Execute Exa primary workflow: Core Workflow A. Use when implementing primary use case, building main features, or core integration tasks. Trigger with phrases like "exa main workflow", "primary task with exa".
npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill exa-core-workflow-aLoading…
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.
exa-js installed and EXA_API_KEY configured| 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 |
import Exa from "exa-js";
const exa = new Exa(process.env.EXA_API_KEY);
// Neural search: phrase your query as a statement, not a question
const results = await exa.search(
"comprehensive guide to building production RAG systems",
{
type: "neural",
numResults: 10, // max 100 for neural/deep
}
);
for (const r of results.results) {
console.log(`[${r.score.toFixed(2)}] ${r.title} — ${r.url}`);
console.log(` Published: ${r.publishedDate || "unknown"}`);
}
// searchAndContents returns page text, highlights, and/or summaries
const results = await exa.searchAndContents(
"best practices for vector database selection",
{
type: "auto",
numResults: 5,
// Text: full page content as markdown
text: { maxCharacters: 2000 },
// Highlights: key excerpts relevant to a custom query
highlights: {
maxCharacters: 500,
query: "comparison of vector databases",
},
// Summary: LLM-generated summary tailored to a query
summary: { 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)}...`);
}
// Filter by publication date and restrict to specific domains
const results = await exa.searchAndContents(
"TypeScript 5.5 new features",
{
type: "auto",
numResults: 10,
// Date filters use ISO 8601 format
startPublishedDate: "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,
}
);
// Categories narrow results to specific content types
// Available: company, research paper, news, tweet, personal site,
// financial report, people
const 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
}
);
// Control whether Exa fetches fresh content or uses cache
const 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 age
livecrawl: "preferred", // try fresh, fall back to cache
livecrawlTimeout: 10000, // 10s timeout for live crawling
}
);
| Error | HTTP Code | Cause | Solution |
|---|---|---|---|
INVALID_REQUEST_BODY | 400 | Invalid parameter types | Check query is string, numResults is integer |
INVALID_NUM_RESULTS | 400 | numResults > 100 with highlights | Reduce numResults or remove highlights |
| Empty results array | 200 | Date filter too narrow | Widen date range or remove filter |
| Low relevance scores | 200 | Keyword-style query | Rephrase as natural language statement |
FETCH_DOCUMENT_ERROR | 422 | URL content unretrievable | Use livecrawl: "fallback" or try without text |
async function getRAGContext(question: string, maxResults = 5) {
const results = await exa.searchAndContents(question, {
type: "neural",
numResults: maxResults,
text: { maxCharacters: 2000 },
highlights: { maxCharacters: 500, query: question },
});
return results.results.map((r, i) => ({
source: `[${i + 1}] ${r.title} (${r.url})`,
content: r.text,
highlights: r.highlights,
}));
}
For similarity search and advanced retrieval, see exa-core-workflow-b.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).