# Exa Search

> Use this skill when users need semantic web search, similar-page discovery, result content retrieval, research-paper lookup, GitHub discovery, or structured Exa-powered research.

- Skill: `benedictking/exa-search` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add benedictking/exa-search`
- Raw SKILL.md: https://api.skillmd.com/api/skills/benedictking/exa-search/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- License: MIT
- Author: benedictking (https://skillmd.com/u/benedictking)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/benedictking/exa-search

---


# Exa Search Skill

## Trigger Conditions & Endpoint Selection

Choose Exa endpoint based on user intent:

- **search**: Need semantic search / find web pages / research topics. Use `type: "auto"` by default.
- **deep search / structured research**: Use the **search** endpoint with `type: "deep"` or `type: "deep-reasoning"` and optional `outputSchema`.
- **contents**: Given result IDs, need to extract full content.
- **findsimilar**: Given URL, need to find similar pages.
- **answer**: Need direct answer to a question.

`/research` and `/research/v1` are deprecated and were hard-removed on 2026-05-01. Do not use them for new calls; migrate research-style requests to `/search` with `type: "deep-reasoning"`.

## Recommended Architecture (Main Skill + Sub-skill)

This skill uses a two-phase architecture:

1. **Main skill (current context)**: Understand user question → Choose endpoint → Assemble JSON payload
2. **Sub-skill (fork context)**: Only responsible for HTTP call execution, avoiding conversation history token waste

## Execution Method

Use Task tool to invoke `exa-fetcher` sub-skill, passing command and JSON (stdin):

```
Task parameters:
- subagent_type: Bash
- description: "Call Exa API"
- prompt: cat <<'JSON' | node scripts/exa-api.cjs <search|contents|findsimilar|answer>
  { ...payload... }
  JSON
```

The script still accepts the legacy `research` command for backwards compatibility, but it normalizes the payload and sends it to `/search` with `type: "deep-reasoning"`.

## Payload Examples

### 1) Search

```bash
cat <<'JSON' | node scripts/exa-api.cjs search
{
  "query": "Latest research in LLMs",
  "type": "auto",
  "numResults": 10,
  "category": "research paper",
  "includeDomains": [],
  "excludeDomains": [],
  "startPublishedDate": "2025-01-01",
  "endPublishedDate": "2025-12-31",
  "contents": {
    "highlights": true,
    "summary": true
  }
}
JSON
```

**Search Types:**
- `auto`: Balanced default
- `fast`: Low latency
- `instant`: Lowest latency
- `deep-lite`: Lightweight synthesized output
- `deep`: Multi-step search with reasoning and structured outputs
- `deep-reasoning`: Highest-effort deep search for complex research tasks

Treat older `neural` references as legacy terminology; prefer `auto` for normal searches.

**Categories:**
- `company`, `people`, `research paper`, `news`, `personal site`, `financial report`, etc.

### 2) Contents

```bash
cat <<'JSON' | node scripts/exa-api.cjs contents
{
  "ids": ["result-id-1", "result-id-2"],
  "text": true,
  "highlights": true,
  "summary": true
}
JSON
```

### 3) Find Similar

```bash
cat <<'JSON' | node scripts/exa-api.cjs findsimilar
{
  "url": "https://example.com/article",
  "numResults": 10,
  "category": "news",
  "includeDomains": [],
  "excludeDomains": [],
  "startPublishedDate": "2025-01-01",
  "contents": {
    "text": true,
    "summary": true
  }
}
JSON
```

### 4) Answer

```bash
cat <<'JSON' | node scripts/exa-api.cjs answer
{
  "query": "What is the capital of France?",
  "numResults": 5,
  "includeDomains": [],
  "excludeDomains": []
}
JSON
```

### 5) Structured Research via Search

Use `/search` with `type: "deep-reasoning"` and `outputSchema` for research-style synthesized output.

```bash
cat <<'JSON' | node scripts/exa-api.cjs search
{
  "query": "What are the latest developments in AI?",
  "type": "deep-reasoning",
  "stream": false,
  "systemPrompt": "Prefer official sources and provide specific, grounded findings.",
  "outputSchema": {
    "type": "object",
    "properties": {
      "topic": {
        "type": "string",
        "description": "The main topic"
      },
      "key_findings": {
        "type": "array",
        "description": "List of key findings",
        "items": {
          "type": "string"
        }
      }
    },
    "required": ["topic"]
  }
}
JSON
```

`/search` returns synthesized content in `output.content` and field-level citations/confidence in `output.grounding` when `outputSchema` is used. Do not add citation or confidence fields to the schema.

## Environment Variables & API Key

Two ways to configure API Key (priority: environment variable > `.env`):

1. Environment variable: `EXA_API_KEY`
2. `.env` file: Place in `.env`, can copy from `.env.example`

## Response Format

All endpoints return JSON with:
- `requestId`: Unique request identifier
- `results`: Array of search results
- `searchType`: Type of search performed (for search endpoint)
- `context`: LLM-friendly context string (if requested)
- `costDollars`: Detailed cost breakdown