Claude-skill-registry exa

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/exa" ~/.claude/skills/majiayu000-claude-skill-registry-exa && rm -rf "$T"

manifest: skills/data/exa/SKILL.md

Exa Search

High-precision semantic search via Exa API. Standalone CLI only (no MCP dependency).

Execution Method

Run

scripts/exa_cli.py

via Bash:

# Prerequisites: pip install httpx tenacity
# Environment: EXA_API_KEY (required), EXA_API_URL (optional, default: https://api.exa.ai)

Available Tools

Search Tools

# Basic semantic search
python scripts/exa_cli.py web_search_exa --query "emerging patterns in TypeScript" [--num-results 10] [--type auto|keyword|neural] [--livecrawl always|fallback|never]

# Advanced search with filters
python scripts/exa_cli.py web_search_advanced_exa --query "machine learning papers" \
  [--include-domains arxiv.org,github.com] [--exclude-domains medium.com] \
  [--start-date 2024-01-01] [--end-date 2024-12-31] \
  [--text] [--highlights] [--summary] [--out results.json]

# Deep search with query expansion
python scripts/exa_cli.py deep_search_exa --objective "foundations of quantum error correction" [--additional-queries "query1|query2"]

# Company research
python scripts/exa_cli.py company_research_exa --company "Anthropic" [--num-results 10]

# LinkedIn profile search
python scripts/exa_cli.py linkedin_search_exa --query "AI researchers at Stanford" [--num-results 10]

Content Tools

# Extract content from URL
python scripts/exa_cli.py crawling_exa --url "https://example.com/article" \
  [--max-chars 5000] [--livecrawl always|fallback|never] \
  [--text] [--highlights] [--summary] [--out content.json]

# Get code context (documentation, examples)
python scripts/exa_cli.py get_code_context_exa --query "React useState hook examples" [--tokens-num 10000] [--out code.json]

Research Tools

# Start AI research task
python scripts/exa_cli.py deep_researcher_start --instructions "Analyze the impact of LLMs on software development" [--model exa-research|exa-research-pro]
# Returns: {"taskId": "abc123", ...}

# Check research status
python scripts/exa_cli.py deep_researcher_check --task-id "abc123" [--out report.json]
# Status: running → completed | failed

Configuration

# Check config and test connection
python scripts/exa_cli.py get_config_info [--no-test]

Tool Capability Matrix

Tool	Required	Optional	Output
`web_search_exa`	`query`	`num-results` , `type` , `livecrawl`	Search results JSON
`web_search_advanced_exa`	`query`	`include-domains` , `exclude-domains` , `start-date` , `end-date` , `text` , `highlights` , `summary`	Filtered results JSON
`deep_search_exa`	`objective`	`additional-queries`	Expanded search results
`company_research_exa`	`company`	`num-results`	Company info JSON
`linkedin_search_exa`	`query`	`num-results`	LinkedIn profiles JSON
`crawling_exa`	`url`	`max-chars` , `livecrawl` , `text` , `highlights` , `summary`	Page content JSON
`get_code_context_exa`	`query`	`tokens-num` (1000-50000)	Code context JSON
`deep_researcher_start`	`instructions`	`model`	Task ID
`deep_researcher_check`	`task-id`	-	Status + report

Tool Routing Guide

Exa vs Grok-Search

Use Case	Recommended Tool
Real-time news, current events	grok-search
Semantic/conceptual research	exa
Code documentation lookup	exa ( `get_code_context_exa` )
Company/professional research	exa
General web content fetch	grok-search
Academic papers, technical docs	exa
AI-powered deep research	exa ( `deep_researcher_*` )

Workflow Patterns

Pattern 1: Quick Semantic Search

python scripts/exa_cli.py web_search_exa --query "best practices for React hooks" --num-results 5

Pattern 2: Filtered Research

python scripts/exa_cli.py web_search_advanced_exa --query "transformer architecture" \
  --include-domains arxiv.org,papers.nips.cc --start-date 2023-01-01 --text --summary

Pattern 3: Deep Research Task

# Start research
python scripts/exa_cli.py deep_researcher_start --instructions "Compare RAG vs fine-tuning for domain adaptation"
# Poll until completed
python scripts/exa_cli.py deep_researcher_check --task-id "<taskId>" --out research_report.json

Error Handling

Error	Recovery
`EXA_API_KEY not configured`	Set environment variable or use `--api-key`
HTTP 429 (Rate limit)	Automatic retry with exponential backoff
HTTP 401 (Unauthorized)	Verify API key is valid
Timeout	Retry or reduce `num-results`

Output Format

All commands output JSON to stdout. Use

--out <file>

to write to file instead.

{
  "results": [
    {"title": "...", "url": "...", "text": "...", "publishedDate": "..."}
  ]
}