments 2026", numResults: 5)
Parameters:
| Param | Type | Default | Notes |
|---|---|---|---|
query |
string | required | Search query |
numResults |
number | 8 | Number of results |
type |
string | auto |
Search mode |
livecrawl |
string | fallback |
Prefer live crawling when needed |
category |
string | none | Optional focus such as company or research paper |
get_code_context_exa
Find code examples and documentation from GitHub, Stack Overflow, and docs sites.
get_code_context_exa(query: "Python asyncio patterns", tokensNum: 3000)
Parameters:
| Param | Type | Default | Notes |
|---|---|---|---|
query |
string | required | Code or API search query |
tokensNum |
number | 5000 | Content tokens (1000-50000) |
Usage Patterns
Quick Lookup
web_search_exa(query: "Node.js 22 new features", numResults: 3)
Code Research
get_code_context_exa(query: "Rust error handling patterns Result type", tokensNum: 3000)
Company or People Research
web_search_exa(query: "Vercel funding valuation 2026", numResults: 3, category: "company")
web_search_exa(query: "site:linkedin.com/in AI safety researchers Anthropic", numResults: 5)
Technical Deep Dive
web_search_exa(query: "WebAssembly component model status and adoption", numResults: 5)
get_code_context_exa(query: "WebAssembly component model examples", tokensNum: 4000)
Tips
- Use
web_search_exafor current information, company lookups, and broad discovery - Use search operators like
site:, quoted phrases, andintitle:to narrow results - Lower
tokensNum(1000-2000) for focused code snippets, higher (5000+) for comprehensive context - Use
get_code_context_exawhen you need API usage or code examples rather than general web pages
Related Skills
deep-research— Full research workflow using firecrawl + exa togethermarket-research— Business-oriented research with decision frameworks
How to use exa-search on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add exa-search
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches exa-search from GitHub repository affaan-m/everything-claude-code and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate exa-search. Access the skill through slash commands (e.g., /exa-search) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★71 reviews- ★★★★★Zara Shah· Dec 28, 2024
exa-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sakura Tandon· Dec 28, 2024
Registry listing for exa-search matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ava Chen· Dec 16, 2024
exa-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chinedu Park· Dec 12, 2024
I recommend exa-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sakura Johnson· Dec 8, 2024
exa-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Dec 4, 2024
Useful defaults in exa-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Chaitanya Patil· Nov 23, 2024
exa-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ava Gonzalez· Nov 19, 2024
exa-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Olivia Robinson· Nov 19, 2024
Keeps context tight: exa-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ren Ghosh· Nov 15, 2024
We added exa-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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