Execute Exa neural search with contents, date filters, and domain scoping.
Use when building search features, implementing RAG context retrieval,
or querying the web with semantic understanding.
Trigger with phrases like "exa search", "exa neural search",
"search with exa", "exa searchAndContents", "exa query".
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Execute Exa neural search with contents, date filters, and domain scoping.
Use when building search features, implementing RAG context retrieval,
or querying the web with semantic understanding.
Trigger with phrases like "exa search", "exa neural search",
"search with exa", "exa searchAndContents", "exa query".
Designed for Claude Code, also compatible with Codex and OpenClaw
Exa Core Workflow A — Neural Search
Overview
Primary workflow for Exa: semantic web search using search() and searchAndContents(). Exa's neural search understands query meaning rather than matching keywords, making it ideal for research, RAG pipelines, and content discovery. This skill covers search types, content extraction, filtering, and categories.
Prerequisites
exa-js installed and EXA_API_KEY configured
Understanding of neural vs keyword search tradeoffs
Search Types
Type
Latency
Best For
auto (default)
300-1500ms
General queries; Exa picks best approach
neural
500-2000ms
Conceptual/semantic queries
keyword
200-500ms
Exact terms, names, URLs
fast
p50 < 425ms
Speed-critical applications
instant
< 150ms
Real-time autocomplete
deep
2-5s
Maximum quality, light deep search
deep-reasoning
5-15s
Complex research questions
Instructions
Step 1: Basic Neural Search
importExafrom"exa-js";
const exa = newExa(process.env.EXA_API_KEY);
// Neural search: phrase your query as a statement, not a questionconst results = await exa.search(
"comprehensive guide to building production RAG systems",
{
type: "neural",
: ,
}
);
( r results.) {
.();
.();
}
numResults
10
// max 100 for neural/deep
for
const
of
results
console
log
`[${r.score.toFixed(2)}] ${r.title} — ${r.url}`
console
log
` Published: ${r.publishedDate || "unknown"}`
Step 2: Search with Content Extraction
// searchAndContents returns page text, highlights, and/or summariesconst results = await exa.searchAndContents(
"best practices for vector database selection",
{
type: "auto",
numResults: 5,
// Text: full page content as markdowntext: { maxCharacters: 2000 },
// Highlights: key excerpts relevant to a custom queryhighlights: {
maxCharacters: 500,
query: "comparison of vector databases",
},
// Summary: LLM-generated summary tailored to a querysummary: { query: "which vector database should I choose?" },
}
);
for (const r of results.results) {
console.log(`## ${r.title}`);
console.log(`Summary: ${r.summary}`);
console.log(`Highlights: ${r.highlights?.join(" ... ")}`);
console.log(`Full text: ${r.text?.substring(0, 300)}...`);
}
Step 3: Date and Domain Filtering
// Filter by publication date and restrict to specific domainsconst results = await exa.searchAndContents(
"TypeScript 5.5 new features",
{
type: "auto",
numResults: 10,
// Date filters use ISO 8601 formatstartPublishedDate: "2024-06-01T00:00:00.000Z",
endPublishedDate: "2025-01-01T00:00:00.000Z",
// Domain filters (up to 1200 domains each)includeDomains: ["devblogs.microsoft.com", "typescriptlang.org"],
// Text content filters (1 string, max 5 words each)includeText: ["TypeScript"],
text: true,
}
);
Step 4: Category-Scoped Search
// Categories narrow results to specific content types// Available: company, research paper, news, tweet, personal site,// financial report, peopleconst papers = await exa.searchAndContents(
"attention mechanism improvements for long context LLMs",
{
type: "neural",
numResults: 10,
category: "research paper",
text: { maxCharacters: 3000 },
highlights: true,
}
);
const companies = await exa.search(
"AI infrastructure startup founded 2024",
{
type: "auto",
numResults: 10,
category: "company",
// Note: company and people categories do NOT support date filters
}
);
Step 5: Content Freshness with LiveCrawl
// Control whether Exa fetches fresh content or uses cacheconst results = await exa.searchAndContents(
"latest AI model releases this week",
{
numResults: 5,
text: { maxCharacters: 1500 },
// maxAgeHours controls freshness (replaces deprecated livecrawl)// 0 = always crawl fresh, -1 = never crawl, positive = max cache agelivecrawl: "preferred", // try fresh, fall back to cachelivecrawlTimeout: 10000, // 10s timeout for live crawling
}
);
Output
Ranked search results with URLs, titles, scores, and published dates
Optional text content, highlights, and summaries per result
Results filtered by date range, domains, categories, and text content
