exa-search

benedictking/exa-search · updated Jun 9, 2026

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$npx skills add https://github.com/benedictking/exa-search --skill exa-search
0 commentsdiscussion
summary

Semantic search across web, research papers, and similar content discovery via Exa API.

  • Five endpoint modes: semantic/keyword search, content extraction, similar page discovery, direct answers, and structured research with custom output schemas
  • Supports search type selection (neural embeddings, fast keyword, deep comprehensive, or auto), category filtering (research papers, GitHub, news, PDFs, tweets, etc.), and date range constraints
  • Two-phase architecture with main skill for intent
skill.md

Exa Search Skill

Trigger Conditions & Endpoint Selection

Choose Exa endpoint based on user intent:

  • search: Need semantic search / find web pages / research topics
  • 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: Need structured research output following given output_schema

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 .claude/skills/exa-search/exa-api.cjs <search|contents|findsimilar|answer|research>
  { ...payload... }
  JSON

Payload Examples

1) Search

cat <<'JSON' | node .claude/skills/exa-search/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",
  "includeText": [],
  "excludeText": [],
  "context": true,
  "contents": {
    "text": true,
    "highlights": true,
    "summary": true
  }
}
JSON

Search Types:

  • neural: Semantic search using embeddings
  • fast: Quick keyword-based search
  • auto: Automatically choose best method (default)
  • deep: Comprehensive deep search

Categories:

  • company, people, research paper, news, pdf, github, tweet, etc.

2) Contents

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

3) Find Similar

cat <<'JSON' | node .claude/skills/exa-search/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

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

5) Research

cat <<'JSON' | node .claude/skills/exa-search/exa-api.cjs research
{
  "input": "What are the latest developments in AI?",
  "model": "auto",
  "stream": false,
  "output_schema": {
    "properties": {
      "topic": {
        "type": "string",
        "description": "The main topic"
      },
      "key_findings": {
        "type": "array",
        "description": "List of key findings",
        "items": {
          "type": "string"
        }
      }
    },
    "required": ["topic"]
  },
  "citation_format": "numbered"
}
JSON

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 .claude/skills/exa-search/.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
how to use exa-search

How to use exa-search on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add exa-search
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/benedictking/exa-search --skill exa-search

The skills CLI fetches exa-search from GitHub repository benedictking/exa-search and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/exa-search

Reload or restart Cursor to activate exa-search. Access the skill through slash commands (e.g., /exa-search) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.625 reviews
  • Chaitanya Patil· Dec 20, 2024

    Useful defaults in exa-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Nikhil Rao· Dec 12, 2024

    We added exa-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Piyush G· Nov 11, 2024

    exa-search has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • James Taylor· Nov 3, 2024

    Keeps context tight: exa-search is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Jin Sanchez· Oct 22, 2024

    exa-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Shikha Mishra· Oct 2, 2024

    Solid pick for teams standardizing on skills: exa-search is focused, and the summary matches what you get after install.

  • Aanya Desai· Sep 17, 2024

    We added exa-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Li Singh· Aug 8, 2024

    exa-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Li Bhatia· Jul 27, 2024

    exa-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Rahul Santra· Jul 3, 2024

    Registry listing for exa-search matched our evaluation — installs cleanly and behaves as described in the markdown.

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