# Exa Search

> Semantic search, similar content discovery, and structured research using Exa API. Use when you need semantic/embeddings-based search, finding similar content, or searching by category (company, people, research papers, etc.).

- Skill: `techwavedev/exa-search` (Agent Skill)
- Install (CLI): `npx skillmds@latest add techwavedev/exa-search`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/exa-search/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/exa-search

---


# exa-search

## Overview
Semantic search, similar content discovery, and structured research using Exa API

## When to Use
- When you need semantic/embeddings-based search
- When finding similar content
- When searching by category (company, people, research papers, etc.)

## Installation
```bash
npx skills add -g BenedictKing/exa-search
```

## Step-by-Step Guide
1. Install the skill using the command above
2. Configure Exa API key
3. Use naturally in Claude Code conversations

## Examples
See [GitHub Repository](https://github.com/BenedictKing/exa-search) for examples.

## Best Practices
- Configure API keys via environment variables

## Troubleshooting
See the GitHub repository for troubleshooting guides.

## Related Skills
- context7-auto-research, tavily-web, firecrawl-scraper, codex-review

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting.

```bash
# Check for prior backend/API context before starting
python3 execution/memory_manager.py auto --query "API design patterns and architecture decisions for Exa Search"
```

### Storing Results

After completing work, store backend/API decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/min per tenant" \
  --type decision --project <project> \
  --tags exa-search backend
```

### Multi-Agent Collaboration

Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \
  --project <project>
```

### Agent Team: Code Review

After implementation, dispatch `code_review_team` for two-stage review (spec compliance + code quality) before merging.

<!-- AGI-INTEGRATION-END -->