agentleFS
Sign inStart free
mturacmturac/everything-openai-codex/.agents/skills/exa-search/SKILL.md

exa-search

Skill89 starsChanged 6 months agoMITView on GitHub

Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.

  • Reads credentials

We split this skill into the rules it gives an agent

3 rules. 0 are common, 1 is distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 61 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Start an AI research agent that runs asynchronously.

    line 122
  2. Very specific:

    Use web_search_exa for broad queries, web_search_advanced_exa for filtered results

    line 160
  3. Very specific:

    Use crawling_exa to get full content from specific URLs found in search results

    line 163

“Common” is measured: kept by at least 6 in 10 of the 61 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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 ~/.codex.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.

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:

ParamTypeDefaultNotes
querystringrequiredSearch query
numResultsnumber8Number of results

web_search_advanced_exa

Filtered search with domain and date constraints.

web_search_advanced_exa(
  query: "React Server Components best practices",
  numResults: 5,
  includeDomains: ["github.com", "react.dev"],
  startPublishedDate: "2025-01-01"
)

Parameters:

ParamTypeDefaultNotes
querystringrequiredSearch query
numResultsnumber8Number of results
includeDomainsstring[]noneLimit to specific domains
excludeDomainsstring[]noneExclude specific domains
startPublishedDatestringnoneISO date filter (start)
endPublishedDatestringnoneISO date filter (end)

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:

ParamTypeDefaultNotes
querystringrequiredCode or API search query
tokensNumnumber5000Content tokens (1000-50000)

company_research_exa

Research companies for business intelligence and news.

company_research_exa(companyName: "OpenAI", numResults: 5)

Parameters:

ParamTypeDefaultNotes
companyNamestringrequiredCompany name
numResultsnumber5Number of results

people_search_exa

Find professional profiles and bios.

people_search_exa(query: "AI safety researchers at OpenAI", numResults: 5)

crawling_exa

Extract full page content from a URL.

crawling_exa(url: "https://example.com/article", tokensNum: 5000)

Parameters:

ParamTypeDefaultNotes
urlstringrequiredURL to extract
tokensNumnumber5000Content tokens

deep_researcher_start / deep_researcher_check

Start an AI research agent that runs asynchronously.

# Start research
deep_researcher_start(query: "comprehensive analysis of AI code editors in 2026")

# Check status (returns results when complete)
deep_researcher_check(researchId: "<id from start>")

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 Due Diligence

company_research_exa(companyName: "Vercel", numResults: 5)
web_search_advanced_exa(query: "Vercel funding valuation 2026", numResults: 3)

Technical Deep Dive

# Start async research
deep_researcher_start(query: "WebAssembly component model status and adoption")
# ... do other work ...
deep_researcher_check(researchId: "<id>")

Tips

  • Use web_search_exa for broad queries, web_search_advanced_exa for filtered results
  • Lower tokensNum (1000-2000) for focused code snippets, higher (5000+) for comprehensive context
  • Combine company_research_exa with web_search_advanced_exa for thorough company analysis
  • Use crawling_exa to get full content from specific URLs found in search results
  • deep_researcher_start is best for comprehensive topics that benefit from AI synthesis

Related Skills

  • deep-research — Full research workflow using firecrawl + exa together
  • market-research — Business-oriented research with decision frameworks

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

Reviews can't be read right now.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free