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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | exa-search |
| description | 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. |
Neural search for web content, code, companies, and people via the Exa MCP server.
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.
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 |
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:
| Param | Type | Default | Notes |
|---|---|---|---|
query | string | required | Search query |
numResults | number | 8 | Number of results |
includeDomains | string[] | none | Limit to specific domains |
excludeDomains | string[] | none | Exclude specific domains |
startPublishedDate | string | none | ISO date filter (start) |
endPublishedDate | string | none | ISO date filter (end) |
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) |
Research companies for business intelligence and news.
company_research_exa(companyName: "OpenAI", numResults: 5)
Parameters:
| Param | Type | Default | Notes |
|---|---|---|---|
companyName | string | required | Company name |
numResults | number | 5 | Number of results |
Find professional profiles and bios.
people_search_exa(query: "AI safety researchers at OpenAI", numResults: 5)
Extract full page content from a URL.
crawling_exa(url: "https://example.com/article", tokensNum: 5000)
Parameters:
| Param | Type | Default | Notes |
|---|---|---|---|
url | string | required | URL to extract |
tokensNum | number | 5000 | Content tokens |
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>")
web_search_exa(query: "Node.js 22 new features", numResults: 3)
get_code_context_exa(query: "Rust error handling patterns Result type", tokensNum: 3000)
company_research_exa(companyName: "Vercel", numResults: 5)
web_search_advanced_exa(query: "Vercel funding valuation 2026", numResults: 3)
# Start async research
deep_researcher_start(query: "WebAssembly component model status and adoption")
# ... do other work ...
deep_researcher_check(researchId: "<id>")
web_search_exa for broad queries, web_search_advanced_exa for filtered resultstokensNum (1000-2000) for focused code snippets, higher (5000+) for comprehensive contextcompany_research_exa with web_search_advanced_exa for thorough company analysiscrawling_exa to get full content from specific URLs found in search resultsdeep_researcher_start is best for comprehensive topics that benefit from AI synthesisdeep-research — Full research workflow using firecrawl + exa togethermarket-research — Business-oriented research with decision frameworksUse this skill to monitor and verify a deployed URL or public OSS launch surface after releases — checks HTTP endpoints, SSE streams, static assets, console errors, performance regressions, PR queue health, maintainer feedback, and listing-review blockers after deploys, merges, submissions, or dependency upgrades. Smoke / canary / post-deploy / PR-watch verification.
Build reputation-safe open-source marketing from verifiable project evidence, not hype, spam, or repeated public pings. Use for launch copy, directory targeting, community posts, proof packets, and maintainer-facing positioning.
Turn public launch, directory, community, or list rejections into repo fixes and better proof without arguing, spamming, or resubmitting blindly. Use after Hacker News, Product Hunt, GitHub list PR, marketplace, or community rejection.
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
Build an evidence-backed ecc install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ecc should be trimmed to what a project actually needs instead of loading the full bundle.
Build a source-derived writing style profile from real posts, essays, launch notes, docs, or site copy, then reuse that profile across content, outreach, and social workflows. Use when the user wants voice consistency without generic AI writing tropes.